04625nas a2201141 4500008004100000022001400041245011000055210006900165260000900234300001200243490000700255520126600262653002401528653001301552653002301565653001101588653001501599653002001614100001901634700002301653700002201676700002101698700002001719700002301739700002101762700002601783700001901809700002001828700001901848700002201867700002301889700002401912700001701936700002101953700001701974700001601991700002402007700002502031700002502056700002302081700002302104700002702127700002402154700001602178700002102194700002602215700002502241700002102266700002102287700001902308700003202327700002102359700002202380700002002402700001902422700002602441700001802467700001802485700002302503700002102526700001902547700001902566700002202585700001802607700001902625700001802644700002302662700001802685700002002703700001902723700003302742700002602775700002702801700002502828700002302853700001802876700002302894700001702917700002402934700001802958700002202976700002502998700002003023700002303043700002003066700002603086700002003112700001903132700002203151700002203173700002003195700002303215700002103238700001803259700002403277710003503301856014703336 2024 eng d a1664-322400aDrug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.0 aDrugtarget identification in COVID19 disease mechanisms using co c2023 a12828590 v143 a
INTRODUCTION: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.
METHODS: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.
RESULTS: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.
DISCUSSION: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.
10aComputer Simulation10aCOVID-1910adrug repositioning10aHumans10aSARS-CoV-210aSystems biology1 aNiarakis, Anna1 aOstaszewski, Marek1 aMazein, Alexander1 aKuperstein, Inna1 aKutmon, Martina1 aGillespie, Marc, E1 aFunahashi, Akira1 aAcencio, Marcio, Luis1 aHemedan, Ahmed1 aAichem, Michael1 aKlein, Karsten1 aCzauderna, Tobias1 aBurtscher, Felicia1 aYamada, Takahiro, G1 aHiki, Yusuke1 aHiroi, Noriko, F1 aHu, Finterly1 aPham, Nhung1 aEhrhart, Friederike1 aWillighagen, Egon, L1 aValdeolivas, Alberto1 aDugourd, Aurélien1 aMessina, Francesco1 aEsteban-Medina, Marina1 aPeña-Chilet, Maria1 aRian, Kinza1 aSoliman, Sylvain1 aAghamiri, Sara, Sadat1 aPuniya, Bhanwar, Lal1 aNaldi, Aurélien1 aHelikar, Tomáš1 aSingh, Vidisha1 aFernández, Marco, Fariñas1 aBermudez, Viviam1 aTsirvouli, Eirini1 aMontagud, Arnau1 aNoël, Vincent1 aPonce-de-Leon, Miguel1 aMaier, Dieter1 aBauch, Angela1 aGyori, Benjamin, M1 aBachman, John, A1 aLuna, Augustin1 aPiñero, Janet1 aFurlong, Laura, I1 aBalaur, Irina1 aRougny, Adrien1 aJarosz, Yohan1 aOverall, Rupert, W1 aPhair, Robert1 aPerfetto, Livia1 aMatthews, Lisa1 aRex, Devasahayam, Arokia Bal1 aOrlic-Milacic, Marija1 aGomez, Luis, Cristobal1 aDe Meulder, Bertrand1 aRavel, Jean, Marie1 aJassal, Bijay1 aSatagopam, Venkata1 aWu, Guanming1 aGolebiewski, Martin1 aGawron, Piotr1 aCalzone, Laurence1 aBeckmann, Jacques, S1 aEvelo, Chris, T1 aD'Eustachio, Peter1 aSchreiber, Falk1 aSaez-Rodriguez, Julio1 aDopazo, Joaquin1 aKuiper, Martin1 aValencia, Alfonso1 aWolkenhauer, Olaf1 aKitano, Hiroaki1 aBarillot, Emmanuel1 aAuffray, Charles1 aBalling, Rudi1 aSchneider, Reinhard1 aCOVID-19 Disease Map Community uhttps://www.clinbioinfosspa.es/content/drug-target-identification-covid-19-disease-mechanisms-using-computational-systems-biology-approaches-002250nas a2200253 4500008004100000022001400041245013700055210006900192260001600261490000700277520134400284100002701628700002001655700002401675700002601699700001801725700001801743700002101761700001901782700002801801700002001829700002601849856012101875 2023 eng d a1999-492300aA Comprehensive Analysis of 21 Actionable Pharmacogenes in the Spanish Population: From Genetic Characterisation to Clinical Impact.0 aComprehensive Analysis of 21 Actionable Pharmacogenes in the Spa c2023 Apr 190 v153 aThe implementation of pharmacogenetics (PGx) is a main milestones of precision medicine nowadays in order to achieve safer and more effective therapies. Nevertheless, the implementation of PGx diagnostics is extremely slow and unequal worldwide, in part due to a lack of ethnic PGx information. We analysed genetic data from 3006 Spanish individuals obtained by different high-throughput (HT) techniques. Allele frequencies were determined in our population for the main 21 actionable PGx genes associated with therapeutical changes. We found that 98% of the Spanish population harbours at least one allele associated with a therapeutical change and, thus, there would be a need for a therapeutical change in a mean of 3.31 of the 64 associated drugs. We also identified 326 putative deleterious variants that were not previously related with PGx in 18 out of the 21 main PGx genes evaluated and a total of 7122 putative deleterious variants for the 1045 PGx genes described. Additionally, we performed a comparison of the main HT diagnostic techniques, revealing that after whole genome sequencing, genotyping with the PGx HT array is the most suitable solution for PGx diagnostics. Finally, all this information was integrated in the Collaborative Spanish Variant Server to be available to and updated by the scientific community.
1 aNúñez-Torres, Rocío1 aPita, Guillermo1 aPeña-Chilet, Maria1 aLópez-López, Daniel1 aZamora, Jorge1 aRoldán, Gema1 aHerráez, Belén1 aAlvarez, Nuria1 aAlonso, María, Rosario1 aDopazo, Joaquin1 aGonzález-Neira, Anna uhttps://www.clinbioinfosspa.es/content/comprehensive-analysis-21-actionable-pharmacogenes-spanish-population-genetic02401nas a2200301 4500008004100000022001400041245012200055210006900177260001600246300001100262520135200273100002301625700002701648700002601675700001801701700002701719700001801746700002101764700002301785700002401808700003201832700002401864700002201888700002001910700001601930700002301946856013001969 2023 eng d a1474-972600amicroRNAs-mediated regulation of insulin signaling in white adipose tissue during aging: Role of caloric restriction.0 amicroRNAsmediated regulation of insulin signaling in white adipo c2023 Jul 04 ae139193 aCaloric restriction is a non-pharmacological intervention known to ameliorate the metabolic defects associated with aging, including insulin resistance. The levels of miRNA expression may represent a predictive tool for aging-related alterations. In order to investigate the role of miRNAs underlying insulin resistance in adipose tissue during the early stages of aging, 3- and 12-month-old male animals fed ad libitum, and 12-month-old male animals fed with a 20% caloric restricted diet were used. In this work we demonstrate that specific miRNAs may contribute to the impaired insulin-stimulated glucose metabolism specifically in the subcutaneous white adipose tissue, through the regulation of target genes implicated in the insulin signaling cascade. Moreover, the expression of these miRNAs is modified by caloric restriction in middle-aged animals, in accordance with the improvement of the metabolic state. Overall, our work demonstrates that alterations in posttranscriptional gene expression because of miRNAs dysregulation might represent an endogenous mechanism by which insulin response in the subcutaneous fat depot is already affected at middle age. Importantly, caloric restriction could prevent this modulation, demonstrating that certain miRNAs could constitute potential biomarkers of age-related metabolic alterations.
1 aCorrales, Patricia1 aMartin-Taboada, Marina1 aVivas-García, Yurena1 aTorres, Lucia1 aRamirez-Jimenez, Laura1 aLopez, Yamila1 aHorrillo, Daniel1 aVila-Bedmar, Rocio1 aBarber-Cano, Eloisa1 aIzquierdo-Lahuerta, Adriana1 aPeña-Chilet, Maria1 aMartínez, Carmen1 aDopazo, Joaquin1 aRos, Manuel1 aMedina-Gomez, Gema uhttps://www.clinbioinfosspa.es/content/micrornas-mediated-regulation-insulin-signaling-white-adipose-tissue-during-aging-role02396nas a2200193 4500008004100000022001400041245017300055210006900228260001600297300001100313520159800324100002301922700003501945700001701980700002101997700002002018700003002038856013402068 2023 eng d a1873-642400aPolystyrene nanoplastics affect transcriptomic and epigenomic signatures of human fibroblasts and derived induced pluripotent stem cells: Implications for human health.0 aPolystyrene nanoplastics affect transcriptomic and epigenomic si c2022 Dec 09 a1208493 aPlastic pollution is increasing at an alarming rate yet the impact of this pollution on human health is poorly understood. Because human induced pluripotent stem cells (hiPSC) are frequently derived from dermal fibroblasts, these cells offer a powerful platform for the identification of molecular biomarkers of environmental pollution in human cells. Here, we describe a novel proof-of-concept for deriving hiPSC from human dermal fibroblasts deliberately exposed to polystyrene (PS) nanoplastic particles; unexposed hiPSC served as controls. In parallel, unexposed hiPSC were exposed to low and high concentrations of PS nanoparticles. Transcriptomic and epigenomic signatures of all fibroblasts and hiPSCs were defined using RNA-seq and whole genome methyl-seq, respectively. Both PS-treated fibroblasts and derived hiPSC showed alterations in expression of ESRRB and HNF1A genes and circuits involved in the pluripotency of stem cells, as well as in pathways involved in cancer, inflammatory disorders, gluconeogenesis, carbohydrate metabolism, innate immunity, and dopaminergic synapse. Similarly, the expression levels of identified key transcriptional and DNA methylation changes (DNMT3A, ESSRB, FAM133CP, HNF1A, SEPTIN7P8, and TTC34) were significantly affected in both PS-exposed fibroblasts and hiPSC. This study illustrates the power of human cellular models of environmental pollution to narrow down and prioritize the list of candidate molecular biomarkers of environmental pollution. This knowledge will facilitate the deciphering of the origins of environmental diseases.
1 aStojkovic, Miodrag1 aGuzmán, Francisco, Manuel Ort1 aHan, Dongjun1 aStojkovic, Petra1 aDopazo, Joaquin1 aStankovic, Konstantina, M uhttps://www.clinbioinfosspa.es/content/polystyrene-nanoplastics-affect-transcriptomic-and-epigenomic-signatures-human-fibroblasts03075nas a2200325 4500008004100000022001400041245009700055210006900152260000900221300001200230490000600242520201700248100001802265700001802283700001902301700002402320700002702344700003202371700002202403700001502425700002202440700002202462700002202484700002602506700002402532700002002556700002202576700002302598856012802621 2023 eng d a2673-764700aVisualization of automatically combined disease maps and pathway diagrams for rare diseases.0 aVisualization of automatically combined disease maps and pathway c2023 a11015050 v33 aInvestigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.
1 aGawron, Piotr1 aHoksza, David1 aPiñero, Janet1 aPeña-Chilet, Maria1 aEsteban-Medina, Marina1 aFernandez-Rueda, Jose, Luis1 aColonna, Vincenza1 aSmula, Ewa1 aHeirendt, Laurent1 aAncien, François1 aGrouès, Valentin1 aSatagopam, Venkata, P1 aSchneider, Reinhard1 aDopazo, Joaquin1 aFurlong, Laura, I1 aOstaszewski, Marek uhttps://www.clinbioinfosspa.es/content/visualization-automatically-combined-disease-maps-and-pathway-diagrams-rare-diseases02679nas a2200337 4500008004100000022001400041245011500055210006900170260001600239520155800255100001601813700001901829700002001848700001901868700003101887700002201918700002501940700001701965700001701982700001901999700002102018700002702039700002002066700002202086700002202108700001902130700002002149700002102169710002002190856013102210 2022 eng d a1399-000400aCIBERER: Spanish National Network for Research on Rare Diseases: a highly productive collaborative initiative.0 aCIBERER Spanish National Network for Research on Rare Diseases a c2022 Jan 203 aCIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on Rare Diseases currently consists of 75 research groups belonging to universities, research centers and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical and cellular research of rare diseases. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this paper, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions towards the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to rare disease research. This article is protected by copyright. All rights reserved.
1 aLuque, Juan1 aMendes, Ingrid1 aGómez, Beatriz1 aMorte, Beatriz1 ade Heredia, Miguel, López1 aHerreras, Enrique1 aCorrochano, Virginia1 aBueren, Juan1 aGallano, Pia1 aArtuch, Rafael1 aFillat, Cristina1 aPérez-Jurado, Luis, A1 aMontoliu, Lluis1 aCarracedo, Ángel1 aMillán, José, M1 aWebb, Susan, M1 aPalau, Francesc1 aLapunzina, Pablo1 aCIBERER Network uhttps://www.clinbioinfosspa.es/content/ciberer-spanish-national-network-research-rare-diseases-highly-productive-collaborative08213nas a2202125 4500008004100000022001400041245005800055210005600113260001600169520175300185100001701938700002901955700002801984700002002012700002702032700002002059700003002079700003402109700002902143700002702172700003102199700002002230700002502250700002002275700002802295700002302323700002102346700001902367700002902386700002302415700001802438700002202456700002402478700002902502700002902531700002902560700002102589700002502610700002302635700001802658700002002676700002002696700002602716700002402742700001802766700002202784700002402806700003102830700002802861700001802889700002102907700003202928700002502960700003102985700003003016700002403046700001903070700003303089700002903122700002903151700003403180700002803214700002503242700002503267700003303292700003203325700002903357700003303386700002703419700003003446700002903476700002803505700002503533700002903558700002003587700002803607700002103635700002603656700002603682700003303708700002703741700002303768700003103791700001903822700002703841700002403868700002603892700002203918700002003940700002203960700002103982700002804003700002004031700002504051700002004076700002904096700002604125700003004151700001804181700002604199700002104225700002704246700002804273700003304301700003104334700002804365700002704393700001604420700002504436700002404461700002004485700002704505700003704532700002104569700002504590700002004615700002504635700002104660700001704681700002204698700002004720700002304740700003004763700002404793700001804817700002204835700001904857700002204876700002804898700001904926700001904945700001704964700002004981700002605001700001905027700002005046700002205066700001905088700003105107700002205138700002205160700002105182700001805203700002105221700002605242700002605268700003005294700002105324700002305345700002405368700002005392700002305412700001605435700002005451700003205471700002005503700001805523700002005541700001805561700001705579700002005596700001805616700001805634700001805652700001905670700002205689700002305711700003005734700001805764700002605782700002205808700002805830700001905858700002105877700002205898710002505920710002505945710002405970856009305994 2022 eng d a1460-208300aNovel genes and sex differences in COVID-19 severity.0 aNovel genes and sex differences in COVID19 severity c2022 Jun 163 aHere we describe the results of a genome-wide study conducted in 11 939 COVID-19 positive cases with an extensive clinical information that were recruited from 34 hospitals across Spain (SCOURGE consortium). In sex-disaggregated genome-wide association studies for COVID-19 hospitalization, genome-wide significance (p < 5x10-8) was crossed for variants in 3p21.31 and 21q22.11 loci only among males (p = 1.3x10-22 and p = 8.1x10-12, respectively), and for variants in 9q21.32 near TLE1 only among females (p = 4.4x10-8). In a second phase, results were combined with an independent Spanish cohort (1598 COVID-19 cases and 1068 population controls), revealing in the overall analysis two novel risk loci in 9p13.3 and 19q13.12, with fine-mapping prioritized variants functionally associated with AQP3 (p = 2.7x10-8) and ARHGAP33 (p = 1.3x10-8), respectively. The meta-analysis of both phases with four European studies stratified by sex from the Host Genetics Initiative confirmed the association of the 3p21.31 and 21q22.11 loci predominantly in males and replicated a recently reported variant in 11p13 (ELF5, p = 4.1x10-8). Six of the COVID-19 HGI discovered loci were replicated and an HGI-based genetic risk score predicted the severity strata in SCOURGE. We also found more SNP-heritability and larger heritability differences by age (<60 or ≥ 60 years) among males than among females. Parallel genome-wide screening of inbreeding depression in SCOURGE also showed an effect of homozygosity in COVID-19 hospitalization and severity and this effect was stronger among older males. In summary, new candidate genes for COVID-19 severity and evidence supporting genetic disparities among sexes are provided.
1 aCruz, Raquel1 ade Almeida, Silvia, Diz-1 aHeredia, Miguel, López1 aQuintela, Inés1 aCeballos, Francisco, C1 aPita, Guillermo1 aLorenzo-Salazar, José, M1 aGonzález-Montelongo, Rafaela1 aGago-Domínguez, Manuela1 aPorras, Marta, Sevilla1 aCastaño, Jair, Antonio Te1 aNevado, Julián1 aAguado, Jose, María1 aAguilar, Carlos1 aAguilera-Albesa, Sergio1 aAlmadana, Virginia1 aAlmoguera, Berta1 aAlvarez, Nuria1 aAndreu-Bernabeu, Álvaro1 aArana-Arri, Eunate1 aArango, Celso1 aArranz, María, J1 aArtiga, Maria-Jesus1 aBaptista-Rosas, Raúl, C1 aBarreda-Sánchez, María1 aBelhassen-Garcia, Moncef1 aBezerra, Joao, F1 aBezerra, Marcos, A C1 aBoix-Palop, Lucía1 aBrión, Maria1 aBrugada, Ramón1 aBustos, Matilde1 aCalderón, Enrique, J1 aCarbonell, Cristina1 aCastano, Luis1 aCastelao, Jose, E1 aConde-Vicente, Rosa1 aCordero-Lorenzana, Lourdes1 aCortes-Sanchez, Jose, L1 aCorton, Marta1 aDarnaude, Teresa1 aDe Martino-Rodríguez, Alba1 aCampo-Pérez, Victor1 aBustamante, Aranzazu, Diaz1 aDomínguez-Garrido, Elena1 aLuchessi, André, D1 aEirós, Rocío1 aSanabria, Gladys, Mercedes E1 aFariñas, María, Carmen1 aFernández-Robelo, Uxía1 aFernández-Rodríguez, Amanda1 aFernández-Villa, Tania1 aGil-Fournier, Belén1 aGómez-Arrue, Javier1 aÁlvarez, Beatriz, González1 aQuirós, Fernan, Gonzalez B1 aGonzález-Peñas, Javier1 aGutiérrez-Bautista, Juan, F1 aHerrero, María, José1 aHerrero-Gonzalez, Antonio1 aJimenez-Sousa, María, A1 aLattig, María, Claudia1 aBorja, Anabel, Liger1 aLopez-Rodriguez, Rosario1 aMancebo, Esther1 aMartín-López, Caridad1 aMartín, Vicente1 aMartinez-Nieto, Oscar1 aMartinez-Lopez, Iciar1 aMartinez-Resendez, Michel, F1 aMartinez-Perez, Ángel1 aMazzeu, Juliana, A1 aMacías, Eleuterio, Merayo1 aMinguez, Pablo1 aCuerda, Victor, Moreno1 aSilbiger, Vivian, N1 aOliveira, Silviene, F1 aOrtega-Paino, Eva1 aParellada, Mara1 aPaz-Artal, Estela1 aSantos, Ney, P C1 aPérez-Matute, Patricia1 aPerez, Patricia1 aPérez-Tomás, Elena1 aPerucho, Teresa1 aPinsach-Abuin, Mel, Lina1 aPompa-Mera, Ericka, N1 aPorras-Hurtado, Gloria, L1 aPujol, Aurora1 aLeón, Soraya, Ramiro1 aResino, Salvador1 aFernandes, Marianne, R1 aRodríguez-Ruiz, Emilio1 aRodriguez-Artalejo, Fernando1 aRodriguez-Garcia, José, A1 aRuiz-Cabello, Francisco1 aRuiz-Hornillos, Javier1 aRyan, Pablo1 aSoria, José, Manuel1 aSouto, Juan, Carlos1 aTamayo, Eduardo1 aTamayo-Velasco, Alvaro1 aTaracido-Fernandez, Juan, Carlos1 aTeper, Alejandro1 aTorres-Tobar, Lilian1 aUrioste, Miguel1 aValencia-Ramos, Juan1 aYáñez, Zuleima1 aZarate, Ruth1 aNakanishi, Tomoko1 aPigazzini, Sara1 aDegenhardt, Frauke1 aButler-Laporte, Guillaume1 aMaya-Miles, Douglas1 aBujanda, Luis1 aBouysran, Youssef1 aPalom, Adriana1 aEllinghaus, David1 aMartínez-Bueno, Manuel1 aRolker, Selina1 aAmitrano, Sara1 aRoade, Luisa1 aFava, Francesca1 aSpinner, Christoph, D1 aPrati, Daniele1 aBernardo, David1 aGarcía, Federico1 aDarcis, Gilles1 aFernández-Cadenas, Israel1 aHolter, Jan, Cato1 aBanales, Jesus, M1 aFrithiof, Robert1 aDuga, Stefano1 aAsselta, Rosanna1 aPereira, Alexandre, C1 aRomero-Gómez, Manuel1 aNafría-Jiménez, Beatriz1 aHov, Johannes, R1 aMigeotte, Isabelle1 aRenieri, Alessandra1 aPlanas, Anna, M1 aLudwig, Kerstin, U1 aButi, Maria1 aRahmouni, Souad1 aAlarcón-Riquelme, Marta, E1 aSchulte, Eva, C1 aFranke, Andre1 aKarlsen, Tom, H1 aValenti, Luca1 aZeberg, Hugo1 aRichards, Brent1 aGanna, Andrea1 aBoada, Mercè1 aRojas, Itziar1 aRuiz, Agustín1 aSánchez, Pascual1 aReal, Luis, Miguel1 aGuillén-Navarro, Encarna1 aAyuso, Carmen1 aGonzález-Neira, Anna1 aRiancho, José, A1 aRojas-Martinez, Augusto1 aFlores, Carlos1 aLapunzina, Pablo1 aCarracedo, Ángel1 aSCOURGE Cohort Group1 aHOSTAGE Cohort Group1 aGRA@CE Cohort Group uhttps://www.clinbioinfosspa.es/content/novel-genes-and-sex-differences-covid-19-severity07193nas a2202077 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2021 eng d a1744-429200aCOVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.0 aCOVID19 Disease Map a computational knowledge repository of viru c2021 10 ae103870 v173 aWe need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
10aAntiviral Agents10aComputational Biology10aComputer Graphics10aCOVID-1910aCytokines10aData Mining10aDatabases, Factual10aGene Expression Regulation10aHost Microbial Interactions10aHumans10aImmunity, Cellular10aImmunity, Humoral10aImmunity, Innate10aLymphocytes10aMetabolic Networks and Pathways10aMyeloid Cells10aProtein Interaction Mapping10aSARS-CoV-210aSignal Transduction10aSoftware10aTranscription Factors10aViral Proteins1 aOstaszewski, Marek1 aNiarakis, Anna1 aMazein, Alexander1 aKuperstein, Inna1 aPhair, Robert1 aOrta-Resendiz, Aurelio1 aSingh, Vidisha1 aAghamiri, Sara, Sadat1 aAcencio, Marcio, Luis1 aGlaab, Enrico1 aRuepp, Andreas1 aFobo, Gisela1 aMontrone, Corinna1 aBrauner, Barbara1 aFrishman, Goar1 aGómez, Luis, Cristóbal1 aSomers, Julia1 aHoch, Matti1 aGupta, Shailendra, Kumar1 aScheel, Julia1 aBorlinghaus, Hanna1 aCzauderna, Tobias1 aSchreiber, Falk1 aMontagud, Arnau1 ade Leon, Miguel, Ponce1 aFunahashi, Akira1 aHiki, Yusuke1 aHiroi, Noriko1 aYamada, Takahiro, G1 aDräger, Andreas1 aRenz, Alina1 aNaveez, Muhammad1 aBocskei, Zsolt1 aMessina, Francesco1 aBörnigen, Daniela1 aFergusson, Liam1 aConti, Marta1 aRameil, Marius1 aNakonecnij, Vanessa1 aVanhoefer, Jakob1 aSchmiester, Leonard1 aWang, Muying1 aAckerman, Emily, E1 aShoemaker, Jason, E1 aZucker, Jeremy1 aOxford, Kristie1 aTeuton, Jeremy1 aKocakaya, Ebru1 aSummak, Gökçe, Yağmur1 aHanspers, Kristina1 aKutmon, Martina1 aCoort, Susan1 aEijssen, Lars1 aEhrhart, Friederike1 aRex, Devasahayam, Arokia Bal1 aSlenter, Denise1 aMartens, Marvin1 aPham, Nhung1 aHaw, Robin1 aJassal, Bijay1 aMatthews, Lisa1 aOrlic-Milacic, Marija1 aRibeiro, Andrea, Senff1 aRothfels, Karen1 aShamovsky, Veronica1 aStephan, Ralf1 aSevilla, Cristoffer1 aVarusai, Thawfeek1 aRavel, Jean-Marie1 aFraser, Rupsha1 aOrtseifen, Vera1 aMarchesi, Silvia1 aGawron, Piotr1 aSmula, Ewa1 aHeirendt, Laurent1 aSatagopam, Venkata1 aWu, Guanming1 aRiutta, Anders1 aGolebiewski, Martin1 aOwen, Stuart1 aGoble, Carole1 aHu, Xiaoming1 aOverall, Rupert, W1 aMaier, Dieter1 aBauch, Angela1 aGyori, Benjamin, M1 aBachman, John, A1 aVega, Carlos1 aGrouès, Valentin1 aVazquez, Miguel1 aPorras, Pablo1 aLicata, Luana1 aIannuccelli, Marta1 aSacco, Francesca1 aNesterova, Anastasia1 aYuryev, Anton1 ade Waard, Anita1 aTurei, Denes1 aLuna, Augustin1 aBabur, Ozgun1 aSoliman, Sylvain1 aValdeolivas, Alberto1 aEsteban-Medina, Marina1 aPeña-Chilet, Maria1 aRian, Kinza1 aHelikar, Tomáš1 aPuniya, Bhanwar, Lal1 aModos, Dezso1 aTreveil, Agatha1 aOlbei, Marton1 aDe Meulder, Bertrand1 aBallereau, Stephane1 aDugourd, Aurélien1 aNaldi, Aurélien1 aNoël, Vincent1 aCalzone, Laurence1 aSander, Chris1 aDemir, Emek1 aKorcsmaros, Tamas1 aFreeman, Tom, C1 aAugé, Franck1 aBeckmann, Jacques, S1 aHasenauer, Jan1 aWolkenhauer, Olaf1 aWilighagen, Egon, L1 aPico, Alexander, R1 aEvelo, Chris, T1 aGillespie, Marc, E1 aStein, Lincoln, D1 aHermjakob, Henning1 aD'Eustachio, Peter1 aSaez-Rodriguez, Julio1 aDopazo, Joaquin1 aValencia, Alfonso1 aKitano, Hiroaki1 aBarillot, Emmanuel1 aAuffray, Charles1 aBalling, Rudi1 aSchneider, Reinhard1 aCOVID-19 Disease Map Community uhttps://www.clinbioinfosspa.es/content/covid19-disease-map-computational-knowledge-repository-virus-host-interaction-mechanisms03083nas a2200493 4500008004100000022001400041245014600055210006900201260001200270300001400282490000700296520140800303100002001711700002401731700002901755700002801784700002501812700002501837700001801862700002401880700002901904700003101933700003501964700002101999700003102020700002102051700001902072700002802091700001802119700003102137700002802168700001702196700003902213700001702252700002502269700002202294700002002316700002302336700001802359700002202377700002902399700002502428856013602453 2021 eng d a1878-026100aA DNA damage repair gene-associated signature predicts responses of patients with advanced soft-tissue sarcoma to treatment with trabectedin.0 aDNA damage repair geneassociated signature predicts responses of c2021 12 a3691-37050 v153 aPredictive biomarkers of trabectedin represent an unmet need in advanced soft-tissue sarcomas (STS). DNA damage repair (DDR) genes, involved in homologous recombination or nucleotide excision repair, had been previously described as biomarkers of trabectedin resistance or sensitivity, respectively. The majority of these studies only focused on specific factors (ERCC1, ERCC5, and BRCA1) and did not evaluate several other DDR-related genes that could have a relevant role for trabectedin efficacy. In this retrospective translational study, 118 genes involved in DDR were evaluated to determine, by transcriptomics, a predictive gene signature of trabectedin efficacy. A six-gene predictive signature of trabectedin efficacy was built in a series of 139 tumor samples from patients with advanced STS. Patients in the high-risk gene signature group showed a significantly worse progression-free survival compared with patients in the low-risk group (2.1 vs 6.0 months, respectively). Differential gene expression analysis defined new potential predictive biomarkers of trabectedin sensitivity (PARP3 and CCNH) or resistance (DNAJB11 and PARP1). Our study identified a new gene signature that significantly predicts patients with higher probability to respond to treatment with trabectedin. Targeting some genes of this signature emerges as a potential strategy to enhance trabectedin efficacy.
1 aMoura, David, S1 aPeña-Chilet, Maria1 aVarela, Juan, Antonio Co1 aAlvarez-Alegret, Ramiro1 aAgra-Pujol, Carolina1 aIzquierdo, Francisco1 aRamos, Rafael1 aOrtega-Medina, Luis1 aMartin-Davila, Francisco1 aCastilla-Ramirez, Carolina1 aHernandez-Leon, Carmen, Nieves1 aRomagosa, Cleofe1 aSalgado, Maria, Angeles Va1 aLavernia, Javier1 aBagué, Silvia1 aMayodormo-Aranda, Empar1 aVicioso, Luis1 aBarceló, Jose, Emilio Her1 aRubio-Casadevall, Jordi1 ade Juan, Ana1 aFiaño-Valverde, Maria, Concepcion1 aHindi, Nadia1 aLopez-Alvarez, Maria1 aLacerenza, Serena1 aDopazo, Joaquin1 aGutierrez, Antonio1 aAlvarez, Rosa1 aValverde, Claudia1 aMartinez-Trufero, Javier1 aMartin-Broto, Javier uhttps://www.clinbioinfosspa.es/content/dna-damage-repair-gene-associated-signature-predicts-responses-patients-advanced-soft-tissue01028nas a2200313 4500008004100000022001400041245008100055210006900136260001200205300001400217490000700231653001500238653002600253653002400279653001100303653002300314653002000337653003200357100001500389700002000404700002600424700001700450700002400467700002100491700002600512700002500538710004000563856011100603 2021 eng d a1548-710500aDOME: recommendations for supervised machine learning validation in biology.0 aDOME recommendations for supervised machine learning validation c2021 10 a1122-11270 v1810aAlgorithms10aComputational Biology10aGuidelines as Topic10aHumans10aModels, Biological10aResearch Design10aSupervised Machine Learning1 aWalsh, Ian1 aFishman, Dmytro1 aGarcia-Gasulla, Dario1 aTitma, Tiina1 aPollastri, Gianluca1 aHarrow, Jennifer1 aPsomopoulos, Fotis, E1 aTosatto, Silvio, C E1 aELIXIR Machine Learning Focus Group uhttps://www.clinbioinfosspa.es/content/dome-recommendations-supervised-machine-learning-validation-biology00806nas a2200229 4500008004100000022001300041245014700054210006900201260001600270300001600286490000700302100001600309700002300325700001800348700002200366700002000388700002700408700003000435700002400465700002000489856006700509 2021 eng d a2001037000aGenome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data0 aGenomescale mechanistic modeling of signaling pathways made easy cJan-01-2021 a2968 - 29780 v191 aRian, Kinza1 aHidalgo, Marta, R.1 aCubuk, Cankut1 aFalco, Matias, M.1 aLoucera, Carlos1 aEsteban-Medina, Marina1 aAlamo-Alvarez, Inmaculada1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://linkinghub.elsevier.com/retrieve/pii/S200103702100203801577nas a2200253 4500008004100000022001400041245005600055210005300111260001600164300000600180490000700186520083300193100001601026700002701042700002201069700001801091700002101109700002001130700001901150700002301169700002401192700002001216856008701236 2021 eng d a1756-038100aMechanistic modeling of the SARS-CoV-2 disease map.0 aMechanistic modeling of the SARSCoV2 disease map c2021 Jan 21 a50 v143 aHere we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments.
1 aRian, Kinza1 aEsteban-Medina, Marina1 aHidalgo, Marta, R1 aCubuk, Cankut1 aFalco, Matias, M1 aLoucera, Carlos1 aGunyel, Devrim1 aOstaszewski, Marek1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/mechanistic-modeling-sars-cov-2-disease-map03248nas a2201189 4500008004100000022001400041245003300055210002900088260001600117100002300133700002200156700001900178700001500197700002500212700001700237700001900254700001700273700002900290700001800319700001600337700002200353700001300375700001700388700002000405700001800425700002500443700002500468700002800493700001800521700001900539700001900558700002300577700002600600700002400626700002500650700002600675700001700701700001400718700002400732700001600756700002100772700002100793700001500814700001900829700002100848700002100869700002400890700001700914700002600931700001900957700001700976700002100993700002501014700002301039700001601062700002301078700001901101700002101120700001901141700002401160700002701184700002001211700002101231700002101252700002201273700002001295700002101315700002001336700001901356700001801375700001901393700002501412700002201437700002401459700001901483700002101502700001901523700001901542700001701561700002601578700002401604700001601628700001801644700002201662700001701684700001801701700001401719700002501733700001701758700002301775700001701798700001801815700002401833700002801857700001801885700001801903700001901921700001701940700002201957700002501979856005402004 2021 eng d a1061-403600aThe NCI Genomic Data Commons0 aNCI Genomic Data Commons cOct-02-20221 aHeath, Allison, P.1 aFerretti, Vincent1 aAgrawal, Stuti1 aAn, Maksim1 aAngelakos, James, C.1 aArya, Renuka1 aBajari, Rosita1 aBaqar, Bilal1 aBarnowski, Justin, H. B.1 aBurt, Jeffrey1 aCatton, Ann1 aChan, Brandon, F.1 aChu, Fay1 aCullion, Kim1 aDavidsen, Tanja1 aDo, Phuong-My1 aDompierre, Christian1 aFerguson, Martin, L.1 aFitzsimons, Michael, S.1 aFord, Michael1 aFukuma, Miyuki1 aGaheen, Sharon1 aGanji, Gajanan, L.1 aGarcia, Tzintzuni, I.1 aGeorge, Sameera, S.1 aGerhard, Daniela, S.1 aGerthoffert, Francois1 aGomez, Fauzi1 aHan, Kang1 aHernandez, Kyle, M.1 aIssac, Biju1 aJackson, Richard1 aJensen, Mark, A.1 aJoshi, Sid1 aKadam, Ajinkya1 aKhurana, Aishmit1 aKim, Kyle, M. J.1 aKraft, Victoria, E.1 aLi, Shenglai1 aLichtenberg, Tara, M.1 aLodato, Janice1 aLolla, Laxmi1 aMartinov, Plamen1 aMazzone, Jeffrey, A.1 aMiller, Daniel, P.1 aMiller, Ian1 aMiller, Joshua, S.1 aMiyauchi, Koji1 aMurphy, Mark, W.1 aNullet, Thomas1 aOgwara, Rowland, O.1 aOrtuño, Francisco, M.1 aPedrosa, Jesús1 aPham, Phuong, L.1 aPopov, Maxim, Y.1 aPorter, James, J.1 aPowell, Raymond1 aRademacher, Karl1 aReid, Colin, P.1 aRich, Samantha1 aRogel, Bessie1 aSahni, Himanso1 aSavage, Jeremiah, H.1 aSchmitt, Kyle, A.1 aSimmons, Trevar, J.1 aSislow, Joseph1 aSpring, Jonathan1 aStein, Lincoln1 aSullivan, Sean1 aTang, Yajing1 aThiagarajan, Mathangi1 aTroyer, Heather, D.1 aWang, Chang1 aWang, Zhining1 aWest, Bedford, L.1 aWilmer, Alex1 aWilson, Shane1 aWu, Kaman1 aWysocki, William, P.1 aXiang, Linda1 aYamada, Joseph, T.1 aYang, Liming1 aYu, Christine1 aYung, Christina, K.1 aZenklusen, Jean, Claude1 aZhang, Junjun1 aZhang, Zhenyu1 aZhao, Yuanheng1 aZubair, Ariz1 aStaudt, Louis, M.1 aGrossman, Robert, L. uhttp://www.nature.com/articles/s41588-021-00791-502217nas a2200349 4500008004100000022001400041245009500055210006900150260001500219300000900234490000700243520109300250100002301343700001801366700001901384700002201403700002301425700001601448700002701464700001901491700001501510700001601525700001801541700001901559700001301578700001501591700001801606700001701624700002601641710007101667856012901738 2021 eng d a2041-172300aOrchestrating and sharing large multimodal data for transparent and reproducible research.0 aOrchestrating and sharing large multimodal data for transparent c2021 10 04 a57970 v123 aReproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.
1 aMammoliti, Anthony1 aSmirnov, Petr1 aNakano, Minoru1 aSafikhani, Zhaleh1 aEeles, Christopher1 aSeo, Heewon1 aNair, Sisira, Kadambat1 aMer, Arvind, S1 aSmith, Ian1 aHo, Chantal1 aBeri, Gangesh1 aKusko, Rebecca1 aLin, Eva1 aYu, Yihong1 aMartin, Scott1 aHafner, Marc1 aHaibe-Kains, Benjamin1 aMassive Analysis Quality Control (MAQC) Society Board of Directors uhttps://www.clinbioinfosspa.es/content/orchestrating-and-sharing-large-multimodal-data-transparent-and-reproducible-research05409nas a2201477 4500008004100000022001400041245007800055210006900133260001200202300001400214490000700228520122900235653002601464653001401490653001101504653001501515653003501530653002001565653003801585100001901623700001901642700001801661700002301679700002401702700002301726700002101749700002801770700001801798700002501816700002401841700002701865700001801892700002301910700002001933700001501953700002201968700002001990700002702010700002102037700002202058700001802080700001902098700002702117700002002144700001902164700002002183700002102203700001702224700002102241700002302262700002002285700002202305700002002327700001802347700002302365700001802388700002102406700002602427700001902453700001702472700001802489700002402507700001902531700001602550700001702566700001602583700002102599700002102620700002102641700002102662700002202683700002202705700002102727700001602748700001702764700001502781700002102796700001702817700002402834700002202858700002002880700002402900700001802924700001602942700002302958700002102981700002403002700001703026700002603043700002403069700002503093700002403118700002203142700002103164700001703185700001903202700001803221700001803239700001303257700001703270700001603287700001903303700002703322700002003349700001703369700002203386700001903408700001903427700002603446700001903472700002903491700002103520700001603541700002203557700001603579700002003595700002403615700001903639700002403658700002203682700002003704700001803724710003303742710004903775856010703824 2021 eng d a1546-170X00aReporting guidelines for human microbiome research: the STORMS checklist.0 aReporting guidelines for human microbiome research the STORMS ch c2021 11 a1885-18920 v273 aThe particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.
10aComputational Biology10aDysbiosis10aHumans10aMicrobiota10aObservational Studies as Topic10aResearch Design10aTranslational Science, Biomedical1 aMirzayi, Chloe1 aRenson, Audrey1 aZohra, Fatima1 aElsafoury, Shaimaa1 aGeistlinger, Ludwig1 aKasselman, Lora, J1 aEckenrode, Kelly1 avan de Wijgert, Janneke1 aLoughman, Amy1 aMarques, Francine, Z1 aMacIntyre, David, A1 aArumugam, Manimozhiyan1 aAzhar, Rimsha1 aBeghini, Francesco1 aBergstrom, Kirk1 aBhatt, Ami1 aBisanz, Jordan, E1 aBraun, Jonathan1 aBravo, Hector, Corrada1 aBuck, Gregory, A1 aBushman, Frederic1 aCasero, David1 aClarke, Gerard1 aCollado, Maria, Carmen1 aCotter, Paul, D1 aCryan, John, F1 aDemmer, Ryan, T1 aDevkota, Suzanne1 aElinav, Eran1 aEscobar, Juan, S1 aFettweis, Jennifer1 aFinn, Robert, D1 aFodor, Anthony, A1 aForslund, Sofia1 aFranke, Andre1 aFurlanello, Cesare1 aGilbert, Jack1 aGrice, Elizabeth1 aHaibe-Kains, Benjamin1 aHandley, Scott1 aHerd, Pamela1 aHolmes, Susan1 aJacobs, Jonathan, P1 aKarstens, Lisa1 aKnight, Rob1 aKnights, Dan1 aKoren, Omry1 aKwon, Douglas, S1 aLangille, Morgan1 aLindsay, Brianna1 aMcGovern, Dermot1 aMcHardy, Alice, C1 aMcWeeney, Shannon1 aMueller, Noel, T1 aNezi, Luigi1 aOlm, Matthew1 aPalm, Noah1 aPasolli, Edoardo1 aRaes, Jeroen1 aRedinbo, Matthew, R1 aRühlemann, Malte1 aSartor, Balfour1 aSchloss, Patrick, D1 aSchriml, Lynn1 aSegal, Eran1 aShardell, Michelle1 aSharpton, Thomas1 aSmirnova, Ekaterina1 aSokol, Harry1 aSonnenburg, Justin, L1 aSrinivasan, Sujatha1 aThingholm, Louise, B1 aTurnbaugh, Peter, J1 aUpadhyay, Vaibhav1 aWalls, Ramona, L1 aWilmes, Paul1 aYamada, Takuji1 aZeller, Georg1 aZhang, Mingyu1 aZhao, Ni1 aZhao, Liping1 aBao, Wenjun1 aCulhane, Aedin1 aDevanarayan, Viswanath1 aDopazo, Joaquin1 aFan, Xiaohui1 aFischer, Matthias1 aJones, Wendell1 aKusko, Rebecca1 aMason, Christopher, E1 aMercer, Tim, R1 aSansone, Susanna-Assunta1 aScherer, Andreas1 aShi, Leming1 aThakkar, Shraddha1 aTong, Weida1 aWolfinger, Russ1 aHunter, Christopher1 aSegata, Nicola1 aHuttenhower, Curtis1 aDowd, Jennifer, B1 aJones, Heidi, E1 aWaldron, Levi1 aGenomic Standards Consortium1 aMassive Analysis and Quality Control Society uhttps://www.clinbioinfosspa.es/content/reporting-guidelines-human-microbiome-research-storms-checklist00678nas a2200217 4500008004100000245007400041210006900115260001600184490000700200100001800207700002000225700002100245700001700266700001600283700001900299700002200318700002200340700001900362700002500381856005400406 2021 eng d00aUniform genomic data analysis in the NCI Genomic Data CommonsAbstract0 aUniform genomic data analysis in the NCI Genomic Data CommonsAbs cJan-12-20210 v121 aZhang, Zhenyu1 aHernandez, Kyle1 aSavage, Jeremiah1 aLi, Shenglai1 aMiller, Dan1 aAgrawal, Stuti1 aOrtuno, Francisco1 aStaudt, Louis, M.1 aHeath, Allison1 aGrossman, Robert, L. uhttp://www.nature.com/articles/s41467-021-21254-902937nas a2200613 4500008004100000022001400041245012600055210006900181260001500250300001500265490000700280520120500287653001801492653001101510653001301521653001101534653002101545653000901566653001401575653002001589653001301609653001501622653001801637100001301655700002401668700001201692700001701704700001601721700001701737700001601754700001601770700001201786700001401798700001601812700001501828700002101843700002101864700002201885700001501907700002301922700002101945700002101966700001601987700001602003700002102019700002502040700001902065700001702084700001402101700001802115700002602133710003102159856013302190 2020 eng d a2405-472000aCommunity Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.0 aCommunity Assessment of the Predictability of Cancer Protein and c2020 08 26 a186-195.e90 v113 aCancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.
10aCrowdsourcing10aFemale10aGenomics10aHumans10aMachine Learning10aMale10aNeoplasms10aPhosphoproteins10aProteins10aProteomics10aTranscriptome1 aYang, Mi1 aPetralia, Francesca1 aLi, Zhi1 aLi, Hongyang1 aMa, Weiping1 aSong, Xiaoyu1 aKim, Sunkyu1 aLee, Heewon1 aYu, Han1 aLee, Bora1 aBae, Seohui1 aHeo, Eunji1 aKaczmarczyk, Jan1 aStępniak, Piotr1 aWarchoł, Michał1 aYu, Thomas1 aCalinawan, Anna, P1 aBoutros, Paul, C1 aPayne, Samuel, H1 aReva, Boris1 aBoja, Emily1 aRodriguez, Henry1 aStolovitzky, Gustavo1 aGuan, Yuanfang1 aKang, Jaewoo1 aWang, Pei1 aFenyö, David1 aSaez-Rodriguez, Julio1 aNCI-CPTAC-DREAM Consortium uhttps://www.clinbioinfosspa.es/content/community-assessment-predictability-cancer-protein-and-phosphoprotein-levels-genomics-and01798nas a2200577 4500008004100000022001400041245011100055210006900166260001500235300000800250490000600258653002000264653002600284653002700310653001300337653002300350653003200373653003100405653001100436653003000447653002300477653001400500653002100514653001500535100002300550700002200573700002300595700002100618700001900639700002300658700002300681700002500704700002000729700001900749700002000768700002100788700001600809700002200825700002200847700002300869700002000892700002700912700002300939700002200962700002100984700002001005700002101025700001801046700002401064856013201088 2020 eng d a2052-446300aCOVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.0 aCOVID19 Disease Map building a computational repository of SARSC c2020 05 05 a1360 v710aBetacoronavirus10aComputational Biology10aCoronavirus Infections10aCOVID-1910aDatabases, Factual10aHost Microbial Interactions10aHost-Pathogen Interactions10aHumans10aInternational Cooperation10aModels, Biological10aPandemics10aPneumonia, Viral10aSARS-CoV-21 aOstaszewski, Marek1 aMazein, Alexander1 aGillespie, Marc, E1 aKuperstein, Inna1 aNiarakis, Anna1 aHermjakob, Henning1 aPico, Alexander, R1 aWillighagen, Egon, L1 aEvelo, Chris, T1 aHasenauer, Jan1 aSchreiber, Falk1 aDräger, Andreas1 aDemir, Emek1 aWolkenhauer, Olaf1 aFurlong, Laura, I1 aBarillot, Emmanuel1 aDopazo, Joaquin1 aOrta-Resendiz, Aurelio1 aMessina, Francesco1 aValencia, Alfonso1 aFunahashi, Akira1 aKitano, Hiroaki1 aAuffray, Charles1 aBalling, Rudi1 aSchneider, Reinhard uhttps://www.clinbioinfosspa.es/content/covid-19-disease-map-building-computational-repository-sars-cov-2-virus-host-interaction03125nas a2200673 4500008004100000022001400041245010800055210006900163260000900232490000600241520108900247653002601336653003101362653004201393653001101435100001901446700002101465700002001486700001901506700003301525700002701558700003501585700002001620700002201640700001201662700001801674700002201692700001301714700002101727700002001748700002101768700001801789700001801807700002601825700001801851700001601869700002401885700001801909700002101927700001701948700002701965700001801992700002302010700001902033700002702052700001402079700002302093700001702116700001902133700001502152700002202167700002102189700002202210700001702232700003202249700001802281700002402299856012802323 2020 eng d a2046-140200aThe ELIXIR Human Copy Number Variations Community: building bioinformatics infrastructure for research.0 aELIXIR Human Copy Number Variations Community building bioinform c20200 v93 aCopy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While "High-Throughput" sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR's recently established with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context.
10aComputational Biology10aDNA Copy Number Variations10aHigh-Throughput Nucleotide Sequencing10aHumans1 aSalgado, David1 aArmean, Irina, M1 aBaudis, Michael1 aBeltran, Sergi1 aCapella-Gutíerrez, Salvador1 aCarvalho-Silva, Denise1 aDel Angel, Victoria, Dominguez1 aDopazo, Joaquin1 aFurlong, Laura, I1 aGao, Bo1 aGarcia, Leyla1 aGerloff, Dietlind1 aGut, Ivo1 aGyenesei, Attila1 aHabermann, Nina1 aHancock, John, M1 aHanauer, Marc1 aHovig, Eivind1 aJohansson, Lennart, F1 aKeane, Thomas1 aKorbel, Jan1 aLauer, Katharina, B1 aLaurie, Steve1 aLeskošek, Brane1 aLloyd, David1 aMarqués-Bonet, Tomás1 aMei, Hailiang1 aMonostory, Katalin1 aPiñero, Janet1 aPoterlowicz, Krzysztof1 aRath, Ana1 aSamarakoon, Pubudu1 aSanz, Ferran1 aSaunders, Gary1 aSie, Daoud1 aSwertz, Morris, A1 aTsukanov, Kirill1 aValencia, Alfonso1 aVidak, Marko1 aGonzález, Cristina, Yenyxe1 aYlstra, Bauke1 aBéroud, Christophe uhttps://www.clinbioinfosspa.es/content/elixir-human-copy-number-variations-community-building-bioinformatics-infrastructure02422nas a2200481 4500008004100000022001400041245004700055210004600102260001600148300001100164490000700175520106100182100001901243700002701262700001901289700002001308700002201328700002101350700002001371700001901391700002401410700001501434700002501449700002301474700002401497700002001521700002701541700002401568700002001592700002101612700002201633700002201655700002901677700001801706700001301724700001701737700001601754700002001770700002501790700001901815700002601834856008001860 2020 eng d a2589-004200aImmune Cell Associations with Cancer Risk.0 aImmune Cell Associations with Cancer Risk c2020 Jul 24 a1012960 v233 aProper immune system function hinders cancer development, but little is known about whether genetic variants linked to cancer risk alter immune cells. Here, we report 57 cancer risk loci associated with differences in immune and/or stromal cell contents in the corresponding tissue. Predicted target genes show expression and regulatory associations with immune features. Polygenic risk scores also reveal associations with immune and/or stromal cell contents, and breast cancer scores show consistent results in normal and tumor tissue. SH2B3 links peripheral alterations of several immune cell types to the risk of this malignancy. Pleiotropic SH2B3 variants are associated with breast cancer risk in BRCA1/2 mutation carriers. A retrospective case-cohort study indicates a positive association between blood counts of basophils, leukocytes, and monocytes and age at breast cancer diagnosis. These findings broaden our knowledge of the role of the immune system in cancer and highlight promising prevention strategies for individuals at high risk.
1 aPalomero, Luis1 aGalván-Femenía, Ivan1 ade Cid, Rafael1 aEspín, Roderic1 aBarnes, Daniel, R1 aBlommaert, Eline1 aGil-Gil, Miguel1 aFalo, Catalina1 aStradella, Agostina1 aOuchi, Dan1 aRoso-Llorach, Albert1 aViolan, Concepció1 aPeña-Chilet, Maria1 aDopazo, Joaquin1 aExtremera, Ana, Isabel1 aGarcía-Valero, Mar1 aHerranz, Carmen1 aMateo, Francesca1 aMereu, Elisabetta1 aBeesley, Jonathan1 aChenevix-Trench, Georgia1 aRoux, Cecilia1 aMak, Tak1 aBrunet, Joan1 aHakem, Razq1 aGorrini, Chiara1 aAntoniou, Antonis, C1 aLázaro, Conxi1 aPujana, Miquel, Angel uhttps://www.clinbioinfosspa.es/content/immune-cell-associations-cancer-risk00750nas a2200157 4500008004100000245011500041210006900156260001600225490000600241100002100247700002400268700002000292700002200312700002000334856023800354 2020 eng d00aMechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscapeAbstract0 aMechanistic models of signaling pathways deconvolute the gliobla cJan-06-20200 v21 aFalco, Matias, M1 aPeña-Chilet, Maria1 aLoucera, Carlos1 aHidalgo, Marta, R1 aDopazo, Joaquin uhttps://academic.oup.com/narcancer/article/doi/10.1093/narcan/zcaa011/5862620http://academic.oup.com/narcancer/article-pdf/2/2/zcaa011/33428092/zcaa011.pdfhttp://academic.oup.com/narcancer/article-pdf/2/2/zcaa011/33428092/zcaa011.pdf03275nas a2200493 4500008004100000022001400041245012000055210006900175260001200244490000600256520175600262653001002018653000902028653004102037653001102078653001102089653000902100653001602109653001402125653001202139653001402151653001602165100002502181700001702206700002302223700002902246700002002275700002202295700002402317700002502341700002402366700002302390700002502413700002802438700002202466700001902488700002402507700002002531700002702551700002502578700002002603700002602623856013202649 2020 eng d a2051-142600aNivolumab and sunitinib combination in advanced soft tissue sarcomas: a multicenter, single-arm, phase Ib/II trial.0 aNivolumab and sunitinib combination in advanced soft tissue sarc c2020 110 v83 aBACKGROUND: Sarcomas exhibit low expression of factors related to immune response, which could explain the modest activity of PD-1 inhibitors. A potential strategy to convert a cold into an inflamed microenvironment lies on a combination therapy. As tumor angiogenesis promotes immunosuppression, we designed a phase Ib/II trial to test the double inhibition of angiogenesis (sunitinib) and PD-1/PD-L1 axis (nivolumab).
METHODS: This single-arm, phase Ib/II trial enrolled adult patients with selected subtypes of sarcoma. Phase Ib established two dose levels: level 0 with sunitinib 37.5 mg daily from day 1, plus nivolumab 3 mg/kg intravenously on day 15, and then every 2 weeks; and level -1 with sunitinib 37.5 mg on the first 14 days (induction) and then 25 mg per day plus nivolumab on the same schedule. The primary endpoint was to determine the recommended dose for phase II (phase I) and the 6-month progression-free survival rate, according to Response Evaluation Criteria in Solid Tumors 1.1 (phase II).
RESULTS: From May 2017 to April 2019, 68 patients were enrolled: 16 in phase Ib and 52 in phase II. The recommended dose of sunitinib for phase II was 37.5 mg as induction and then 25 mg in combination with nivolumab. After a median follow-up of 17 months (4-26), the 6-month progression-free survival rate was 48% (95% CI 41% to 55%). The most common grade 3-4 adverse events included transaminitis (17.3%) and neutropenia (11.5%).
CONCLUSIONS: Sunitinib plus nivolumab is an active scheme with manageable toxicity in the treatment of selected patients with advanced soft tissue sarcoma, with almost half of patients free of progression at 6 months. NCT03277924.
10aAdult10aAged10aAntineoplastic Agents, Immunological10aFemale10aHumans10aMale10aMiddle Aged10aNivolumab10aSarcoma10aSunitinib10aYoung Adult1 aMartin-Broto, Javier1 aHindi, Nadia1 aGrignani, Giovanni1 aMartinez-Trufero, Javier1 aRedondo, Andres1 aValverde, Claudia1 aStacchiotti, Silvia1 aLopez-Pousa, Antonio1 aD'Ambrosio, Lorenzo1 aGutierrez, Antonio1 aPerez-Vega, Herminia1 aEncinas-Tobajas, Victor1 ade Alava, Enrique1 aCollini, Paola1 aPeña-Chilet, Maria1 aDopazo, Joaquin1 aCarrasco-Garcia, Irene1 aLopez-Alvarez, Maria1 aMoura, David, S1 aLopez-Martin, Jose, A uhttps://www.clinbioinfosspa.es/content/nivolumab-and-sunitinib-combination-advanced-soft-tissue-sarcomas-multicenter-single-arm05180nas a2201117 4500008004100000022001400041245011300055210006900168260001200237300001200249490000700261520174900268653001402017653003102031653001502062653002502077653001902102653005102121653001102172653002902183653003802212653001102250653000902261653002302270653003602293653002702329100002202356700001702378700002402395700002802419700003302447700002702480700001502507700002402522700002902546700002602575700002802601700001702629700002002646700002102666700001902687700002702706700001902733700002002752700002402772700003002796700001902826700002602845700002902871700001802900700002102918700002202939700003202961700002002993700002603013700002903039700002103068700002203089700002103111700001903132700002703151700002403178700002903202700003203231700002003263700001803283700003103301700002803332700001603360700002503376700003103401700003303432700002903465700002203494700002103516700002403537700001903561700002503580700001703605700001703622700001803639700002303657700002003680700001603700700002203716700002503738700001803763700002303781700002003804700002003824700002103844700003303865700001703898700002103915856012603936 2020 eng d a1468-624400aOptimised molecular genetic diagnostics of Fanconi anaemia by whole exome sequencing and functional studies.0 aOptimised molecular genetic diagnostics of Fanconi anaemia by wh c2020 04 a258-2680 v573 aPURPOSE: Patients with Fanconi anaemia (FA), a rare DNA repair genetic disease, exhibit chromosome fragility, bone marrow failure, malformations and cancer susceptibility. FA molecular diagnosis is challenging since FA is caused by point mutations and large deletions in 22 genes following three heritability patterns. To optimise FA patients' characterisation, we developed a simplified but effective methodology based on whole exome sequencing (WES) and functional studies.
METHODS: 68 patients with FA were analysed by commercial WES services. Copy number variations were evaluated by sequencing data analysis with RStudio. To test missense variants, wt FANCA cDNA was cloned and variants were introduced by site-directed mutagenesis. Vectors were then tested for their ability to complement DNA repair defects of a FANCA-KO human cell line generated by TALEN technologies.
RESULTS: We identified 93.3% of mutated alleles including large deletions. We determined the pathogenicity of three FANCA missense variants and demonstrated that two variants reported in mutations databases as 'affecting functions' are SNPs. Deep analysis of sequencing data revealed patients' true mutations, highlighting the importance of functional analysis. In one patient, no pathogenic variant could be identified in any of the 22 known FA genes, and in seven patients, only one deleterious variant could be identified (three patients each with FANCA and FANCD2 and one patient with FANCE mutations) CONCLUSION: WES and proper bioinformatics analysis are sufficient to effectively characterise patients with FA regardless of the rarity of their complementation group, type of mutations, mosaic condition and DNA source.
10aCell Line10aDNA Copy Number Variations10aDNA Repair10aDNA-Binding Proteins10aFanconi Anemia10aFanconi Anemia Complementation Group A Protein10aFemale10aGene Knockout Techniques10aGenetic Predisposition to Disease10aHumans10aMale10aMutation, Missense10aPolymorphism, Single Nucleotide10awhole exome sequencing1 aBogliolo, Massimo1 aPujol, Roser1 aAza-Carmona, Miriam1 aMuñoz-Subirana, Núria1 aRodriguez-Santiago, Benjamin1 aCasado, José, Antonio1 aRio, Paula1 aBauser, Christopher1 aReina-Castillón, Judith1 aLopez-Sanchez, Marcos1 aGonzalez-Quereda, Lidia1 aGallano, Pia1 aCatalá, Albert1 aRuiz-Llobet, Ana1 aBadell, Isabel1 aDiaz-Heredia, Cristina1 aHladun, Raquel1 aSenent, Leonort1 aArgiles, Bienvenida1 aBurgues, Juan, Miguel Ber1 aBañez, Fatima1 aArrizabalaga, Beatriz1 aAlmaraz, Ricardo, López1 aLopez, Monica1 aFiguera, Ángela1 aMolinés, Antonio1 ade Soto, Inmaculada, Pérez1 aHernando, Inés1 aMuñoz, Juan, Antonio1 aMarin, Maria, Del Rosari1 aBalmaña, Judith1 aStjepanovic, Neda1 aCarrasco, Estela1 aCuesta, Isabel1 aCosuelo, José, Miguel1 aRegueiro, Alexandra1 aJimenez, José, Moraleda1 aGalera-Miñarro, Ana, Maria1 aRosiñol, Laura1 aCarrió, Anna1 aBeléndez-Bieler, Cristina1 aSoto, Antonio, Escudero1 aCela, Elena1 ade la Mata, Gregorio1 aFernández-Delgado, Rafael1 aGarcia-Pardos, Maria, Carmen1 aSáez-Villaverde, Raquel1 aBarragaño, Marta1 aPortugal, Raquel1 aLendinez, Francisco1 aHernadez, Ines1 aVagace, José, Manue1 aTapia, Maria1 aNieto, José1 aGarcia, Marta1 aGonzalez, Macarena1 aVicho, Cristina1 aGalvez, Eva1 aValiente, Alberto1 aAntelo, Maria, Luisa1 aAncliff, Phil1 aGarcía, Francisco1 aDopazo, Joaquin1 aSevilla, Julian1 aPaprotka, Tobias1 aPérez-Jurado, Luis, Alberto1 aBueren, Juan1 aSurralles, Jordi uhttps://www.clinbioinfosspa.es/content/optimised-molecular-genetic-diagnostics-fanconi-anaemia-whole-exome-sequencing-and04661nas a2200625 4500008004100000022001400041245010700055210006900162260001200231300001200243490000700255520277400262653000903036653001103045653002203056653001103078653001403089653000903103653001603112653002403128653001403152653002403166653003003190653001603220653004903236653002803285653001703313653001803330100002503348700001903373700001903392700001903411700001703430700001603447700002003463700002103483700002203504700003403526700002003560700002403580700002303604700001903627700002003646700002003666700002503686700002303711700002203734700002003756700002903776700003203805700002103837700002003858700002403878856013303902 2020 eng d a1474-548800aPazopanib for treatment of typical solitary fibrous tumours: a multicentre, single-arm, phase 2 trial.0 aPazopanib for treatment of typical solitary fibrous tumours a mu c2020 03 a456-4660 v213 aBACKGROUND: Solitary fibrous tumour is an ultra-rare sarcoma, which encompasses different clinicopathological subgroups. The dedifferentiated subgroup shows an aggressive course with resistance to pazopanib, whereas in the malignant subgroup, pazopanib shows higher activity than in previous studies with chemotherapy. We designed a trial to test pazopanib activity in two different cohorts of solitary fibrous tumour: the malignant-dedifferentiated cohort, which was previously published, and the typical cohort, which is presented here.
METHODS: In this single-arm, phase 2 trial, adult patients (aged ≥18 years) diagnosed with confirmed metastatic or unresectable typical solitary fibrous tumour of any location, who had progressed in the previous 6 months (by Choi criteria or Response Evaluation Criteria in Solid Tumors [RECIST]) and an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2 were enrolled at 11 tertiary hospitals in Italy, France, and Spain. Patients received pazopanib 800 mg once daily, taken orally, until progression, unacceptable toxicity, withdrawal of consent, non-compliance, or a delay in pazopanib administration of longer than 3 weeks. The primary endpoint was proportion of patients achieving an overall response measured by Choi criteria in patients who received at least 1 month of treatment with at least one radiological assessment. All patients who received at least one dose of the study drug were included in the safety analyses. This study is registered in ClinicalTrials.gov, NCT02066285, and with the European Clinical Trials Database, EudraCT 2013-005456-15.
FINDINGS: From June 26, 2014, to Dec 13, 2018, of 40 patients who were assessed, 34 patients were enrolled and 31 patients were included in the response analysis. Median follow-up was 18 months (IQR 14-34), and 18 (58%) of 31 patients had a partial response, 12 (39%) had stable disease, and one (3%) showed progressive disease according to Choi criteria and central review. The proportion of overall response based on Choi criteria was 58% (95% CI 34-69). There were no deaths caused by toxicity, and the most frequent adverse events were diarrhoea (18 [53%] of 34 patients), fatigue (17 [50%]), and hypertension (17 [50%]).
INTERPRETATION: To our knowledge, this is the first prospective trial of pazopanib for advanced typical solitary fibrous tumour. The manageable toxicity and activity shown by pazopanib in this cohort suggest that this drug could be considered as first-line treatment for advanced typical solitary fibrous tumour.
FUNDING: Spanish Group for Research on Sarcomas (GEIS), Italian Sarcoma Group (ISG), French Sarcoma Group (FSG), GlaxoSmithKline, and Novartis.
10aAged10aFemale10aFollow-Up Studies10aHumans10aIndazoles10aMale10aMiddle Aged10aNeoplasm Metastasis10aPrognosis10aProspective Studies10aProtein Kinase Inhibitors10aPyrimidines10aResponse Evaluation Criteria in Solid Tumors10aSolitary Fibrous Tumors10aSulfonamides10aSurvival Rate1 aMartin-Broto, Javier1 aCruz, Josefina1 aPenel, Nicolas1 aLe Cesne, Axel1 aHindi, Nadia1 aLuna, Pablo1 aMoura, David, S1 aBernabeu, Daniel1 ade Alava, Enrique1 aLopez-Guerrero, Jose, Antonio1 aDopazo, Joaquin1 aPeña-Chilet, Maria1 aGutierrez, Antonio1 aCollini, Paola1 aKaranian, Marie1 aRedondo, Andres1 aLopez-Pousa, Antonio1 aGrignani, Giovanni1 aDiaz-Martin, Juan1 aMarcilla, David1 aFernandez-Serra, Antonio1 aGonzalez-Aguilera, Cristina1 aCasali, Paolo, G1 aBlay, Jean-Yves1 aStacchiotti, Silvia uhttps://www.clinbioinfosspa.es/content/pazopanib-treatment-typical-solitary-fibrous-tumours-multicentre-single-arm-phase-2-trial02700nas a2200325 4500008004100000022001400041245009200055210006900147260001200216300001400228490000700242520165100249653002201900653002601922653003301948653003001981653001102011653002102022653001202043653001902055100002002074700002902094700002102123700002102144700001802165700002002183700002502203700001902228856012702247 2020 eng d a2168-220800aTowards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.0 aTowards Improving Skin Cancer Diagnosis by Integrating Microarra c2020 07 a2119-21300 v243 aMany clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94% and 80%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.
10aBiomarkers, Tumor10aComputational Biology10aDiagnosis, Computer-Assisted10aGene Expression Profiling10aHumans10aMachine Learning10aRNA-seq10aSkin Neoplasms1 aGalvez, Juan, M1 aCastillo-Secilla, Daniel1 aHerrera, Luis, J1 aValenzuela, Olga1 aCaba, Octavio1 aPrados, Jose, C1 aOrtuno, Francisco, M1 aRojas, Ignacio uhttps://www.clinbioinfosspa.es/content/towards-improving-skin-cancer-diagnosis-integrating-microarray-and-rna-seq-datasets01363nas a2200433 4500008004100000022001400041245006500055210006400120260001200184300001200196490000800208653001500216653002800231653003100259100002600290700002800316700001700344700002600361700001800387700001300405700002000418700002100438700002000459700002100479700002200500700002400522700002100546700002200567700002000589700001800609700001900627700002300646700001900669700002500688700002200713700002300735710007100758856010000829 2020 eng d a1476-468700aTransparency and reproducibility in artificial intelligence.0 aTransparency and reproducibility in artificial intelligence c2020 10 aE14-E160 v58610aAlgorithms10aArtificial Intelligence10aReproducibility of Results1 aHaibe-Kains, Benjamin1 aAdam, George, Alexandru1 aHosny, Ahmed1 aKhodakarami, Farnoosh1 aWaldron, Levi1 aWang, Bo1 aMcIntosh, Chris1 aGoldenberg, Anna1 aKundaje, Anshul1 aGreene, Casey, S1 aBroderick, Tamara1 aHoffman, Michael, M1 aLeek, Jeffrey, T1 aKorthauer, Keegan1 aHuber, Wolfgang1 aBrazma, Alvis1 aPineau, Joelle1 aTibshirani, Robert1 aHastie, Trevor1 aIoannidis, John, P A1 aQuackenbush, John1 aAerts, Hugo, J W L1 aMassive Analysis Quality Control (MAQC) Society Board of Directors uhttps://www.clinbioinfosspa.es/content/transparency-and-reproducibility-artificial-intelligence01818nas a2200265 4500008004100000022001400041245007700055210006900132260001500201300001400216490000700230520098400237653001501221653001101236653002301247653002401270653002001294653001801314100001901332700002201351700001801373700003101391700002001422856011001442 2019 eng d a1477-405400aA comparison of mechanistic signaling pathway activity analysis methods.0 acomparison of mechanistic signaling pathway activity analysis me c2019 09 27 a1655-16680 v203 aUnderstanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.
10aAlgorithms10aHumans10aPostmortem Changes10aSignal Transduction10aSystems biology10aTranscriptome1 aAmadoz, Alicia1 aHidalgo, Marta, R1 aCubuk, Cankut1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/comparison-mechanistic-signaling-pathway-activity-analysis-methods02824nas a2200397 4500008004100000022001400041245011600055210006900171260000900240300000600249490000600255520157600261653002601837653002401863653001901887653002901906653001101935653001301946653003601959653002301995653001402018653001402032653001302046653001802059100001802077700002202095700001902117700001602136700002402152700002202176700002102198700002002219700003102239700002002270856013602290 2019 eng d a2056-718900aDifferential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.0 aDifferential metabolic activity and discovery of therapeutic tar c2019 a70 v53 aIn spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.
10aComputational Biology10aComputer Simulation10aDrug discovery10aGene Regulatory Networks10aHumans10aInternet10aMetabolic Networks and Pathways10aModels, Biological10aNeoplasms10aPhenotype10aSoftware10aTranscriptome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aRian, Kinza1 aSalavert, Francisco1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/differential-metabolic-activity-and-discovery-therapeutic-targets-using-summarized-metabolic05186nas a2200661 4500008004100000022001400041245013800055210006900193260001200262300001200274490000700286520316900293653001003462653000903472653002803481653002603509653001103535653001103546653001403557653000903571653001603580653002603596653001603622653004903638653002603687653002803713653001703741653002203758100002503780700002403805700002503829700002003854700002103874700002203895700002103917700002203938700002303960700002003983700002404003700002204027700002204049700001804071700001904089700003004108700002904138700002304167700001804190700002904208700002404237700001704261700001504278700002004293700002704313700001804340700002004358700001904378856012704397 2019 eng d a1474-548800aPazopanib for treatment of advanced malignant and dedifferentiated solitary fibrous tumour: a multicentre, single-arm, phase 2 trial.0 aPazopanib for treatment of advanced malignant and dedifferentiat c2019 01 a134-1440 v203 aBACKGROUND: A solitary fibrous tumour is a rare soft-tissue tumour with three clinicopathological variants: typical, malignant, and dedifferentiated. Preclinical experiments and retrospective studies have shown different sensitivities of solitary fibrous tumour to chemotherapy and antiangiogenics. We therefore designed a trial to assess the activity of pazopanib in a cohort of patients with malignant or dedifferentiated solitary fibrous tumour. The clinical and translational results are presented here.
METHODS: In this single-arm, phase 2 trial, adult patients (aged ≥ 18 years) with histologically confirmed metastatic or unresectable malignant or dedifferentiated solitary fibrous tumour at any location, who had progressed (by RECIST and Choi criteria) in the previous 6 months and had an ECOG performance status of 0-2, were enrolled at 16 third-level hospitals with expertise in sarcoma care in Spain, Italy, and France. Patients received pazopanib 800 mg once daily, taken orally without food, at least 1 h before or 2 h after a meal, until progression or intolerance. The primary endpoint of the study was overall response measured by Choi criteria in the subset of the intention-to-treat population (patients who received at least 1 month of treatment with at least one radiological assessment). All patients who received at least one dose of the study drug were included in the safety analyses. This study is registered with ClinicalTrials.gov, number NCT02066285, and with the European Clinical Trials Database, EudraCT number 2013-005456-15.
FINDINGS: From June 26, 2014, to Nov 24, 2016, of 40 patients assessed, 36 were enrolled (34 with malignant solitary fibrous tumour and two with dedifferentiated solitary fibrous tumour). Median follow-up was 27 months (IQR 16-31). Based on central radiology review, 18 (51%) of 35 evaluable patients had partial responses, nine (26%) had stable disease, and eight (23%) had progressive disease according to Choi criteria. Further enrolment of patients with dedifferentiated solitary fibrous tumour was stopped after detection of early and fast progressions in a planned interim analysis. 51% (95% CI 34-69) of 35 patients achieved an overall response according to Choi criteria. Ten (29%) of 35 patients died. There were no deaths related to adverse events and the most frequent grade 3 or higher adverse events were hypertension (11 [31%] of 36 patients), neutropenia (four [11%]), increased concentrations of alanine aminotransferase (four [11%]), and increased concentrations of bilirubin (three [8%]).
INTERPRETATION: To our knowledge, this is the first trial of pazopanib for treatment of malignant solitary fibrous tumour showing activity in this patient group. The manageable toxicity profile and the activity shown by pazopanib suggests that this drug could be an option for systemic treatment of advanced malignant solitary fibrous tumour, and provides a benchmark for future trials.
FUNDING: Spanish Group for Research on Sarcomas (GEIS), Italian Sarcoma Group (ISG), French Sarcoma Group (FSG), GlaxoSmithKline, and Novartis.
10aAdult10aAged10aAngiogenesis Inhibitors10aAntineoplastic Agents10aFemale10aHumans10aIndazoles10aMale10aMiddle Aged10aMultivariate Analysis10aPyrimidines10aResponse Evaluation Criteria in Solid Tumors10aSoft Tissue Neoplasms10aSolitary Fibrous Tumors10aSulfonamides10aSurvival Analysis1 aMartin-Broto, Javier1 aStacchiotti, Silvia1 aLopez-Pousa, Antonio1 aRedondo, Andres1 aBernabeu, Daniel1 ade Alava, Enrique1 aCasali, Paolo, G1 aItaliano, Antoine1 aGutierrez, Antonio1 aMoura, David, S1 aPeña-Chilet, Maria1 aDiaz-Martin, Juan1 aBiscuola, Michele1 aTaron, Miguel1 aCollini, Paola1 aRanchere-Vince, Dominique1 aDel Muro, Xavier, Garcia1 aGrignani, Giovanni1 aDumont, Sarah1 aMartinez-Trufero, Javier1 aPalmerini, Emanuela1 aHindi, Nadia1 aSebio, Ana1 aDopazo, Joaquin1 aTos, Angelo, Paolo Dei1 aLeCesne, Axel1 aBlay, Jean-Yves1 aCruz, Josefina uhttps://www.clinbioinfosspa.es/content/pazopanib-treatment-advanced-malignant-and-dedifferentiated-solitary-fibrous-tumour00765nas a2200181 4500008004100000245009000041210006900131260001600200490000600216100002400222700002700246700002200273700001600295700002300311700002000334700002000354856020900374 2019 eng d00aUsing mechanistic models for the clinical interpretation of complex genomic variation0 aUsing mechanistic models for the clinical interpretation of comp cJan-12-20190 v91 aPeña-Chilet, Maria1 aEsteban-Medina, Marina1 aFalco, Matias, M.1 aRian, Kinza1 aHidalgo, Marta, R.1 aLoucera, Carlos1 aDopazo, Joaquin uhttp://www.nature.com/articles/s41598-019-55454-7http://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-701484nas a2200421 4500008004100000245011900041210006900160260001600229490000600245110005300251700001800304700001800322700002300340700002500363700001600388700001900404700001500423700001800438700001900456700002400475700001900499700002200518700001600540700003100556700001800587700001800605700001800623700001900641700002200660700002300682700002700705700002700732700001900759700002400778700002500802700002600827856020900853 2018 eng d00aA crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection0 acrowdsourced analysis to identify ab initio molecular signatures cJan-12-20180 v91 aThe Respiratory Viral DREAM Challenge Consortium1 aFourati, Slim1 aTalla, Aarthi1 aMahmoudian, Mehrad1 aBurkhart, Joshua, G.1 aKlén, Riku1 aHenao, Ricardo1 aYu, Thomas1 aAydın, Zafer1 aYeung, Ka, Yee1 aAhsen, Mehmet, Eren1 aAlmugbel, Reem1 aJahandideh, Samad1 aLiang, Xiao1 aNordling, Torbjörn, E. M.1 aShiga, Motoki1 aStanescu, Ana1 aVogel, Robert1 aPandey, Gaurav1 aChiu, Christopher1 aMcClain, Micah, T.1 aWoods, Christopher, W.1 aGinsburg, Geoffrey, S.1 aElo, Laura, L.1 aTsalik, Ephraim, L.1 aMangravite, Lara, M.1 aSieberts, Solveig, K. uhttp://www.nature.com/articles/s41467-018-06735-8http://www.nature.com/articles/s41467-018-06735-8.pdfhttp://www.nature.com/articles/s41467-018-06735-8.pdfhttp://www.nature.com/articles/s41467-018-06735-802577nas a2200529 4500008004100000022001400041245008700055210006900142260001500211300000800226490000600234520103000240653001001270653001801280653001001298653001101308653002001319653001101339653002301350653002301373653001901396653002501415653001801440100002301458700002701481700002101508700002501529700001601554700001901570700001701589700002201606700002401628700003101652700002101683700002401704700001901728700002401747700001801771700001801789700002101807700002001828700002001848700002101868700002301889700002001912856011501932 2018 eng d a2041-172300aThe effects of death and post-mortem cold ischemia on human tissue transcriptomes.0 aeffects of death and postmortem cold ischemia on human tissue tr c2018 02 13 a4900 v93 aPost-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.
10aBlood10aCold Ischemia10aDeath10aFemale10agene expression10aHumans10aModels, Biological10aPostmortem Changes10aRNA, Messenger10aStochastic Processes10aTranscriptome1 aFerreira, Pedro, G1 aMuñoz-Aguirre, Manuel1 aReverter, Ferran1 aGodinho, Caio, P Sá1 aSousa, Abel1 aAmadoz, Alicia1 aSodaei, Reza1 aHidalgo, Marta, R1 aPervouchine, Dmitri1 aCarbonell-Caballero, José1 aNurtdinov, Ramil1 aBreschi, Alessandra1 aAmador, Raziel1 aOliveira, Patrícia1 aCubuk, Cankut1 aCurado, João1 aAguet, François1 aOliveira, Carla1 aDopazo, Joaquin1 aSammeth, Michael1 aArdlie, Kristin, G1 aGuigó, Roderic uhttps://www.clinbioinfosspa.es/content/effects-death-and-post-mortem-cold-ischemia-human-tissue-transcriptomes02787nas a2200349 4500008004100000022001400041245008700055210006900142260001200211490000600223520171300229653001201942653003001954653001101984653002201995653001302017653001102030653000902041653001002050653002702060100003302087700002902120700002002149700003002169700002102199700001902220700002002239700001902259700001902278700002202297856011802319 2018 eng d a2057-585800aThe first complete genomic structure of Butyrivibrio fibrisolvens and its chromid.0 afirst complete genomic structure of Butyrivibrio fibrisolvens an c2018 100 v43 aButyrivibrio fibrisolvens forms part of the gastrointestinal microbiome of ruminants and other mammals, including humans. Indeed, it is one of the most common bacteria found in the rumen and plays an important role in ruminal fermentation of polysaccharides, yet, to date, there is no closed reference genome published for this species in any ruminant animal. We successfully assembled the nearly complete genome sequence of B. fibrisolvens strain INBov1 isolated from cow rumen using Illumina paired-end reads, 454 Roche single-end and mate pair sequencing technology. Additionally, we constructed an optical restriction map of this strain to aid in scaffold ordering and positioning, and completed the first genomic structure of this species. Moreover, we identified and assembled the first chromid of this species (pINBov266). The INBov1 genome encodes a large set of genes involved in the cellulolytic process but lacks key genes. This seems to indicate that B. fibrisolvens plays an important role in ruminal cellulolytic processes, but does not have autonomous cellulolytic capacity. When searching for genes involved in the biohydrogenation of unsaturated fatty acids, no linoleate isomerase gene was found in this strain. INBov1 does encode oleate hydratase genes known to participate in the hydrogenation of oleic acids. Furthermore, INBov1 contains an enolase gene, which has been recently determined to participate in the synthesis of conjugated linoleic acids. This work confirms the presence of a novel chromid in B. fibrisolvens and provides a new potential reference genome sequence for this species, providing new insight into its role in biohydrogenation and carbohydrate degradation.
10aAnimals10aButyrivibrio fibrisolvens10aCattle10aGenome, Bacterial10aGenomics10aHumans10aMilk10aRumen10aSequence Analysis, DNA1 aHernáez, Javier, Rodríguez1 aCucchi, Maria, Esperanza1 aCravero, Silvio1 aMartinez, Maria, Carolina1 aGonzalez, Sergio1 aPuebla, Andrea1 aDopazo, Joaquin1 aFarber, Marisa1 aPaniego, Norma1 aRivarola, Máximo uhttps://www.clinbioinfosspa.es/content/first-complete-genomic-structure-butyrivibrio-fibrisolvens-and-its-chromid02950nas a2200445 4500008004100000022001400041245009500055210006900150260001500219300001400234490000700248520156700255653002101822653002101843653002401864653003001888653004301918653002901961653001101990653002602001653001502027653001302042653001402055653001402069653001402083653001402097653002702111653002702138653001802165653002202183100001802205700002202223700001902245700002202264700002102286700002002307700003102327700002002358856012602378 2018 eng d a1538-744500aGene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.0 aGene Expression Integration into Pathway Modules Reveals a PanCa c2018 11 01 a6059-60720 v783 aMetabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .
10aCell Line, Tumor10aCluster Analysis10aDisease Progression10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aGene Regulatory Networks10aHumans10aKaplan-Meier Estimate10aMetabolome10amutation10aNeoplasms10aOncogenes10aPhenotype10aPrognosis10aRNA, Small Interfering10aSequence Analysis, RNA10aTranscriptome10aTreatment Outcome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/gene-expression-integration-pathway-modules-reveals-pan-cancer-metabolic-landscape03417nas a2200793 4500008004100000022001400041245009300055210006900148260001500217300000900232490000600241520096600247653001201213653001401225653002301239653001801262653003601280653003001316653001101346653003101357653001101388653002401399653002101423653001201444653002801456653002501484653004101509653001601550653000901566653000901575653002301584653001501607653003901622653001701661653003001678653003401708100002801742700002201770700003201792700002901824700002601853700002601879700003101905700003301936700002201969700003101991700001902022700002002041700002002061700002202081700002002103700002302123700001602146700002302162700002302185700002302208700001902231700002502250700002902275700002102304700002502325700003202350700001602382700001802398700003802416700001702454700002402471856012802495 2018 eng d a2041-172300aLRH-1 agonism favours an immune-islet dialogue which protects against diabetes mellitus.0 aLRH1 agonism favours an immuneislet dialogue which protects agai c2018 04 16 a14880 v93 aType 1 diabetes mellitus (T1DM) is due to the selective destruction of islet beta cells by immune cells. Current therapies focused on repressing the immune attack or stimulating beta cell regeneration still have limited clinical efficacy. Therefore, it is timely to identify innovative targets to dampen the immune process, while promoting beta cell survival and function. Liver receptor homologue-1 (LRH-1) is a nuclear receptor that represses inflammation in digestive organs, and protects pancreatic islets against apoptosis. Here, we show that BL001, a small LRH-1 agonist, impedes hyperglycemia progression and the immune-dependent inflammation of pancreas in murine models of T1DM, and beta cell apoptosis in islets of type 2 diabetic patients, while increasing beta cell mass and insulin secretion. Thus, we suggest that LRH-1 agonism favors a dialogue between immune and islet cells, which could be druggable to protect against diabetes mellitus.
10aAnimals10aApoptosis10aCell Communication10aCell Survival10aDiabetes Mellitus, Experimental10aDiabetes Mellitus, Type 210aFemale10aGene Expression Regulation10aHumans10aHypoglycemic Agents10aImmunity, Innate10ainsulin10aInsulin-Secreting Cells10aIslets of Langerhans10aIslets of Langerhans Transplantation10aMacrophages10aMale10aMice10aMice, Inbred C57BL10aPhenalenes10aReceptors, Cytoplasmic and Nuclear10aStreptozocin10aT-Lymphocytes, Regulatory10aTransplantation, Heterologous1 aCobo-Vuilleumier, Nadia1 aLorenzo, Petra, I1 aRodríguez, Noelia, García1 aGómez, Irene, de Gracia1 aFuente-Martin, Esther1 aLópez-Noriega, Livia1 aMellado-Gil, José, Manuel1 aRomero-Zerbo, Silvana-Yanina1 aBaquié, Mathurin1 aLachaud, Christian, Claude1 aStifter, Katja1 aPerdomo, German1 aBugliani, Marco1 aDe Tata, Vincenzo1 aBosco, Domenico1 aParnaud, Geraldine1 aPozo, David1 aHmadcha, Abdelkrim1 aFlorido, Javier, P1 aToscano, Miguel, G1 ade Haan, Peter1 aSchoonjans, Kristina1 aPalazón, Luis, Sánchez1 aMarchetti, Piero1 aSchirmbeck, Reinhold1 aMartín-Montalvo, Alejandro1 aMeda, Paolo1 aSoria, Bernat1 aBermúdez-Silva, Francisco-Javier1 aSt-Onge, Luc1 aGauthier, Benoit, R uhttps://www.clinbioinfosspa.es/content/lrh-1-agonism-favours-immune-islet-dialogue-which-protects-against-diabetes-mellitus02239nas a2200277 4500008004100000022001400041245011400055210006900169260001500238300000700253490000700260520126600267653002601533653004301559653001101602653004201613653002401655653001801679653002401697100002201721700001901743700001801762700003101780700002001811856013001831 2018 eng d a1745-615000aModels of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.0 aModels of cell signaling uncover molecular mechanisms of highris c2018 08 22 a160 v133 aBACKGROUND: Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.
RESULTS: Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.
CONCLUSION: We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.
REVIEWERS: This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.
10aComputational Biology10aGene Expression Regulation, Neoplastic10aHumans10aJNK Mitogen-Activated Protein Kinases10aModels, Theoretical10aNeuroblastoma10aSignal Transduction1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aCubuk, Cankut1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/models-cell-signaling-uncover-molecular-mechanisms-high-risk-neuroblastoma-and-predict02954nas a2200541 4500008004100000022001400041245008900055210006900144260000900213300001300222490000700235520138200242653001001624653000901634653001801643653001101661653002901672653003201701653002601733653001101759653001401770653002901784653000901813653001601822653001301838653002201851653001801873653001401891653002701905100002101932700003101953700002001984700002402004700002402028700002602052700001602078700001902094700001902113700002502132700002002157700001902177700002002196700002002216700002402236700002002260700001902280856011302299 2018 eng d a1932-620300aThe modular network structure of the mutational landscape of Acute Myeloid Leukemia.0 amodular network structure of the mutational landscape of Acute M c2018 ae02029260 v133 aAcute myeloid leukemia (AML) is associated with the sequential accumulation of acquired genetic alterations. Although at diagnosis cytogenetic alterations are frequent in AML, roughly 50% of patients present an apparently normal karyotype (NK), leading to a highly heterogeneous prognosis. Due to this significant heterogeneity, it has been suggested that different molecular mechanisms may trigger the disease with diverse prognostic implications. We performed whole-exome sequencing (WES) of tumor-normal matched samples of de novo AML-NK patients lacking mutations in NPM1, CEBPA or FLT3-ITD to identify new gene mutations with potential prognostic and therapeutic relevance to patients with AML. Novel candidate-genes, together with others previously described, were targeted resequenced in an independent cohort of 100 de novo AML patients classified in the cytogenetic intermediate-risk (IR) category. A mean of 4.89 mutations per sample were detected in 73 genes, 35 of which were mutated in more than one patient. After a network enrichment analysis, we defined a single in silico model and established a set of seed-genes that may trigger leukemogenesis in patients with normal karyotype. The high heterogeneity of gene mutations observed in AML patients suggested that a specific alteration could not be as essential as the interaction of deregulated pathways.
10aAdult10aAged10aCytodiagnosis10aFemale10aGene Regulatory Networks10aGenetic Association Studies10aGenetic Heterogeneity10aHumans10aKaryotype10aLeukemia, Myeloid, Acute10aMale10aMiddle Aged10amutation10aNeoplasm Proteins10aNucleophosmin10aPrognosis10awhole exome sequencing1 aIbáñez, Mariam1 aCarbonell-Caballero, José1 aSuch, Esperanza1 aGarcía-Alonso, Luz1 aLiquori, Alessandro1 aLópez-Pavía, María1 aLLop, Marta1 aAlonso, Carmen1 aBarragán, Eva1 aGómez-Seguí, Inés1 aNeef, Alexander1 aHervás, David1 aMontesinos, Pau1 aSanz, Guillermo1 aSanz, Miguel, Angel1 aDopazo, Joaquin1 aCervera, José uhttps://www.clinbioinfosspa.es/content/modular-network-structure-mutational-landscape-acute-myeloid-leukemia02298nas a2200361 4500008004100000022001400041245004600055210004400101260001500145300001400160490000700174520132500181653002201506653001801528653001101546653001301557653001301570653002801583100001801611700001701629700002101646700002301667700002301690700002201713700001601735700001701751700001901768700001801787700002001805700002001825700002001845856007101865 2017 eng d a1362-496200aHGVA: the Human Genome Variation Archive.0 aHGVA the Human Genome Variation Archive c2017 07 03 aW189-W1940 v453 aHigh-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic data for key reference projects in a clean, fast and integrated fashion. HGVA provides an efficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libraries in Python, Java and JavaScript for fast programmatic access to its knowledge base. HGVA calculates population frequencies for these projects and enriches their data with variant annotation provided by CellBase, a rich and fast annotation solution. HGVA serves as a proof-of-concept of the genome analysis developments being carried out by the University of Cambridge together with UK's 100 000 genomes project and the National Institute for Health Research BioResource Rare-Diseases, in particular, deploying open-source for Computational Biology (OpenCB) software platform for storing and analyzing massive genomic datasets.
10aGenetic Variation10aGenome, Human10aHumans10aInternet10aSoftware10aUser-Computer Interface1 aLopez, Javier1 aColl, Jacobo1 aHaimel, Matthias1 aKandasamy, Swaathi1 aTárraga, Joaquín1 aFurio-Tari, Pedro1 aBari, Wasim1 aBleda, Marta1 aRueda, Antonio1 aGräf, Stefan1 aRendon, Augusto1 aDopazo, Joaquin1 aMedina, Ignacio uhttps://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx44502048nas a2200313 4500008004100000022001400041245012900055210006900184260001600253300001400269490000600283520099600289653002601285653002001311653002901331653001101360653001301371653001401384653002301398653002701421653002401448100002201472700001801494700001901512700002401531700003101555700002001586856012801606 2017 eng d a1949-255300aHigh throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.0 aHigh throughput estimation of functional cell activities reveals c2017 Jan 17 a5160-51780 v83 aUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.
10aComputational Biology10agene expression10aGene Regulatory Networks10aHumans10amutation10aNeoplasms10aPrecision Medicine10aSequence Analysis, RNA10aSignal Transduction1 aHidalgo, Marta, R1 aCubuk, Cankut1 aAmadoz, Alicia1 aSalavert, Francisco1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/high-throughput-estimation-functional-cell-activities-reveals-disease-mechanisms-and03013nas a2200433 4500008004100000022001400041245016000055210006900215260001300284300001200297490000700309520160000316653001601916653003801932653001501970653001701985653001902002653002702021653001502048653002602063653002602089653001002115100002402125700002402149700001902173700002002192700002202212700002102234700002202255700002902277700002002306700001802326700002002344700002402364700001902388700002102407700001902428856013202447 2017 eng d a1573-502800aIntegration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.).0 aIntegration of transcriptomic and metabolic data reveals hub tra c2017 Jul a549-5640 v943 aBy integration of transcriptional and metabolic profiles we identified pathways and hubs transcription factors regulated during drought conditions in sunflower, useful for applications in molecular and/or biotechnological breeding. Drought is one of the most important environmental stresses that effects crop productivity in many agricultural regions. Sunflower is tolerant to drought conditions but the mechanisms involved in this tolerance remain unclear at the molecular level. The aim of this study was to characterize and integrate transcriptional and metabolic pathways related to drought stress in sunflower plants, by using a system biology approach. Our results showed a delay in plant senescence with an increase in the expression level of photosynthesis related genes as well as higher levels of sugars, osmoprotectant amino acids and ionic nutrients under drought conditions. In addition, we identified transcription factors that were upregulated during drought conditions and that may act as hubs in the transcriptional network. Many of these transcription factors belong to families implicated in the drought response in model species. The integration of transcriptomic and metabolomic data in this study, together with physiological measurements, has improved our understanding of the biological responses during droughts and contributes to elucidate the molecular mechanisms involved under this environmental condition. These findings will provide useful biotechnological tools to improve stress tolerance while maintaining crop yield under restricted water availability.
10aChlorophyll10aGene Expression Regulation, Plant10aHelianthus10aPlant Leaves10aPlant Proteins10aProtein Array Analysis10aRNA, Plant10aStress, Physiological10aTranscription Factors10aWater1 aMoschen, Sebastián1 aDi Rienzo, Julio, A1 aHiggins, Janet1 aTohge, Takayuki1 aWatanabe, Mutsumi1 aGonzalez, Sergio1 aRivarola, Máximo1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aHopp, Esteban1 aHoefgen, Rainer1 aFernie, Alisdair, R1 aPaniego, Norma1 aFernandez, Paula1 aHeinz, Ruth, A uhttps://www.clinbioinfosspa.es/content/integration-transcriptomic-and-metabolic-data-reveals-hub-transcription-factors-involved02685nas a2200349 4500008004100000022001400041245011500055210006900170260001600239300001400255490000700269520162000276653001201896653002201908653001101930653001301941653001301954653001101967653002401978653002002002653003102022653001302053100003102066700001902097700002002116700002202136700001802158700001802176700002802194700002002222856009302242 2017 eng d a1367-481100aReference genome assessment from a population scale perspective: an accurate profile of variability and noise.0 aReference genome assessment from a population scale perspective c2017 Nov 15 a3511-35170 v333 aMotivation: Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome.
Results: The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples.
Availability and implementation: This tool is freely available at http://gitlab.com/carbonell/ces.
Contact: jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es.
Supplementary information: Supplementary data are available at Bioinformatics online.
10aAnimals10aGenetic Variation10aGenome10aGenomics10aGenotype10aHumans10aModels, Statistical10aQuality Control10aReproducibility of Results10aSoftware1 aCarbonell-Caballero, José1 aAmadoz, Alicia1 aAlonso, Roberto1 aHidalgo, Marta, R1 aCubuk, Cankut1 aConesa, David1 aLópez-Quílez, Antonio1 aDopazo, Joaquin uhttps://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx48203542nas a2200649 4500008004100000022001400041245010900055210006900164260001600233300000700249490000700256520162800263653001601891653001701907653000801924100001901932700002001951700002201971700002501993700002502018700002202043700001702065700002102082700002502103700001902128700002202147700002002169700002102189700001902210700001802229700001802247700002202265700002302287700001802310700002002328700001602348700002602364700001702390700002202407700001702429700002202446700002102468700003202489700002802521700002302549700002902572700002502601700001802626700001902644700002502663700002602688700002302714700001902737700003402756700002402790856007802814 2017 eng d a1474-760X00aWhole exome sequencing coupled with unbiased functional analysis reveals new Hirschsprung disease genes.0 aWhole exome sequencing coupled with unbiased functional analysis c2017 Mar 08 a480 v183 aBACKGROUND: Hirschsprung disease (HSCR), which is congenital obstruction of the bowel, results from a failure of enteric nervous system (ENS) progenitors to migrate, proliferate, differentiate, or survive within the distal intestine. Previous studies that have searched for genes underlying HSCR have focused on ENS-related pathways and genes not fitting the current knowledge have thus often been ignored. We identify and validate novel HSCR genes using whole exome sequencing (WES), burden tests, in silico prediction, unbiased in vivo analyses of the mutated genes in zebrafish, and expression analyses in zebrafish, mouse, and human. RESULTS: We performed de novo mutation (DNM) screening on 24 HSCR trios. We identify 28 DNMs in 21 different genes. Eight of the DNMs we identified occur in RET, the main HSCR gene, and the remaining 20 DNMs reside in genes not reported in the ENS. Knockdown of all 12 genes with missense or loss-of-function DNMs showed that the orthologs of four genes (DENND3, NCLN, NUP98, and TBATA) are indispensable for ENS development in zebrafish, and these results were confirmed by CRISPR knockout. These genes are also expressed in human and mouse gut and/or ENS progenitors. Importantly, the encoded proteins are linked to neuronal processes shared by the central nervous system and the ENS. CONCLUSIONS: Our data open new fields of investigation into HSCR pathology and provide novel insights into the development of the ENS. Moreover, the study demonstrates that functional analyses of genes carrying DNMs are warranted to delineate the full genetic architecture of rare complex diseases.10aHirschprung10aRare Disease10aWES1 aGui, Hongsheng1 aSchriemer, Duco1 aCheng, William, W1 aChauhan, Rajendra, K1 aAntiňolo, Guillermo1 aBerrios, Courtney1 aBleda, Marta1 aBrooks, Alice, S1 aBrouwer, Rutger, W W1 aBurns, Alan, J1 aCherny, Stacey, S1 aDopazo, Joaquin1 aEggen, Bart, J L1 aGriseri, Paola1 aJalloh, Binta1 aLe, Thuy-Linh1 aLui, Vincent, C H1 aLuzón-Toro, Berta1 aMatera, Ivana1 aNgan, Elly, S W1 aPelet, Anna1 aRuiz-Ferrer, Macarena1 aSham, Pak, C1 aShepherd, Iain, T1 aSo, Man-Ting1 aSribudiani, Yunia1 aTang, Clara, S M1 avan den Hout, Mirjam, C G N1 avan der Linde, Herma, C1 avan Ham, Tjakko, J1 avan IJcken, Wilfred, F J1 aVerheij, Joke, B G M1 aAmiel, Jeanne1 aBorrego, Salud1 aCeccherini, Isabella1 aChakravarti, Aravinda1 aLyonnet, Stanislas1 aTam, Paul, K H1 aGarcia-Barceló, Maria-Mercè1 aHofstra, Robert, Mw uhttp://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1174-601877nas a2200577 4500008004100000245010800041210006900149260001600218490000700234100001900241700002000260700002300280700002600303700002500329700002200354700001700376700002200393700002700415700002000442700002300462700002000485700002300505700001900528700001800547700001800565700002400583700002300607700001800630700002200648700001600670700002600686700001800712700002300730700001700753700002200770700002300792700003500815700002900850700002400879700003100903700002800934700001800962700001900980700002500999700002601024700002301050700002101073700003401094700002701128856014401155 2017 eng d00aWhole exome sequencing coupled with unbiased functional analysis reveals new Hirschsprung disease genes0 aWhole exome sequencing coupled with unbiased functional analysis cJan-12-20170 v181 aGui, Hongsheng1 aSchriemer, Duco1 aCheng, William, W.1 aChauhan, Rajendra, K.1 aAntiňolo, Guillermo1 aBerrios, Courtney1 aBleda, Marta1 aBrooks, Alice, S.1 aBrouwer, Rutger, W. W.1 aBurns, Alan, J.1 aCherny, Stacey, S.1 aDopazo, Joaquin1 aEggen, Bart, J. L.1 aGriseri, Paola1 aJalloh, Binta1 aLe, Thuy-Linh1 aLui, Vincent, C. H.1 aLuzón-Toro, Berta1 aMatera, Ivana1 aNgan, Elly, S. W.1 aPelet, Anna1 aRuiz-Ferrer, Macarena1 aSham, Pak, C.1 aShepherd, Iain, T.1 aSo, Man-Ting1 aSribudiani, Yunia1 aTang, Clara, S. M.1 avan den Hout, Mirjam, C. G. N.1 avan der Linde, Herma, C.1 avan Ham, Tjakko, J.1 avan IJcken, Wilfred, F. J.1 aVerheij, Joke, B. G. M.1 aAmiel, Jeanne1 aBorrego, Salud1 aCeccherini, Isabella1 aChakravarti, Aravinda1 aLyonnet, Stanislas1 aTam, Paul, K. H.1 aGarcia-Barceló, Maria-Mercè1 aHofstra, Robert, M. W. uhttp://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1174-6http://link.springer.com/content/pdf/10.1186/s13059-017-1174-6.pdf01971nas a2200301 4500008004100000022001400041245010200055210006900157260001500226520100600241653002101247653002201268653002601290653001901316653002101335653002601356653001501382653002401397100002401421700002101445700001901466700001801485700002001503700001901523700003101542700002101573856007501594 2016 eng d a1362-496200aActionable pathways: interactive discovery of therapeutic targets using signaling pathway models.0 aActionable pathways interactive discovery of therapeutic targets c2016 May 23 aThe discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.10aactionable genes10aDisease mechanism10adrug action mechanism10aDrug discovery10apathway analysis10apersonalized medicine10asignalling10atherapeutic targets1 aSalavert, Francisco1 aHidago, Marta, R1 aAmadoz, Alicia1 aCubuk, Cankut1 aMedina, Ignacio1 aCrespo, Daniel1 aCarbonell-Caballero, José1 aDopazo, Joaquín uhttp://nar.oxfordjournals.org/content/early/2016/05/02/nar.gkw369.full01158nas a2200313 4500008004100000245011100041210006900152260001600221490000600237100002000243700003400263700002700297700002700324700002200351700002400373700002500397700002100422700001900443700002000462700002300482700002200505700001900527700003300546700002000579700002300599700002000622700002100642856018100663 2016 eng d00aExtension of human lncRNA transcripts by RACE coupled with long-read high-throughput sequencing (RACE-Seq)0 aExtension of human lncRNA transcripts by RACE coupled with longr cJan-11-20160 v71 aLagarde, Julien1 aUszczynska-Ratajczak, Barbara1 aSantoyo-López, Javier1 aGonzalez, Jose, Manuel1 aTapanari, Electra1 aMudge, Jonathan, M.1 aSteward, Charles, A.1 aWilming, Laurens1 aTanzer, Andrea1 aHowald, Cédric1 aChrast, Jacqueline1 aVela-Boza, Alicia1 aRueda, Antonio1 aLopez-Domingo, Francisco, J.1 aDopazo, Joaquin1 aReymond, Alexandre1 aGuigó, Roderic1 aHarrow, Jennifer uhttp://www.nature.com/articles/ncomms12339http://www.nature.com/articles/ncomms12339.pdfhttp://www.nature.com/articles/ncomms12339.pdfhttp://www.nature.com/articles/ncomms1233902094nas a2200349 4500008004100000022001400041245011200055210006900167260000900236300001000245490000600255520101800261100002001279700003401299700002701333700002701360700002201387700002301409700002401432700002101456700001901477700002001496700002301516700002201539700001901561700003301580700002001613700002301633700002001656700002101676856004701697 2016 eng d a2041-172300aExtension of human lncRNA transcripts by RACE coupled with long-read high-throughput sequencing (RACE-Seq).0 aExtension of human lncRNA transcripts by RACE coupled with longr c2016 a123390 v73 aLong non-coding RNAs (lncRNAs) constitute a large, yet mostly uncharacterized fraction of the mammalian transcriptome. Such characterization requires a comprehensive, high-quality annotation of their gene structure and boundaries, which is currently lacking. Here we describe RACE-Seq, an experimental workflow designed to address this based on RACE (rapid amplification of cDNA ends) and long-read RNA sequencing. We apply RACE-Seq to 398 human lncRNA genes in seven tissues, leading to the discovery of 2,556 on-target, novel transcripts. About 60% of the targeted loci are extended in either 5’ or 3’, often reaching genomic hallmarks of gene boundaries. Analysis of the novel transcripts suggests that lncRNAs are as long, have as many exons and undergo as much alternative splicing as protein-coding genes, contrary to current assumptions. Overall, we show that RACE-Seq is an effective tool to annotate an organism’s deep transcriptome, and compares favourably to other targeted sequencing techniques.1 aLagarde, Julien1 aUszczynska-Ratajczak, Barbara1 aSantoyo-López, Javier1 aGonzalez, Jose, Manuel1 aTapanari, Electra1 aMudge, Jonathan, M1 aSteward, Charles, A1 aWilming, Laurens1 aTanzer, Andrea1 aHowald, Cédric1 aChrast, Jacqueline1 aVela-Boza, Alicia1 aRueda, Antonio1 aLópez-Domingo, Francisco, J1 aDopazo, Joaquin1 aReymond, Alexandre1 aGuigó, Roderic1 aHarrow, Jennifer uhttp://www.nature.com/articles/ncomms1233901698nas a2200337 4500008004100000022001400041245007000055210006800125260001300193300001100206490000700217520067800224653001300902653004200915653001100957653003200968653002701000653001801027100001401045700001601059700001701075700001801092700001901110700001601129700002301145700002301168700002601191700002501217700001401242856010401256 2016 eng d a1756-166300aHighly sensitive and ultrafast read mapping for RNA-seq analysis.0 aHighly sensitive and ultrafast read mapping for RNAseq analysis c2016 Apr a93-1000 v233 aAs sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available.
10aGenomics10aHigh-Throughput Nucleotide Sequencing10aHumans10aSensitivity and Specificity10aSequence Analysis, RNA10aTranscriptome1 aMedina, I1 aTárraga, J1 aMartínez, H1 aBarrachina, S1 aCastillo, M, I1 aPaschall, J1 aSalavert-Torres, J1 aBlanquer-Espert, I1 aHernández-García, V1 aQuintana-Ortí, E, S1 aDopazo, J uhttps://www.clinbioinfosspa.es/content/highly-sensitive-and-ultrafast-read-mapping-rna-seq-analysis02499nas a2200505 4500008004100000022001400041245008800055210006900143260001600212300000900228490000600237520111400243653001001357653000901367653002201376653002001398653001601418653001101434653001101445653000901456653001601465653003101481653002201512653002601534100002201560700001201582700001301594700001301607700001401620700002301634700001801657700001701675700002301692700002001715700002001735700001401755700001401769700001301783700001601796700001201812700001401824700001601838700001601854856012301870 2016 eng d a2158-318800aHuman DNA methylomes of neurodegenerative diseases show common epigenomic patterns.0 aHuman DNA methylomes of neurodegenerative diseases show common e c2016 Jan 19 ae7180 v63 aDifferent neurodegenerative disorders often show similar lesions, such as the presence of amyloid plaques, TAU-neurotangles and synuclein inclusions. The genetically inherited forms are rare, so we wondered whether shared epigenetic aberrations, such as those affecting DNA methylation, might also exist. The studied samples were gray matter samples from the prefrontal cortex of control and neurodegenerative disease-associated cases. We performed the DNA methylation analyses of Alzheimer's disease, dementia with Lewy bodies, Parkinson's disease and Alzheimer-like neurodegenerative profile associated with Down's syndrome samples. The DNA methylation landscapes obtained show that neurodegenerative diseases share similar aberrant CpG methylation shifts targeting a defined gene set. Our findings suggest that neurodegenerative disorders might have similar pathogenetic mechanisms that subsequently evolve into different clinical entities. The identified aberrant DNA methylation changes can be used as biomarkers of the disorders and as potential new targets for the development of new therapies.
10aAdult10aAged10aAged, 80 and over10aDNA Methylation10aEpigenomics10aFemale10aHumans10aMale10aMiddle Aged10aneurodegenerative diseases10aPrefrontal Cortex10aTissue Array Analysis1 aSanchez-Mut, J, V1 aHeyn, H1 aVidal, E1 aMoran, S1 aSayols, S1 aDelgado-Morales, R1 aSchultz, M, D1 aAnsoleaga, B1 aGarcia-Esparcia, P1 aPons-Espinal, M1 ade Lagran, M, M1 aDopazo, J1 aRabano, A1 aAvila, J1 aDierssen, M1 aLott, I1 aFerrer, I1 aEcker, J, R1 aEsteller, M uhttps://www.clinbioinfosspa.es/content/human-dna-methylomes-neurodegenerative-diseases-show-common-epigenomic-patterns03254nas a2200493 4500008004100000022001400041245010800055210006900163260001300232300001100245490000700256520165600263653004101919653003001960653003801990653001802028653001702046653001502063653000902078653001702087653004402104653001702148653003302165653001902198653002602217100002402243700002602267700002402293700002802317700002002345700002202365700002102387700002102408700002202429700002902451700002002480700002702500700002002527700002402547700001902571700002102590700001902611856013002630 2016 eng d a1467-765200aIntegrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower.0 aIntegrating transcriptomic and metabolomic analysis to understan c2016 Feb a719-340 v143 aLeaf senescence is a complex process, which has dramatic consequences on crop yield. In sunflower, gap between potential and actual yields reveals the economic impact of senescence. Indeed, sunflower plants are incapable of maintaining their green leaf area over sustained periods. This study characterizes the leaf senescence process in sunflower through a systems biology approach integrating transcriptomic and metabolomic analyses: plants being grown under both glasshouse and field conditions. Our results revealed a correspondence between profile changes detected at the molecular, biochemical and physiological level throughout the progression of leaf senescence measured at different plant developmental stages. Early metabolic changes were detected prior to anthesis and before the onset of the first senescence symptoms, with more pronounced changes observed when physiological and molecular variables were assessed under field conditions. During leaf development, photosynthetic activity and cell growth processes decreased, whereas sucrose, fatty acid, nucleotide and amino acid metabolisms increased. Pathways related to nutrient recycling processes were also up-regulated. Members of the NAC, AP2-EREBP, HB, bZIP and MYB transcription factor families showed high expression levels, and their expression level was highly correlated, suggesting their involvement in sunflower senescence. The results of this study thus contribute to the elucidation of the molecular mechanisms involved in the onset and progression of leaf senescence in sunflower leaves as well as to the identification of candidate genes involved in this process.
10aGas Chromatography-Mass Spectrometry10aGene Expression Profiling10aGene Expression Regulation, Plant10aGene ontology10aGenes, Plant10aHelianthus10aIons10ametabolomics10aOligonucleotide Array Sequence Analysis10aPlant Leaves10aPrincipal Component Analysis10aRNA, Messenger10aTranscription Factors1 aMoschen, Sebastián1 aLuoni, Sofía, Bengoa1 aDi Rienzo, Julio, A1 aCaro, María, Del Pilar1 aTohge, Takayuki1 aWatanabe, Mutsumi1 aHollmann, Julien1 aGonzalez, Sergio1 aRivarola, Máximo1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aHopp, Horacio, Esteban1 aHoefgen, Rainer1 aFernie, Alisdair, R1 aPaniego, Norma1 aFernandez, Paula1 aHeinz, Ruth, A uhttps://www.clinbioinfosspa.es/content/integrating-transcriptomic-and-metabolomic-analysis-understand-natural-leaf-senescence02271nas a2200253 4500008004100000022001400041245007800055210006900133260001500202300001000217490000600227520146300233653001601696653001201712653003001724653003101754653001401785100001901799700002901818700002001847700002101867700002301888856010601911 2016 eng d a2045-232200aThe transcriptomics of an experimentally evolved plant-virus interaction.0 atranscriptomics of an experimentally evolved plantvirus interact c2016 04 26 a249010 v63 aModels of plant-virus interaction assume that the ability of a virus to infect a host genotype depends on the matching between virulence and resistance genes. Recently, we evolved tobacco etch potyvirus (TEV) lineages on different ecotypes of Arabidopsis thaliana, and found that some ecotypes selected for specialist viruses whereas others selected for generalists. Here we sought to evaluate the transcriptomic basis of such relationships. We have characterized the transcriptomic responses of five ecotypes infected with the ancestral and evolved viruses. Genes and functional categories differentially expressed by plants infected with local TEV isolates were identified, showing heterogeneous responses among ecotypes, although significant parallelism existed among lineages evolved in the same ecotype. Although genes involved in immune responses were altered upon infection, other functional groups were also pervasively over-represented, suggesting that plant resistance genes were not the only drivers of viral adaptation. Finally, the transcriptomic consequences of infection with the generalist and specialist lineages were compared. Whilst the generalist induced very similar perturbations in the transcriptomes of the different ecotypes, the perturbations induced by the specialist were divergent. Plant defense mechanisms were activated when the infecting virus was specialist but they were down-regulated when infecting with generalist.
10aArabidopsis10aEcotype10aGene Expression Profiling10aHost-Pathogen Interactions10aPotyvirus1 aHillung, Julia1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aCuevas, José, M1 aElena, Santiago, F uhttps://www.clinbioinfosspa.es/content/transcriptomics-experimentally-evolved-plant-virus-interaction02967nas a2200529 4500008004100000022001400041245011800055210006900173260001200242300001400254490000800268520135400276653001501630653001001645653001201655653002701667653002101694653002401715653001001739653002101749653002801770653001001798653001101808653003501819653002201854653004201876653001101918653003301929653001301962653002601975653003702001653002502038653001802063100002202081700001902103700001802122700001902140700001502159700001802174700001902192700001902211700002002230700001802250700002202268700002002290856012702310 2016 eng d a1432-120300aWhole exome sequencing of Rett syndrome-like patients reveals the mutational diversity of the clinical phenotype.0 aWhole exome sequencing of Rett syndromelike patients reveals the c2016 12 a1343-13540 v1353 aClassical Rett syndrome (RTT) is a neurodevelopmental disorder where most of cases carry MECP2 mutations. Atypical RTT variants involve mutations in CDKL5 and FOXG1. However, a subset of RTT patients remains that do not carry any mutation in the described genes. Whole exome sequencing was carried out in a cohort of 21 female probands with clinical features overlapping with those of RTT, but without mutations in the customarily studied genes. Candidates were functionally validated by assessing the appearance of a neurological phenotype in Caenorhabditis elegans upon disruption of the corresponding ortholog gene. We detected pathogenic variants that accounted for the RTT-like phenotype in 14 (66.6 %) patients. Five patients were carriers of mutations in genes already known to be associated with other syndromic neurodevelopmental disorders. We determined that the other patients harbored mutations in genes that have not previously been linked to RTT or other neurodevelopmental syndromes, such as the ankyrin repeat containing protein ANKRD31 or the neuronal acetylcholine receptor subunit alpha-5 (CHRNA5). Furthermore, worm assays demonstrated that mutations in the studied candidate genes caused locomotion defects. Our findings indicate that mutations in a variety of genes contribute to the development of RTT-like phenotypes.
10aAdolescent10aAdult10aAnimals10aCaenorhabditis elegans10aCarrier Proteins10aCell Cycle Proteins10aChild10aChild, Preschool10aDNA Mutational Analysis10aExome10aFemale10aForkhead Transcription Factors10aGenetic Variation10aHigh-Throughput Nucleotide Sequencing10aHumans10aMethyl-CpG-Binding Protein 210amutation10aNerve Tissue Proteins10aProtein Serine-Threonine Kinases10aReceptors, Nicotinic10aRett Syndrome1 aLucariello, Mario1 aVidal, Enrique1 aVidal, Silvia1 aSaez, Mauricio1 aRoa, Laura1 aHuertas, Dori1 aPineda, Mercè1 aDalfó, Esther1 aDopazo, Joaquin1 aJurado, Paola1 aArmstrong, Judith1 aEsteller, Manel uhttps://www.clinbioinfosspa.es/content/whole-exome-sequencing-rett-syndrome-patients-reveals-mutational-diversity-clinical01932nas a2200253 4500008004100000022001400041245011000055210006900165260001600234300001400250490000700264520114400271653000801415653001301423653001501436653002001451100003601471700002401507700003001531700002301561700002001584700002101604856005301625 2015 eng d a1362-496200aAssessing the impact of mutations found in next generation sequencing data over human signaling pathways.0 aAssessing the impact of mutations found in next generation seque c2015 Apr 16 aW270-W2750 v433 aModern sequencing technologies produce increasingly detailed data on genomic variation. However, conventional methods for relating either individual variants or mutated genes to phenotypes present known limitations given the complex, multigenic nature of many diseases or traits. Here we present PATHiVar, a web-based tool that integrates genomic variation data with gene expression tissue information. PATHiVar constitutes a new generation of genomic data analysis methods that allow studying variants found in next generation sequencing experiment in the context of signaling pathways. Simple Boolean models of pathways provide detailed descriptions of the impact of mutations in cell functionality so as, recurrences in functionality failures can easily be related to diseases, even if they are produced by mutations in different genes. Patterns of changes in signal transmission circuits, often unpredictable from individual genes mutated, correspond to patterns of affected functionalities that can be related to complex traits such as disease progression, drug response, etc. PATHiVar is available at: http://pathivar.babelomics.org.10aNGS10apathways10asignalling10aSystems biology1 aHernansaiz-Ballesteros, Rosa, D1 aSalavert, Francisco1 aSebastián-Leon, Patricia1 aAlemán, Alejandro1 aMedina, Ignacio1 aDopazo, Joaquín uhttp://nar.oxfordjournals.org/content/43/W1/W27002445nas a2200469 4500008004100000022001400041245008300055210006900138260001600207300001400223490000700237520108700244653001501331653002101346653002201367653001601389653002101405653000801426653001201434653002001446653002001466100002001486700002401506700002901530700003101559700001701590700002401607700002301631700002201654700002401676700002101700700001801721700002201739700001901761700003601780700002301816700002301839700002001862700002001882700002001902856005301922 2015 eng d a1362-496200aBabelomics 5.0: functional interpretation for new generations of genomic data.0 aBabelomics 50 functional interpretation for new generations of g c2015 Apr 20 aW117-W1210 v433 aBabelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.10ababelomics10adata integration10agene set analysis10ainteractome10anetwork analysis10aNGS10aRNA-seq10aSystems biology10atranscriptomics1 aAlonso, Roberto1 aSalavert, Francisco1 aGarcia-Garcia, Francisco1 aCarbonell-Caballero, José1 aBleda, Marta1 aGarcía-Alonso, Luz1 aSanchis-Juan, Alba1 aPerez-Gil, Daniel1 aMarin-Garcia, Pablo1 aSánchez, Rubén1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aHernansaiz-Ballesteros, Rosa, D1 aAlemán, Alejandro1 aTárraga, Joaquín1 aMontaner, David1 aMedina, Ignacio1 aDopazo, Joaquin uhttp://nar.oxfordjournals.org/content/43/W1/W11703376nas a2200793 4500008004100000022001400041245011500055210006900170260001600239520112300255653001101378653000801389653002001397100001901417700002601436700001201462700001801474700002201492700002701514700002201541700002201563700001901585700002901604700002301633700002001656700002001676700002301696700002501719700002201744700002201766700002101788700004001809700001201849700001701861700001201878700001601890700001901906700001801925700002101943700001601964700002001980700002402000700002002024700001802044700001302062700001702075700001802092700002102110700002402131700002002155700002102175700001702196700002102213700002502234700002102259700001902280700001902299700002102318700001602339700001402355700001702369700001502386700001302401700003102414700002102445700002102466700002002487856007502507 2015 eng d a1548-710500aCombining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.0 aCombining tumor genome simulation with crowdsourcing to benchmar c2015 May 183 aThe detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.10acancer10aNGS10avariant calling1 aEwing, Adam, D1 aHoulahan, Kathleen, E1 aHu, Yin1 aEllrott, Kyle1 aCaloian, Cristian1 aYamaguchi, Takafumi, N1 aBare, Christopher1 aP’ng, Christine1 aWaggott, Daryl1 aSabelnykova, Veronica, Y1 aKellen, Michael, R1 aNorman, Thea, C1 aHaussler, David1 aFriend, Stephen, H1 aStolovitzky, Gustavo1 aMargolin, Adam, A1 aStuart, Joshua, M1 aBoutros, Paul, C1 aparticipants, ICGC-TCGA, DREAM Soma1 aXi, Liu1 aDewal, Ninad1 aFan, Yu1 aWang, Wenyi1 aWheeler, David1 aWilm, Andreas1 aTing, Grace, Hui1 aLi, Chenhao1 aBertrand, Denis1 aNagarajan, Niranjan1 aChen, Qing-Rong1 aHsu, Chih-Hao1 aHu, Ying1 aYan, Chunhua1 aKibbe, Warren1 aMeerzaman, Daoud1 aCibulskis, Kristian1 aRosenberg, Mara1 aBergelson, Louis1 aKiezun, Adam1 aRadenbaugh, Amie1 aSertier, Anne-Sophie1 aFerrari, Anthony1 aTonton, Laurie1 aBhutani, Kunal1 aHansen, Nancy, F1 aWang, Difei1 aSong, Lei1 aLai, Zhongwu1 aLiao, Yang1 aShi, Wei1 aCarbonell-Caballero, José1 aDopazo, Joaquín1 aLau, Cheryl, C K1 aGuinney, Justin uhttp://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3407.html02390nas a2200397 4500008004100000022001400041245007600055210006900131260001300200300001300213490000700226520116000233653001201393653001801405653002201423653002201445653002301467653002401490653002801514653003801542653001101580653002001591653002801611653001301639653002201652653001401674653003601688653003201724653002401756100002401780700002401804700001801828700002001846700001701866856010901883 2015 eng d a1553-735800aA Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces.0 aPanCancer Catalogue of Cancer Driver Protein Interaction Interfa c2015 Oct ae10045180 v113 aDespite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated.
10aAnimals10aBase Sequence10aBiomarkers, Tumor10aCatalogs as Topic10aChromosome Mapping10aComputer Simulation10aDNA Mutational Analysis10aGenetic Predisposition to Disease10aHumans10aModels, Genetic10aMolecular Sequence Data10amutation10aNeoplasm Proteins10aNeoplasms10aPolymorphism, Single Nucleotide10aProtein Interaction Mapping10aSignal Transduction1 aPorta-Pardo, Eduard1 aGarcía-Alonso, Luz1 aHrabe, Thomas1 aDopazo, Joaquin1 aGodzik, Adam uhttps://www.clinbioinfosspa.es/content/pan-cancer-catalogue-cancer-driver-protein-interaction-interfaces02147nas a2200301 4500008004100000022001400041245009600055210006900151260001600220520124900236100002101485700002401506700001401530700001401544700002201558700001701580700001701597700001701614700002301631700001701654700001801671700001501689700001901704700001801723700001501741700001801756856007101774 2015 eng d a1546-169600aPrediction of human population responses to toxic compounds by a collaborative competition.0 aPrediction of human population responses to toxic compounds by a c2015 Aug 103 aThe ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson’s r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.1 aEduati, Federica1 aMangravite, Lara, M1 aWang, Tao1 aTang, Hao1 aBare, Christopher1 aHuang, Ruili1 aNorman, Thea1 aKellen, Mike1 aMenden, Michael, P1 aYang, Jichen1 aZhan, Xiaowei1 aZhong, Rui1 aXiao, Guanghua1 aXia, Menghang1 aAbdo, Nour1 aKosyk, Oksana uhttp://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3299.html02330nas a2200265 4500008004100000022001400041245012000055210006900175260001300244300001300257490000700270520140800277653002301685653001301708653003201721653004301753100001901796700001901815700001601834700002401850700002001874700002401894700001501918856013101933 2015 eng d a1362-496200aPTMcode v2: a resource for functional associations of post-translational modifications within and between proteins.0 aPTMcode v2 a resource for functional associations of posttransla c2015 Jan aD494-5020 v433 aThe post-translational regulation of proteins is mainly driven by two molecular events, their modification by several types of moieties and their interaction with other proteins. These two processes are interdependent and together are responsible for the function of the protein in a particular cell state. Several databases focus on the prediction and compilation of protein-protein interactions (PPIs) and no less on the collection and analysis of protein post-translational modifications (PTMs), however, there are no resources that concentrate on describing the regulatory role of PTMs in PPIs. We developed several methods based on residue co-evolution and proximity to predict the functional associations of pairs of PTMs that we apply to modifications in the same protein and between two interacting proteins. In order to make data available for understudied organisms, PTMcode v2 (http://ptmcode.embl.de) includes a new strategy to propagate PTMs from validated modified sites through orthologous proteins. The second release of PTMcode covers 19 eukaryotic species from which we collected more than 300,000 experimentally verified PTMs (>1,300,000 propagated) of 69 types extracting the post-translational regulation of >100,000 proteins and >100,000 interactions. In total, we report 8 million associations of PTMs regulating single proteins and over 9.4 million interplays tuning PPIs.
10aDatabases, Protein10aInternet10aProtein Interaction Mapping10aProtein Processing, Post-Translational1 aMinguez, Pablo1 aLetunic, Ivica1 aParca, Luca1 aGarcía-Alonso, Luz1 aDopazo, Joaquin1 aHuerta-Cepas, Jaime1 aBork, Peer uhttps://www.clinbioinfosspa.es/content/ptmcode-v2-resource-functional-associations-post-translational-modifications-within-and02168nas a2200289 4500008004100000022001400041245017000055210006900225260000900294300000800303490000600311520111200317100002701429700001601456700002901472700002601501700002901527700001901556700002101575700002901596700002701625700002001652700002401672700002401696700002601720856013201746 2014 eng d a2234-943X00aThe Activation of the Sox2 RR2 Pluripotency Transcriptional Reporter in Human Breast Cancer Cell Lines is Dynamic and Labels Cells with Higher Tumorigenic Potential.0 aActivation of the Sox2 RR2 Pluripotency Transcriptional Reporter c2014 a3080 v43 aThe striking similarity displayed at the mechanistic level between tumorigenesis and the generation of induced pluripotent stem cells and the fact that genes and pathways relevant for embryonic development are reactivated during tumor progression highlights the link between pluripotency and cancer. Based on these observations, we tested whether it is possible to use a pluripotency-associated transcriptional reporter, whose activation is driven by the SRR2 enhancer from the Sox2 gene promoter (named S4+ reporter), to isolate cancer stem cells (CSCs) from breast cancer cell lines. The S4+ pluripotency transcriptional reporter allows the isolation of cells with enhanced tumorigenic potential and its activation was switched on and off in the cell lines studied, reflecting a plastic cellular process. Microarray analysis comparing the populations in which the reporter construct is active versus inactive showed that positive cells expressed higher mRNA levels of cytokines (IL-8, IL-6, TNF) and genes (such as ATF3, SNAI2, and KLF6) previously related with the CSC phenotype in breast cancer.
1 aIglesias, Juan, Manuel1 aLeis, Olatz1 aRuiz, Estíbaliz, Pérez1 aBarrie, Juan, Gumuzio1 aGarcia-Garcia, Francisco1 aAduriz, Ariane1 aBeloqui, Izaskun1 aHernandez-Garcia, Susana1 aLopez-Mato, Maria, Paz1 aDopazo, Joaquin1 aPandiella, Atanasio1 aMenendez, Javier, A1 aMartin, Angel, Garcia uhttps://www.clinbioinfosspa.es/content/activation-sox2-rr2-pluripotency-transcriptional-reporter-human-breast-cancer-cell-lines02676nas a2200541 4500008004100000022001400041245013100055210006900186260000900255300000900264490000600273520115300279653001201432100002001444700002001464700001601484700001701500700002501517700001601542700002001558700001801578700002101596700001801617700002001635700002001655700002101675700001401696700001501710700001801725700001901743700002601762700002701788700002701815700001601842700001301858700002101871700002601892700001801918700001601936700001801952700001501970700001501985700001302000700001502013700001302028700001602041856007702057 2014 eng d a2041-172300aAssessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.0 aAssessing technical performance in differential gene expression c2014 a51250 v53 aThere is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard ’dashboard’ of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.10aRNA-seq1 aMunro, Sarah, A1 aLund, Steven, P1 aPine, Scott1 aBinder, Hans1 aClevert, Djork-Arné1 aConesa, Ana1 aDopazo, Joaquin1 aFasold, Mario1 aHochreiter, Sepp1 aHong, Huixiao1 aJafari, Nadereh1 aKreil, David, P1 aLabaj, Paweł, P1 aLi, Sheng1 aLiao, Yang1 aLin, Simon, M1 aMeehan, Joseph1 aMason, Christopher, E1 aSantoyo-López, Javier1 aSetterquist, Robert, A1 aShi, Leming1 aShi, Wei1 aSmyth, Gordon, K1 aStralis-Pavese, Nancy1 aSu, Zhenqiang1 aTong, Weida1 aWang, Charles1 aWang, Jian1 aXu, Joshua1 aYe, Zhan1 aYang, Yong1 aYu, Ying1 aSalit, Marc uhttp://www.nature.com/ncomms/2014/140925/ncomms6125/full/ncomms6125.html02423nas a2200433 4500008004100000022001400041245008500055210006900140260001500209300001400224490000700238520117700245653001501422653001601437653003101453100002001484700002201504700001901526700001901545700001701564700002201581700001901603700002601622700001801648700002301666700002201689700002101711700002001732700001801752700002001770700003001790700002301820700002501843700002001868700002001888700001301908700002001921856004801941 2014 eng d a1538-744500aA Comprehensive DNA Methylation Profile of Epithelial-to-Mesenchymal Transition.0 aComprehensive DNA Methylation Profile of EpithelialtoMesenchymal c2014 Aug 8 a5608–190 v743 aEpithelial-to-mesenchymal transition (EMT) is a plastic process in which fully differentiated epithelial cells are converted into poorly differentiated, migratory and invasive mesenchymal cells and it has been related to the metastasis potential of tumors. This is a reversible process and cells can also eventually undergo mesenchymal-to-epithelial transition (MET). The existence of a dynamic EMT process suggests the involvement of epigenetic shifts in the phenotype. Herein, we obtained the DNA methylomes at single-base resolution of MDCK cells undergoing epithelial-to-mesenchymal transition (EMT) and translated the identified differentially methylated regions (DMRs) to human breast cancer cells undergoing a gain of migratory and invasive capabilities associated with the EMT phenotype. We noticed dynamic and reversible changes of DNA methylation, both on promoter sequences and gene-bodies in association with transcription regulation of EMT-related genes. Most importantly, the identified DNA methylation markers of EMT were present in primary mammary tumors in association with the epithelial or the mesenchymal phenotype of the studied breast cancer samples.10aMethyl-Seq10aMethylomics10aNext Generation Sequencing1 aCarmona, Javier1 aDavalos, Veronica1 aVidal, Enrique1 aGomez, Antonio1 aHeyn, Holger1 aHashimoto, Yutaka1 aVizoso, Miguel1 aMartinez-Cardus, Anna1 aSayols, Sergi1 aFerreira, Humberto1 aSanchez-Mut, Jose1 aMoran, Sebastian1 aMargeli, Mireia1 aCastella, Eva1 aBerdasco, Maria1 aStefansson, Olafur, Andri1 aEyfjord, Jorunn, E1 aGonzalez-Suarez, Eva1 aDopazo, Joaquin1 aOrozco, Modesto1 aGut, Ivo1 aEsteller, Manel uhttp://www.ncbi.nlm.nih.gov/pubmed/2510642702492nas a2200577 4500008004100000022001400041245006100055210005800116260001500174300001600189490000700205520082400212653000801036653001901044653001701063100001801080700002101098700002101119700002801140700002301168700001701191700002201208700002201230700003001252700001601282700002001298700002601318700002901344700002201373700002201395700002001417700002901437700002701466700002101493700001701514700002201531700002101553700002401574700002201598700002901620700002701649700002001676700002001696700002501716700002101741700002001762700001601782700002801798700002101826856006701847 2014 eng d a1098-100400aA New Overgrowth Syndrome is Due to Mutations in RNF125.0 aNew Overgrowth Syndrome is Due to Mutations in RNF125 c2014 Sep 5 a1436–14410 v353 aOvergrowth syndromes (OGS) are a group of disorders in which all parameters of growth and physical development are above the mean for age and sex. We evaluated a series of 270 families from the Spanish Overgrowth Syndrome Registry with no known overgrowth syndrome. We identified one de novo deletion and three missense mutations in RNF125 in six patients from 4 families with overgrowth, macrocephaly, intellectual disability, mild hydrocephaly, hypoglycaemia and inflammatory diseases resembling Sjögren syndrome. RNF125 encodes an E3 ubiquitin ligase and is a novel gene of OGS. Our studies of the RNF125 pathway point to upregulation of RIG-I-IPS1-MDA5 and/or disruption of the PI3K-AKT and interferon signaling pathways as the putative final effectors. This article is protected by copyright. All rights reserved.10aNGS10aprioritization10aRare Disease1 aTenorio, Jair1 aMansilla, Alicia1 aValencia, María1 aMartínez-Glez, Víctor1 aRomanelli, Valeria1 aArias, Pedro1 aCastrejón, Nerea1 aPoletta, Fernando1 aGuillén-Navarro, Encarna1 aGordo, Gema1 aMansilla, Elena1 aGarcía-Santiago, Fé1 aGonzález-Casado, Isabel1 aVallespín, Elena1 aPalomares, María1 aMori, María, A1 aSantos-Simarro, Fernando1 aGarcía-Miñaur, Sixto1 aFernández, Luis1 aMena, Rocío1 aBenito-Sanz, Sara1 aDel Pozo, Angela1 aSilla, Juan, Carlos1 aIbañez, Kristina1 aLópez-Granados, Eduardo1 aMartín-Trujillo, Alex1 aMontaner, David1 aHeath, Karen, E1 aCampos-Barros, Angel1 aDopazo, Joaquín1 aNevado, Julián1 aMonk, David1 aRuiz-Pérez, Víctor, L1 aLapunzina, Pablo uhttp://onlinelibrary.wiley.com/doi/10.1002/humu.22689/abstract02957nas a2200481 4500008004100000022001400041245013400055210006900189260001600258300001100274490000800285520141700293653002801710653002501738653001001763653001501773653001601788653002001804653002001824653003001844653001101874653000901885653001601894653004401910653001901954653001801973100002401991700002402015700002602039700002502065700002402090700002202114700001802136700002002154700002902174700002902203700001802232700002402250700002402274700002502298700002102323856013102344 2013 eng d a1873-349200aNovel genes detected by transcriptional profiling from whole-blood cells in patients with early onset of acute coronary syndrome.0 aNovel genes detected by transcriptional profiling from wholebloo c2013 Jun 05 a184-900 v4213 aBACKGROUND: Genome-wide expression analysis using microarrays has been used as a research strategy to discovery new biomarkers and candidate genes for a number of diseases. We aim to find new biomarkers for the prediction of acute coronary syndrome (ACS) with a differentially expressed mRNA profiling approach using whole genomic expression analysis in a peripheral blood cell model from patients with early ACS.
METHODS AND RESULTS: This study was carried out in two phases. On phase 1 a restricted clinical criteria (ACS-Ph1, n=9 and CG-Ph1, n=6) was used in order to select potential mRNA biomarkers candidates. A subsequent phase 2 study was performed using selected phase 1 markers analyzed by RT-qPCR using a larger and independent casuistic (ACS-Ph2, n=74 and CG-Ph2, n=41). A total of 549 genes were found to be differentially expressed in the first 48 h after the ACS-Ph1. Technical and biological validation further confirmed that ALOX15, AREG, BCL2A1, BCL2L1, CA1, COX7B, ECHDC3, IL18R1, IRS2, KCNE1, MMP9, MYL4 and TREML4, are differentially expressed in both phases of this study.
CONCLUSIONS: Transcriptomic analysis by microarray technology demonstrated differential expression during a 48 h time course suggesting a potential use of some of these genes as biomarkers for very early stages of ACS, as well as for monitoring early cardiac ischemic recovery.
10aAcute Coronary Syndrome10aAcute-Phase Proteins10aAdult10abiomarkers10aBlood Cells10aEarly Diagnosis10agene expression10aGene Expression Profiling10aHumans10aMale10aMiddle Aged10aOligonucleotide Array Sequence Analysis10aRNA, Messenger10aTranscriptome1 aSilbiger, Vivian, N1 aLuchessi, André, D1 aHirata, Rosário, D C1 aLima-Neto, Lídio, G1 aCavichioli, Débora1 aCarracedo, Ángel1 aBrión, Maria1 aDopazo, Joaquin1 aGarcia-Garcia, Francisco1 aSantos, Elizabete, S Dos1 aRamos, Rui, F1 aSampaio, Marcelo, F1 aArmaganijan, Dikran1 aSousa, Amanda, G M R1 aHirata, Mario, H uhttps://www.clinbioinfosspa.es/content/novel-genes-detected-transcriptional-profiling-whole-blood-cells-patients-early-onset-001640nas a2200289 4500008004100000022001400041245018800055210006900243260001600312520053300328100002400861700002400885700002600909700002500935700002400960700002200984700001801006700002101024700002901045700002901074700001801103700002401121700002401145700002501169700002101194856013501215 2013 eng d a1873-349200aNovel genes detected by transcriptional profiling from whole-blood cells in patients with early onset of acute coronary syndrome: Transcriptional profiling of acute coronary syndrome.0 aNovel genes detected by transcriptional profiling from wholebloo c2013 Mar 243 a{BACKGROUND: Genome-wide expression analysis using microarrays has been used as a research strategy to discovery new biomarkers and candidate genes for a number of diseases. We aim to find new biomarkers for the prediction of acute coronary syndrome (ACS) with a differentially expressed mRNA profiling approach using whole genomic expression analysis in a peripheral blood cell model from patients with early ACS. METHODS AND RESULTS: This study was carried out in two phases. On phase 1 a restricted clinical criteria (ACS-Ph11 aSilbiger, Vivian, N1 aLuchessi, André, D1 aHirata, Rosário, D C1 aLima-Neto, Lídio, G1 aCavichioli, Débora1 aCarracedo, Ángel1 aBrión, Maria1 aDopazo, Joaquín1 aGarcia-Garcia, Francisco1 aSantos, Elizabete, S Dos1 aRamos, Rui, F1 aSampaio, Marcelo, F1 aArmaganijan, Dikran1 aSousa, Amanda, G M R1 aHirata, Mario, H uhttps://www.clinbioinfosspa.es/content/novel-genes-detected-transcriptional-profiling-whole-blood-cells-patients-early-onset-acute02519nas a2200373 4500008004100000022001400041245006800055210006500123260001500188300000800203490000600211520145200217653000901669653001601678653002101694653002701715100002601742700001701768700002301785700002401808700001901832700002201851700002001873700002401893700001601917700002501933700002301958700002401981700002602005700002502031700002102056700001902077856004902096 2013 eng d a1750-117200aPathways systematically associated to Hirschsprung’s disease.0 aPathways systematically associated to Hirschsprung s disease c2013 Dec 2 a1870 v83 aDespite it has been reported that several loci are involved in Hirschsprung’s disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung’s disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.10aGWAS10aHirschprung10anetwork analysis10aPathway Based Analysis1 aFernández, Raquel, M1 aBleda, Marta1 aLuzón-Toro, Berta1 aGarcía-Alonso, Luz1 aArnold, Stacey1 aSribudiani, Yunia1 aBesmond, Claude1 aLantieri, Francesca1 aDoan, Betty1 aCeccherini, Isabella1 aLyonnet, Stanislas1 aHofstra, Robert, Mw1 aChakravarti, Aravinda1 aAntiňolo, Guillermo1 aDopazo, Joaquín1 aBorrego, Salud uhttp://www.ojrd.com/content/8/1/187/abstract02677nas a2200409 4500008004100000022001400041245006600055210006400121260001600185300000800201490000600209520145600215653001101671653003801682653001301720653002501733653001101758653000901769653003601778100002601814700001701840700002301857700002401880700001901904700002201923700002001945700002401965700001601989700002502005700002302030700002402053700002602077700002502103700002002128700001902148856010002167 2013 eng d a1750-117200aPathways systematically associated to Hirschsprung's disease.0 aPathways systematically associated to Hirschsprungs disease c2013 Dec 02 a1870 v83 aDespite it has been reported that several loci are involved in Hirschsprung's disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung's disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.
10aFemale10aGenetic Predisposition to Disease10aGenotype10aHirschsprung Disease10aHumans10aMale10aPolymorphism, Single Nucleotide1 aFernández, Raquel, M1 aBleda, Marta1 aLuzón-Toro, Berta1 aGarcía-Alonso, Luz1 aArnold, Stacey1 aSribudiani, Yunia1 aBesmond, Claude1 aLantieri, Francesca1 aDoan, Betty1 aCeccherini, Isabella1 aLyonnet, Stanislas1 aHofstra, Robert, Mw1 aChakravarti, Aravinda1 aAntiňolo, Guillermo1 aDopazo, Joaquin1 aBorrego, Salud uhttps://www.clinbioinfosspa.es/content/pathways-systematically-associated-hirschsprungs-disease02998nas a2200349 4500008004100000022001400041245015500055210006900210260000900279300001100288490000600299520188000305100002102185700001902206700001702225700002002242700002402262700002202286700002802308700002102336700002002357700002102377700002102398700002302419700002902442700001602471700001802487700002102505700002402526700001902550856007902569 2012 eng d a1932-620300aDevelopment, Characterization and Experimental Validation of a Cultivated Sunflower (Helianthus annuus L.) Gene Expression Oligonucleotide Microarray.0 aDevelopment Characterization and Experimental Validation of a Cu c2012 ae458990 v73 aOligonucleotide-based microarrays with accurate gene coverage represent a key strategy for transcriptional studies in orphan species such as sunflower, H. annuus L., which lacks full genome sequences. The goal of this study was the development and functional annotation of a comprehensive sunflower unigene collection and the design and validation of a custom sunflower oligonucleotide-based microarray. A large scale EST (>130,000 ESTs) curation, assembly and sequence annotation was performed using Blast2GO (www.blast2go.de). The EST assembly comprises 41,013 putative transcripts (12,924 contigs and 28,089 singletons). The resulting Sunflower Unigen Resource (SUR version 1.0) was used to design an oligonucleotide-based Agilent microarray for cultivated sunflower. This microarray includes a total of 42,326 features: 1,417 Agilent controls, 74 control probes for sunflower replicated 10 times (740 controls) and 40,169 different non-control probes. Microarray performance was validated using a model experiment examining the induction of senescence by water deficit. Pre-processing and differential expression analysis of Agilent microarrays was performed using the Bioconductor limma package. The analyses based on p-values calculated by eBayes (p<0.01) allowed the detection of 558 differentially expressed genes between water stress and control conditions; from these, ten genes were further validated by qPCR. Over-represented ontologies were identified using FatiScan in the Babelomics suite. This work generated a curated and trustable sunflower unigene collection, and a custom, validated sunflower oligonucleotide-based microarray using Agilent technology. Both the curated unigene collection and the validated oligonucleotide microarray provide key resources for sunflower genome analysis, transcriptional studies, and molecular breeding for crop improvement.1 aFernandez, Paula1 aSoria, Marcelo1 aBlesa, David1 aDirienzo, Julio1 aMoschen, Sebastián1 aRivarola, Máximo1 aClavijo, Bernardo, Jose1 aGonzalez, Sergio1 aPeluffo, Lucila1 aPríncipi, Dario1 aDosio, Guillermo1 aAguirrezabal, Luis1 aGarcia-Garcia, Francisco1 aConesa, Ana1 aHopp, Esteban1 aDopazo, Joaquín1 aHeinz, Ruth, Amelia1 aPaniego, Norma uhttp://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.004589902277nas a2200193 4500008004100000022001400041245011200055210006900167260000900236300001100245490000600256520164600262100002401908700002101932700002101953700002001974700002001994856006902014 2012 eng d a1176-934300aEvolutionary Genomics of Genes Involved in Olfactory Behavior in the Drosophila melanogaster Species Group.0 aEvolutionary Genomics of Genes Involved in Olfactory Behavior in c2012 a89-1040 v83 aPrevious comparative genomic studies of genes involved in olfactory behavior in Drosophila focused only on particular gene families such as odorant receptor and/or odorant binding proteins. However, olfactory behavior has a complex genetic architecture that is orchestrated by many interacting genes. In this paper, we present a comparative genomic study of olfactory behavior in Drosophila including an extended set of genes known to affect olfactory behavior. We took advantage of the recent burst of whole genome sequences and the development of powerful statistical tools to analyze genomic data and test evolutionary and functional hypotheses of olfactory genes in the six species of the Drosophila melanogaster species group for which whole genome sequences are available. Our study reveals widespread purifying selection and limited incidence of positive selection on olfactory genes. We show that the pace of evolution of olfactory genes is mostly independent of the life cycle stage, and of the number of life cycle stages, in which they participate in olfaction. However, we detected a relationship between evolutionary rates and the position that the gene products occupy in the olfactory system, genes occupying central positions tend to be more constrained than peripheral genes. Finally, we demonstrate that specialization to one host does not seem to be associated with bursts of adaptive evolution in olfactory genes in D. sechellia and D. erecta, the two specialists species analyzed, but rather different lineages have idiosyncratic evolutionary histories in which both historical and ecological factors have been involved.1 aLavagnino, Nicolás1 aSerra, François1 aArbiza, Leonardo1 aDopazo, Hernán1 aHasson, Esteban uhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3273929/?tool=pubmed03423nas a2200253 4500008004100000022001400041245011200055210006900167260000900236300000800245490000700253520257000260100002502830700003202855700001602887700001702903700002102920700002502941700002202966700001802988700002203006700001803028856012303046 2012 eng d a1471-216400aIdentification of yeast genes that confer resistance to chitosan oligosaccharide (COS) using chemogenomics.0 aIdentification of yeast genes that confer resistance to chitosan c2012 a2670 v133 aBACKGROUND: Chitosan oligosaccharide (COS), a deacetylated derivative of chitin, is an abundant, and renewable natural polymer. COS has higher antimicrobial properties than chitosan and is presumed to act by disrupting/permeabilizing the cell membranes of bacteria, yeast and fungi. COS is relatively non-toxic to mammals. By identifying the molecular and genetic targets of COS, we hope to gain a better understanding of the antifungal mode of action of COS. RESULTS: Three different chemogenomic fitness assays, haploinsufficiency (HIP), homozygous deletion (HOP), and multicopy suppression (MSP) profiling were combined with a transcriptomic analysis to gain insight in to the mode of action and mechanisms of resistance to chitosan oligosaccharides. The fitness assays identified 39 yeast deletion strains sensitive to COS and 21 suppressors of COS sensitivity. The genes identified are involved in processes such as RNA biology (transcription, translation and regulatory mechanisms), membrane functions (e.g. signalling, transport and targeting), membrane structural components, cell division, and proteasome processes. The transcriptomes of control wild type and 5 suppressor strains overexpressing ARL1, BCK2, ERG24, MSG5, or RBA50, were analyzed in the presence and absence of COS. Some of the up-regulated transcripts in the suppressor overexpressing strains exposed to COS included genes involved in transcription, cell cycle, stress response and the Ras signal transduction pathway. Down-regulated transcripts included those encoding protein folding components and respiratory chain proteins. The COS-induced transcriptional response is distinct from previously described environmental stress responses (i.e. thermal, salt, osmotic and oxidative stress) and pre-treatment with these well characterized environmental stressors provided little or any resistance to COS. CONCLUSIONS: Overexpression of the ARL1 gene, a member of the Ras superfamily that regulates membrane trafficking, provides protection against COS-induced cell membrane permeability and damage. We found that the ARL1 COS-resistant over-expression strain was as sensitive to Amphotericin B, Fluconazole and Terbinafine as the wild type cells and that when COS and Fluconazole are used in combination they act in a synergistic fashion. The gene targets of COS identified in this study indicate that COS’s mechanism of action is different from other commonly studied fungicides that target membranes, suggesting that COS may be an effective fungicide for drug-resistant fungal pathogens.1 aJaime, María, D L A1 aLopez-Llorca, Luis, Vicente1 aConesa, Ana1 aLee, Anna, Y1 aProctor, Michael1 aHeisler, Lawrence, E1 aGebbia, Marinella1 aGiaever, Guri1 aWestwood, Timothy1 aNislow, Corey uhttps://www.clinbioinfosspa.es/content/identification-yeast-genes-confer-resistance-chitosan-oligosaccharide-cos-using01878nas a2200289 4500008004100000022001400041245008700055210006900142260000900211300001100220490000600231520100600237100002701243700002001270700002801290700001901318700002701337700002201364700001801386700002001404700002001424700001801444700002101462700002101483700002001504856006401524 2012 eng d a1748-567300aSelect your SNPs (SYSNPs): a web tool for automatic and massive selection of SNPs.0 aSelect your SNPs SYSNPs a web tool for automatic and massive sel c2012 a324-340 v63 aAssociation studies are the choice approach in the discovery of the genomic basis of complex traits. To carry out such analysis, researchers frequently need to (1) select optimally informative sets of Single Nucleotide Polymorphisms (SNPs) in candidate regions and (2) annotate the results of associations found by means of genome-wide SNP arrays. These are complex tasks, since many criteria have to be considered, including the SNPs’ functional properties, technological information and haplotype frequencies in given populations. SYSNPs implements algorithms that allow for efficient and simultaneous consideration of all the relevant criteria to obtain sets of SNPs that properly cover arbitrarily large lists of genes or genomic regions. Complementarily, SYSNPs allows for comprehensive functional annotation of SNPs linked to any given marker SNP. SYSNPs dramatically reduces the effort needed for SNP selection from days of searching various databases to a few minutes using a simple browser.1 aLorente-Galdos, Belén1 aMedina, Ignacio1 aMorcillo-Suarez, Carlos1 aHeredia, Txema1 aCarreño-Torres, Angel1 aSangrós, Ricardo1 aAlegre, Josep1 aPita, Guillermo1 aVellalta, Gemma1 aMalats, Nuria1 aPisano, David, G1 aDopazo, Joaquín1 aNavarro, Arcadi uhttp://inderscience.metapress.com/content/f76740x8071u513n/03644nas a2200505 4500008004100000022001400041245012700055210006900182260000900251300001100260490000600271520212800277653001902405653001202424653002302436653001802459653002602477653001802503653000802521653001502529653002202544653003002566653002302596653001002619653002402629653002802653653000902681653002202690653001302712653001702725653001802742100001902760700001802779700002102797700001402818700001602832700002402848700002202872700002102894700002202915700001802937700001702955700003002972856013603002 2012 eng d a1932-620300aTranscriptome profiling of the intoxication response of Tenebrio molitor larvae to Bacillus thuringiensis Cry3Aa protoxin.0 aTranscriptome profiling of the intoxication response of Tenebrio c2012 ae346240 v73 aBacillus thuringiensis (Bt) crystal (Cry) proteins are effective against a select number of insect pests, but improvements are needed to increase efficacy and decrease time to mortality for coleopteran pests. To gain insight into the Bt intoxication process in Coleoptera, we performed RNA-Seq on cDNA generated from the guts of Tenebrio molitor larvae that consumed either a control diet or a diet containing Cry3Aa protoxin. Approximately 134,090 and 124,287 sequence reads from the control and Cry3Aa-treated groups were assembled into 1,318 and 1,140 contigs, respectively. Enrichment analyses indicated that functions associated with mitochondrial respiration, signalling, maintenance of cell structure, membrane integrity, protein recycling/synthesis, and glycosyl hydrolases were significantly increased in Cry3Aa-treated larvae, whereas functions associated with many metabolic processes were reduced, especially glycolysis, tricarboxylic acid cycle, and fatty acid synthesis. Microarray analysis was used to evaluate temporal changes in gene expression after 6, 12 or 24 h of Cry3Aa exposure. Overall, microarray analysis indicated that transcripts related to allergens, chitin-binding proteins, glycosyl hydrolases, and tubulins were induced, and those related to immunity and metabolism were repressed in Cry3Aa-intoxicated larvae. The 24 h microarray data validated most of the RNA-Seq data. Of the three intoxication intervals, larvae demonstrated more differential expression of transcripts after 12 h exposure to Cry3Aa. Gene expression examined by three different methods in control vs. Cry3Aa-treated larvae at the 24 h time point indicated that transcripts encoding proteins with chitin-binding domain 3 were the most differentially expressed in Cry3Aa-intoxicated larvae. Overall, the data suggest that T. molitor larvae mount a complex response to Cry3Aa during the initial 24 h of intoxication. Data from this study represent the largest genetic sequence dataset for T. molitor to date. Furthermore, the methods in this study are useful for comparative analyses in organisms lacking a sequenced genome.10aAdministration10aAnimals10aBacterial Proteins10aBase Sequence10aBiosynthetic Pathways10aComplementary10aDNA10aEndotoxins10aEnergy Metabolism10aGene Expression Profiling10aHemolysin Proteins10aLarva10aMicroarray Analysis10aMolecular Sequence Data10aOral10aSequence Analysis10aTenebrio10aTime Factors10aTranscriptome1 aOppert, Brenda1 aDowd, Scot, E1 aBouffard, Pascal1 aLi, Lewyn1 aConesa, Ana1 aLorenzen, Marcé, D1 aToutges, Michelle1 aMarshall, Jeremy1 aHuestis, Diana, L1 aFabrick, Jeff1 aOppert, Cris1 aJurat-Fuentes, Juan, Luis uhttps://www.clinbioinfosspa.es/content/transcriptome-profiling-intoxication-response-tenebrio-molitor-larvae-bacillus-thuringiensis01857nas a2200265 4500008004100000022001400041245011600055210006900171260001600240300001400256490000600270520098400276653003001260653001801290653001001308653000801318100002701326700003001353700002901383700002301412700002001435700002101455700002001476856009501496 2012 eng d a1557-996400aUsing GPUs for the Exact Alignment of Short-read Genetic Sequences by Means of the Burrows–Wheeler Transform.0 aUsing GPUs for the Exact Alignment of Shortread Genetic Sequence c2012 Mar 20 a1245-12560 v93 aGeneral Purpose Graphic Processing Units (GPGPUs) constitute an inexpensive resource for computing-intensive applications that could exploit an intrinsic fine-grain parallelism. This paper presents the design and implementation in GPGPUs of an exact alignment tool for nucleotide sequences based on the Burrows-Wheeler Transform. We compare this algorithm with state-of-the-art implementations of the same algorithm over standard CPUs, and considering the same conditions in terms of I/O. Excluding disk transfers, the implementation of the algorithm in GPUs shows a speedup larger than 12x, when compared to CPU execution. This implementation exploits the parallelism by concurrently searching different sequences on the same reference search tree, maximising memory locality and ensuring a symmetric access to the data. The article describes the behaviour of the algorithm in GPU, showing a good scalability in the performance, only limited by the size of the GPU inner memory.10aBurrows-Wheeler transform10aCPU execution10aGPGPU10aNGS1 aTorres, Jose, Salavert1 aEspert, Ignacio, Blanquer1 aDominguez, Andres, Tomas1 aHernendez, Vicente1 aMedina, Ignacio1 aTerraga, Joaquin1 aDopazo, Joaquin uhttp://ieeexplore.ieee.org.sire.ub.edu/xpl/articleDetails.jsp?reload=true&arnumber=617588802874nas a2200493 4500008004100000022001400041245007000055210006800125260001600193300001100209490000600220520139500226653001801621653002701639653002101666653004101687653002001728653002401748653000901772653001101781653001801792653004201810653001101852653003701863653001301900653003801913653002701951653001301978100001701991700001902008700001802027700002502045700002102070700002002091700001902111700002602130700002302156700001802179700001602197700002402213700002002237700002002257856010302277 2012 eng d a1559-230800aWhole-genome bisulfite DNA sequencing of a DNMT3B mutant patient.0 aWholegenome bisulfite DNA sequencing of a DNMT3B mutant patient c2012 Jun 01 a542-500 v73 aThe immunodeficiency, centromere instability and facial anomalies (ICF) syndrome is associated to mutations of the DNA methyl-transferase DNMT3B, resulting in a reduction of enzyme activity. Aberrant expression of immune system genes and hypomethylation of pericentromeric regions accompanied by chromosomal instability were determined as alterations driving the disease phenotype. However, so far only technologies capable to analyze single loci were applied to determine epigenetic alterations in ICF patients. In the current study, we performed whole-genome bisulphite sequencing to assess alteration in DNA methylation at base pair resolution. Genome-wide we detected a decrease of methylation level of 42%, with the most profound changes occurring in inactive heterochromatic regions, satellite repeats and transposons. Interestingly, transcriptional active loci and ribosomal RNA repeats escaped global hypomethylation. Despite a genome-wide loss of DNA methylation the epigenetic landscape and crucial regulatory structures were conserved. Remarkably, we revealed a mislocated activity of mutant DNMT3B to H3K4me1 loci resulting in hypermethylation of active promoters. Functionally, we could associate alterations in promoter methylation with the ICF syndrome immunodeficient phenotype by detecting changes in genes related to the B-cell receptor mediated maturation pathway.
10aB-Lymphocytes10aCell Line, Transformed10aChild, Preschool10aDNA (Cytosine-5-)-Methyltransferases10aDNA Methylation10aEpigenesis, Genetic10aFace10aFemale10aGenome, Human10aHigh-Throughput Nucleotide Sequencing10aHumans10aImmunologic Deficiency Syndromes10amutation10aPrimary Immunodeficiency Diseases10aSequence Analysis, DNA10aSulfites1 aHeyn, Holger1 aVidal, Enrique1 aSayols, Sergi1 aSanchez-Mut, Jose, V1 aMoran, Sebastian1 aMedina, Ignacio1 aSandoval, Juan1 aSimó-Riudalbas, Laia1 aSzczesna, Karolina1 aHuertas, Dori1 aGatto, Sole1 aMatarazzo, Maria, R1 aDopazo, Joaquin1 aEsteller, Manel uhttps://www.clinbioinfosspa.es/content/whole-genome-bisulfite-dna-sequencing-dnmt3b-mutant-patient01382nas a2200313 4500008004100000245005700041210005500098260001300153300001300166490000600179520044900185100001700634700002200651700002700673700002200700700002000722700002200742700001800764700001900782700001700801700003300818700002800851700002000879700002600899700002500925700001600950700002100966856008100987 2011 eng d00aDiscovery of an ebolavirus-like filovirus in europe.0 aDiscovery of an ebolaviruslike filovirus in europe c2011 Oct ae10023040 v73 aFiloviruses, amongst the most lethal of primate pathogens, have only been reported as natural infections in sub-Saharan Africa and the Philippines. Infections of bats with the ebolaviruses and marburgviruses do not appear to be associated with disease. Here we report identification in dead insectivorous bats of a genetically distinct filovirus, provisionally named Lloviu virus, after the site of detection, Cueva del Lloviu, in Spain.
1 aNegredo, Ana1 aPalacios, Gustavo1 aVázquez-Morón, Sonia1 aGonzález, Félix1 aDopazo, Hernán1 aMolero, Francisca1 aJuste, Javier1 aQuetglas, Juan1 aSavji, Nazir1 aMartínez, Maria, de la Cruz1 aHerrera, Jesus, Enrique1 aPizarro, Manuel1 aHutchison, Stephen, K1 aEchevarría, Juan, E1 aLipkin, Ian1 aTenorio, Antonio uhttps://www.clinbioinfosspa.es/content/discovery-ebolavirus-filovirus-europe02376nas a2200289 4500008004100000022001400041245012000055210006900175260001300244300001000257490000700267520143700274653001201711653002301723653002501746653002101771653002001792653001101812653001101823653000901834653002201843100002401865700002001889700002301909700002001932856013401952 2011 eng d a1477-405400aEvidence for short-time divergence and long-time conservation of tissue-specific expression after gene duplication.0 aEvidence for shorttime divergence and longtime conservation of t c2011 Sep a442-80 v123 aGene duplication is one of the main mechanisms by which genomes can acquire novel functions. It has been proposed that the retention of gene duplicates can be associated to processes of tissue expression divergence. These models predict that acquisition of divergent expression patterns should be acquired shortly after the duplication, and that larger divergence in tissue expression would be expected for paralogs, as compared to orthologs of a similar age. Many studies have shown that gene duplicates tend to have divergent expression patterns and that gene family expansions are associated with high levels of tissue specificity. However, the timeframe in which these processes occur have rarely been investigated in detail, particularly in vertebrates, and most analyses do not include direct comparisons of orthologs as a baseline for the expected levels of tissue specificity in absence of duplications. To assess the specific contribution of duplications to expression divergence, we combine here phylogenetic analyses and expression data from human and mouse. In particular, we study differences in spatial expression among human-mouse paralogs, specifically duplicated after the radiation of mammals, and compare them to pairs of orthologs in the same species. Our results show that gene duplication leads to increased levels of tissue specificity and that this tends to occur promptly after the duplication event.
10aAnimals10aConserved Sequence10aEvolution, Molecular10aGene Duplication10agene expression10aGenome10aHumans10aMice10aOrgan Specificity1 aHuerta-Cepas, Jaime1 aDopazo, Joaquin1 aHuynen, Martijn, A1 aGabaldón, Toni uhttps://www.clinbioinfosspa.es/content/evidence-short-time-divergence-and-long-time-conservation-tissue-specific-expression-after02025nas a2200349 4500008004100000022001400041245011700055210006900172260001300241300001100254490000700265520090400272653002501176653001301201653001301214653001401227653002301241653001301264100002101277700002101298700002301319700002001342700002101362700001701383700002401400700003301424700002401457700002001481700002001501700002001521856013401541 2011 eng d a1362-496200aPhylemon 2.0: a suite of web-tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing.0 aPhylemon 20 a suite of webtools for molecular evolution phylogen c2011 Jul aW470-40 v393 aPhylemon 2.0 is a new release of the suite of web tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing. It has been designed as a response to the increasing demand of molecular sequence analyses for experts and non-expert users. Phylemon 2.0 has several unique features that differentiates it from other similar web resources: (i) it offers an integrated environment that enables evolutionary analyses, format conversion, file storage and edition of results; (ii) it suggests further analyses, thereby guiding the users through the web server; and (iii) it allows users to design and save phylogenetic pipelines to be used over multiple genes (phylogenomics). Altogether, Phylemon 2.0 integrates a suite of 30 tools covering sequence alignment reconstruction and trimming; tree reconstruction, visualization and manipulation; and evolutionary hypotheses testing.
10aEvolution, Molecular10aGenomics10aInternet10aPhylogeny10aSequence Alignment10aSoftware1 aSánchez, Rubén1 aSerra, François1 aTárraga, Joaquín1 aMedina, Ignacio1 aCarbonell, José1 aPulido, Luis1 aDe Maria, Alejandro1 aCapella-Gutíerrez, Salvador1 aHuerta-Cepas, Jaime1 aGabaldón, Toni1 aDopazo, Joaquin1 aDopazo, Hernán uhttps://www.clinbioinfosspa.es/content/phylemon-20-suite-web-tools-molecular-evolution-phylogenetics-phylogenomics-and-hypotheses02778nas a2200385 4500008004100000245011100041210006900152260001300221300001000234490000700244520152200251100002301773700002601796700001901822700002401841700002701865700002901892700002001921700002001941700002801961700001901989700002302008700002402031700002002055700001602075700002302091700001902114700002002133700002002153700002002173700002002193700002502213700002302238856013102261 2010 eng d00aChanges in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus.0 aChanges in the pattern of DNA methylation associate with twin di c2010 Feb a170-90 v203 aMonozygotic (MZ) twins are partially concordant for most complex diseases, including autoimmune disorders. Whereas phenotypic concordance can be used to study heritability, discordance suggests the role of non-genetic factors. In autoimmune diseases, environmentally driven epigenetic changes are thought to contribute to their etiology. Here we report the first high-throughput and candidate sequence analyses of DNA methylation to investigate discordance for autoimmune disease in twins. We used a cohort of MZ twins discordant for three diseases whose clinical signs often overlap: systemic lupus erythematosus (SLE), rheumatoid arthritis, and dermatomyositis. Only MZ twins discordant for SLE featured widespread changes in the DNA methylation status of a significant number of genes. Gene ontology analysis revealed enrichment in categories associated with immune function. Individual analysis confirmed the existence of DNA methylation and expression changes in genes relevant to SLE pathogenesis. These changes occurred in parallel with a global decrease in the 5-methylcytosine content that was concomitantly accompanied with changes in DNA methylation and expression levels of ribosomal RNA genes, although no changes in repetitive sequences were found. Our findings not only identify potentially relevant DNA methylation markers for the clinical characterization of SLE patients but also support the notion that epigenetic changes may be critical in the clinical manifestations of autoimmune disease.
1 aJavierre, Biola, M1 aFernandez, Agustin, F1 aRichter, Julia1 aAl-Shahrour, Fatima1 aMartin-Subero, Ignacio1 aRodriguez-Ubreva, Javier1 aBerdasco, Maria1 aFraga, Mario, F1 aO’Hanlon, Terrance, P1 aRider, Lisa, G1 aJacinto, Filipe, V1 aLopez-Longo, Javier1 aDopazo, Joaquin1 aForn, Marta1 aPeinado, Miguel, A1 aCarreño, Luis1 aSawalha, Amr, H1 aHarley, John, B1 aSiebert, Reiner1 aEsteller, Manel1 aMiller, Frederick, W1 aBallestar, Esteban uhttps://www.clinbioinfosspa.es/content/changes-pattern-dna-methylation-associate-twin-discordance-systemic-lupus-erythematosus02514nas a2200217 4500008004100000022001400041245005200055210005000107260001600157300000700173490000700180520188600187653002602073653002302099653001402122653001302136100002402149700002002173700002002193856008302213 2010 eng d a1471-210500aETE: a python Environment for Tree Exploration.0 aETE a python Environment for Tree Exploration c2010 Jan 13 a240 v113 aBACKGROUND: Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.
RESULTS: Here we present the Environment for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external data such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETE's programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations.
CONCLUSIONS: ETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformatic toolkits of a more general purpose. ETE is free software and can be downloaded from http://ete.cgenomics.org.
10aComputational Biology10aDatabases, Genetic10aPhylogeny10aSoftware1 aHuerta-Cepas, Jaime1 aDopazo, Joaquin1 aGabaldón, Toni uhttps://www.clinbioinfosspa.es/content/ete-python-environment-tree-exploration03102nas a2200325 4500008004100000245012300041210006900164260001600233520197300249100001902222700001902241700002402260700002102284700001702305700003102322700002002353700003002373700002502403700002402428700001902452700001902471700002102490700002602511700002402537700002902561700001802590700002002608700001902628856012902647 2010 eng d00aFine-scale evolution: genomic, phenotypic and ecological differentiation in two coexisting Salinibacter ruber strains.0 aFinescale evolution genomic phenotypic and ecological differenti c2010 Feb 183 aGenomic and metagenomic data indicate a high degree of genomic variation within microbial populations, although the ecological and evolutive meaning of this microdiversity remains unknown. Microevolution analyses, including genomic and experimental approaches, are so far very scarce for non-pathogenic bacteria. In this study, we compare the genomes, metabolomes and selected ecological traits of the strains M8 and M31 of the hyperhalophilic bacterium Salinibacter ruber that contain ribosomal RNA (rRNA) gene and intergenic regions that are identical in sequence and were simultaneously isolated from a Mediterranean solar saltern. Comparative analyses indicate that S. ruber genomes present a mosaic structure with conserved and hypervariable regions (HVRs). The HVRs or genomic islands, are enriched in transposases, genes related to surface properties, strain-specific genes and highly divergent orthologous. However, the many indels outside the HVRs indicate that genome plasticity extends beyond them. Overall, 10% of the genes encoded in the M8 genome are absent from M31 and could stem from recent acquisitions. S. ruber genomes also harbor 34 genes located outside HVRs that are transcribed during standard growth and probably derive from lateral gene transfers with Archaea preceding the M8/M31 divergence. Metabolomic analyses, phage susceptibility and competition experiments indicate that these genomic differences cannot be considered neutral from an ecological perspective. The results point to the avoidance of competition by micro-niche adaptation and response to viral predation as putative major forces that drive microevolution within these Salinibacter strains. In addition, this work highlights the extent of bacterial functional diversity and environmental adaptation, beyond the resolution of the 16S rRNA and internal transcribed spacers regions.The ISME Journal advance online publication, 18 February 2010; doi:10.1038/ismej.2010.6.
1 aPeña, Arantxa1 aTeeling, Hanno1 aHuerta-Cepas, Jaime1 aSantos, Fernando1 aYarza, Pablo1 aBrito-Echeverría, Jocelyn1 aLucio, Marianna1 aSchmitt-Kopplin, Philippe1 aMeseguer, Inmaculada1 aSchenowitz, Chantal1 aDossat, Carole1 aBarbe, Valerie1 aDopazo, Joaquín1 aRosselló-Mora, Ramon1 aSchüler, Margarete1 aGlöckner, Frank, Oliver1 aAmann, Rudolf1 aGabaldón, Toni1 aAntón, Josefa uhttps://www.clinbioinfosspa.es/content/fine-scale-evolution-genomic-phenotypic-and-ecological-differentiation-two-coexisting07842nas a2202545 4500008004100000245014500041210006900186260001300255300001100268490000700279520110300286100001601389700002201405700002201427700002101449700001701470700002301487700001801510700001801528700002601546700001901572700002501591700002201616700002301638700002301661700001801684700001901702700001701721700001201738700002201750700002301772700002301795700001401818700002401832700002001856700001701876700001601893700001701909700002001926700002201946700001701968700001701985700001502002700001602017700001502033700002402048700002102072700001802093700002702111700001802138700002002156700002002176700001902196700001802215700001602233700001702249700001502266700002002281700002102301700001802322700001402340700002402354700002002378700002202398700002002420700002702440700001102467700001402478700001302492700001802505700001702523700002602540700001302566700001302579700002302592700001902615700002202634700002202656700001802678700001902696700002102715700001502736700002102751700001602772700001602788700001702804700002702821700002302848700002102871700001702892700002702909700002002936700001702956700001502973700001402988700002203002700001703024700001903041700001703060700001703077700001503094700002203109700001603131700002703147700002003174700002403194700002003218700002603238700001703264700001503281700001603296700001403312700001703326700002603343700001603369700002203385700001703407700001503424700002103439700003003460700002103490700001903511700001603530700002603546700001203572700002303584700001403607700002303621700002103644700001803665700002003683700002403703700001503727700002403742700002103766700002003787700002103807700001903828700001703847700001103864700001703875700001803892700001303910700001703923700001303940700001403953700001703967700001303984700001903997700003204016700002004048700001904068700001904087700002404106700002004130700002104150700002404171700002104195700002404216700001804240700001904258700001704277700002004294700001704314700002104331700001804352700001704370700001204387700002504399700001904424700002404443700002604467700002504493700001504518700002004533700001704553700001904570700002304589700001604612700002104628700001904649700002004668700001704688700001604705700001504721700001804736700001704754700002204771700001804793700002004811700002504831700002304856700001804879700001504897700001704912700001404929700002204943700002104965700001904986700001605005700001905021700001505040700001205055700001405067700001505081700001705096700001405113700001505127700001505142700001705157700001605174700001605190700002605206856006405232 2010 eng d00aThe MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.0 aMicroArray Quality Control MAQCII study of common practices for c2010 Aug a827-380 v283 aGene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
1 aShi, Leming1 aCampbell, Gregory1 aJones, Wendell, D1 aCampagne, Fabien1 aWen, Zhining1 aWalker, Stephen, J1 aSu, Zhenqiang1 aChu, Tzu-Ming1 aGoodsaid, Federico, M1 aPusztai, Lajos1 aShaughnessy, John, D1 aOberthuer, André1 aThomas, Russell, S1 aPaules, Richard, S1 aFielden, Mark1 aBarlogie, Bart1 aChen, Weijie1 aDu, Pan1 aFischer, Matthias1 aFurlanello, Cesare1 aGallas, Brandon, D1 aGe, Xijin1 aMegherbi, Dalila, B1 aSymmans, Fraser1 aWang, May, D1 aZhang, John1 aBitter, Hans1 aBrors, Benedikt1 aBushel, Pierre, R1 aBylesjo, Max1 aChen, Minjun1 aCheng, Jie1 aCheng, Jing1 aChou, Jeff1 aDavison, Timothy, S1 aDelorenzi, Mauro1 aDeng, Youping1 aDevanarayan, Viswanath1 aDix, David, J1 aDopazo, Joaquin1 aDorff, Kevin, C1 aElloumi, Fathi1 aFan, Jianqing1 aFan, Shicai1 aFan, Xiaohui1 aFang, Hong1 aGonzaludo, Nina1 aHess, Kenneth, R1 aHong, Huixiao1 aHuan, Jun1 aIrizarry, Rafael, A1 aJudson, Richard1 aJuraeva, Dilafruz1 aLababidi, Samir1 aLambert, Christophe, G1 aLi, Li1 aLi, Yanen1 aLi, Zhen1 aLin, Simon, M1 aLiu, Guozhen1 aLobenhofer, Edward, K1 aLuo, Jun1 aLuo, Wen1 aMcCall, Matthew, N1 aNikolsky, Yuri1 aPennello, Gene, A1 aPerkins, Roger, G1 aPhilip, Reena1 aPopovici, Vlad1 aPrice, Nathan, D1 aQian, Feng1 aScherer, Andreas1 aShi, Tieliu1 aShi, Weiwei1 aSung, Jaeyun1 aThierry-Mieg, Danielle1 aThierry-Mieg, Jean1 aThodima, Venkata1 aTrygg, Johan1 aVishnuvajjala, Lakshmi1 aWang, Sue, Jane1 aWu, Jianping1 aWu, Yichao1 aXie, Qian1 aYousef, Waleed, A1 aZhang, Liang1 aZhang, Xuegong1 aZhong, Sheng1 aZhou, Yiming1 aZhu, Sheng1 aArasappan, Dhivya1 aBao, Wenjun1 aLucas, Anne, Bergstrom1 aBerthold, Frank1 aBrennan, Richard, J1 aBuness, Andreas1 aCatalano, Jennifer, G1 aChang, Chang1 aChen, Rong1 aCheng, Yiyu1 aCui, Jian1 aCzika, Wendy1 aDemichelis, Francesca1 aDeng, Xutao1 aDosymbekov, Damir1 aEils, Roland1 aFeng, Yang1 aFostel, Jennifer1 aFulmer-Smentek, Stephanie1 aFuscoe, James, C1 aGatto, Laurent1 aGe, Weigong1 aGoldstein, Darlene, R1 aGuo, Li1 aHalbert, Donald, N1 aHan, Jing1 aHarris, Stephen, C1 aHatzis, Christos1 aHerman, Damir1 aHuang, Jianping1 aJensen, Roderick, V1 aJiang, Rui1 aJohnson, Charles, D1 aJurman, Giuseppe1 aKahlert, Yvonne1 aKhuder, Sadik, A1 aKohl, Matthias1 aLi, Jianying1 aLi, Li1 aLi, Menglong1 aLi, Quan-Zhen1 aLi, Shao1 aLi, Zhiguang1 aLiu, Jie1 aLiu, Ying1 aLiu, Zhichao1 aMeng, Lu1 aMadera, Manuel1 aMartinez-Murillo, Francisco1 aMedina, Ignacio1 aMeehan, Joseph1 aMiclaus, Kelci1 aMoffitt, Richard, A1 aMontaner, David1 aMukherjee, Piali1 aMulligan, George, J1 aNeville, Padraic1 aNikolskaya, Tatiana1 aNing, Baitang1 aPage, Grier, P1 aParker, Joel1 aParry, Mitchell1 aPeng, Xuejun1 aPeterson, Ron, L1 aPhan, John, H1 aQuanz, Brian1 aRen, Yi1 aRiccadonna, Samantha1 aRoter, Alan, H1 aSamuelson, Frank, W1 aSchumacher, Martin, M1 aShambaugh, Joseph, D1 aShi, Qiang1 aShippy, Richard1 aSi, Shengzhu1 aSmalter, Aaron1 aSotiriou, Christos1 aSoukup, Mat1 aStaedtler, Frank1 aSteiner, Guido1 aStokes, Todd, H1 aSun, Qinglan1 aTan, Pei-Yi1 aTang, Rong1 aTezak, Zivana1 aThorn, Brett1 aTsyganova, Marina1 aTurpaz, Yaron1 aVega, Silvia, C1 aVisintainer, Roberto1 avon Frese, Juergen1 aWang, Charles1 aWang, Eric1 aWang, Junwei1 aWang, Wei1 aWestermann, Frank1 aWilley, James, C1 aWoods, Matthew1 aWu, Shujian1 aXiao, Nianqing1 aXu, Joshua1 aXu, Lei1 aYang, Lun1 aZeng, Xiao1 aZhang, Jialu1 aZhang, Li1 aZhang, Min1 aZhao, Chen1 aPuri, Raj, K1 aScherf, Uwe1 aTong, Weida1 aWolfinger, Russell, D uhttp://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html02245nas a2200217 4500008004100000245012100041210007100162260001300233300001100246490000700257520147000264100001901734700002201753700001801775700002201793700002001815700001901835700001601854700002301870856013401893 2010 eng d00aSIMAP–a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters.0 aSIMAP–a comprehensive database of precalculated protein sequence c2010 Jan aD223-60 v383 aThe prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the similarity matrix, the computation of the Similarity Matrix of Proteins (SIMAP) becomes a computational intensive task. The SIMAP database provides a comprehensive and up-to-date pre-calculation of the protein sequence similarity matrix, sequence-based features and sequence clusters. As of September 2009, SIMAP covers 48 million proteins and more than 23 million non-redundant sequences. Novel features of SIMAP include the expansion of the sequence space by including databases such as ENSEMBL as well as the integration of metagenomes based on their consistent processing and annotation. Furthermore, protein function predictions by Blast2GO are pre-calculated for all sequences in SIMAP and the data access and query functions have been improved. SIMAP assists biologists to query the up-to-date sequence space systematically and facilitates large-scale downstream projects in computational biology. Access to SIMAP is freely provided through the web portal for individuals (http://mips.gsf.de/simap/) and for programmatic access through DAS (http://webclu.bio.wzw.tum.de/das/) and Web-Service (http://mips.gsf.de/webservices/services/SimapService2.0?wsdl).
1 aRattei, Thomas1 aTischler, Patrick1 aGötz, Stefan1 aJehl, Marc-André1 aHoser, Jonathan1 aArnold, Roland1 aConesa, Ana1 aMewes, Hans-Werner uhttps://www.clinbioinfosspa.es/content/simap%E2%80%93-comprehensive-database-pre-calculated-protein-sequence-similarities-domains00816nas a2200265 4500008004100000022001400041245005600055210005500111300001100166490000700177100002100184700002300205700002100228700002100249700002300270700002300293700001800316700001500334700002000349700002300369700002700392700001600419700002100435856009400456 2009 eng d a1557-810000aModeling and managing experimental data using FuGE.0 aModeling and managing experimental data using FuGE a239-510 v131 aJones, Andrew, R1 aLister, Allyson, L1 aHermida, Leandro1 aWilkinson, Peter1 aEisenacher, Martin1 aBelhajjame, Khalid1 aGibson, Frank1 aLord, Phil1 aPocock, Matthew1 aRosenfelder, Heiko1 aSantoyo-López, Javier1 aWipat, Anil1 aPaton, Norman, W uhttps://www.clinbioinfosspa.es/content/modeling-and-managing-experimental-data-using-fuge00658nas a2200241 4500008004100000245004000041210003900081300001200120490000600132100001400138700001600152700001400168700001600182700001500198700001800213700001500231700002100246700002200267700001500289700002200304700001500326856007500341 2009 eng d00aPere Alberch: Originator of EvoDevo0 aPere Alberch Originator of EvoDevo a351-3530 v31 aReiss, JO1 aBurke, A, C1 aArcher, C1 aDe Renzi, M1 aDopazo, H.1 aEtxeberria, A1 aGale, E, A1 aHinchliffe, J, R1 ade la Rosa, Nuño1 aRose, C, S1 aRasskin-Gutman, D1 aMüller, G uhttps://www.clinbioinfosspa.es/content/pere-alberch-originator-evodevo00912nas a2200265 4500008004100000245010100041210006900142260000700211100002100218700002100239700002000260700002200280700001800302700002200320700002200342700002000364700002000384700001400404700001900418700001900437700001800456700002400474700001800498856013000516 2009 eng d00aPeripheral blood cells transcriptome to study new biomarkers for myocardial infarction follow up0 aPeripheral blood cells transcriptome to study new biomarkers for c061 aSilbiger, Vivian1 aLuchessi, André1 aHirata, Rosario1 aCarracedo, Ángel1 aBrión, Maria1 aNeto, Lidio, Lima1 aPastorelli, C, P.1 aDopazo, Joaquin1 aMontaner, David1 aGarcia, F1 aSampaio, M, P.1 aPereira, M, P.1 aSantos, E, S.1 aArmaganijan, Dikran1 aHirata, Mario uhttps://www.clinbioinfosspa.es/content/peripheral-blood-cells-transcriptome-study-new-biomarkers-myocardial-infarction-follow02659nas a2200253 4500008004100000245010800041210006900149300001000218490000700228520165100235653006101886653019201947100001402139700001302153700001302166700001802179700001702197700001502214700002002229700001602249700001502265700001902280856010602299 2008 eng d00aCLEAR-test: combining inference for differential expression and variability in microarray data analysis0 aCLEARtest combining inference for differential expression and va a33-450 v413 aA common goal of microarray experiments is to detect genes that are differentially expressed under distinct experimental conditions. Several statistical tests have been proposed to determine whether the observed changes in gene expression are significant. The t-test assigns a score to each gene on the basis of changes in its expression relative to its estimated variability, in such a way that genes with a higher score (in absolute values) are more likely to be significant. Most variants of the t-test use the complete set of genes to influence the variance estimate for each single gene. However, no inference is made in terms of the variability itself. Here, we highlight the problem of low observed variances in the t-test, when genes with relatively small changes are declared differentially expressed. Alternatively, the z-test could be used although, unlike the t-test, it can declare differentially expressed genes with high observed variances. To overcome this, we propose to combine the z-test, which focuses on large changes, with a chi(2) test to evaluate variability. We call this procedure CLEAR-test and we provide a combined p-value that offers a compromise between both aspects. Analysis of three publicly available microarray datasets reveals the greater performance of the CLEAR-test relative to the t-test and alternative methods. Finally, empirical and simulated data analyses demonstrate the greater reproducibility and statistical power of the CLEAR-test and z-test with respect to current alternative methods. In addition, the CLEAR-test improves the z-test by capturing reproducible genes with high variability.
10a*Algorithms Artificial Intelligence *Data Interpretation10aStatistical Gene Expression Profiling/*methods Gene Expression Regulation/*physiology Oligonucleotide Array Sequence Analysis/*methods Proteome/*metabolism Signal Transduction/*physiology1 aValls, J.1 aGrau, M.1 aSole, X.1 aHernandez, P.1 aMontaner, D.1 aDopazo, J.1 aPeinado, M., A.1 aCapella, G.1 aMoreno, V.1 aPujana, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1759700903014nas a2200229 4500008004100000245012600041210006900167300001200236490000700248520187400255653014702129653025902276100002302535700001602558700001502574700002002589700001802609700002002627700001702647700001402664856010602678 2008 eng d00aControlled ovarian stimulation induces a functional genomic delay of the endometrium with potential clinical implications0 aControlled ovarian stimulation induces a functional genomic dela a4500-100 v933 aCONTEXT: Controlled ovarian stimulation induces morphological, biochemical, and functional genomic modifications of the human endometrium during the window of implantation. OBJECTIVE: Our objective was to compare the gene expression profile of the human endometrium in natural vs. controlled ovarian stimulation cycles throughout the early-mid secretory transition using microarray technology. METHOD: Microarray data from 49 endometrial biopsies obtained from LH+1 to LH+9 (n=25) in natural cycles and from human chorionic gonadotropin (hCG) +1 to hCG+9 in controlled ovarian stimulation cycles (n=24) were analyzed using different methods, such as clustering, profiling of biological processes, and selection of differentially expressed genes, as implemented in Gene Expression Pattern Analysis Suite and Babelomics programs. RESULTS: Endometria from natural cycles followed different genomic patterns compared with controlled ovarian stimulation cycles in the transition from the pre-receptive (days LH/hCG+1 until LH/hCG+5) to the receptive phase (day LH+7/hCG+7). Specifically, we have demonstrated the existence of a 2-d delay in the activation/repression of two clusters composed by 218 and 133 genes, respectively, on day hCG+7 vs. LH+7. Many of these delayed genes belong to the class window of implantation genes affecting basic biological processes in the receptive endometrium. CONCLUSIONS: These results demonstrate that gene expression profiling of the endometrium is different between natural and controlled ovarian stimulation cycles in the receptive phase. Identification of these differentially regulated genes can be used to understand the different developmental profiles of receptive endometrium during controlled ovarian stimulation and to search for the best controlled ovarian stimulation treatment in terms of minimal endometrial impact.
10aAlgorithms Chorionic Gonadotropin/genetics Endometrium/cytology/pathology/*physiology/physiopathology Female Gene Expression Regulation Genome10aHuman Glutathione Peroxidase/genetics Humans Insulin-Like Growth Factor Binding Proteins/genetics Luteal Phase/physiology Luteinizing Hormone/genetics Menstrual Cycle Oligonucleotide Array Sequence Analysis Ovulation Induction/*methods RNA/genetics/isola1 aHorcajadas, J., A.1 aMinguez, P.1 aDopazo, J.1 aEsteban, F., J.1 aDominguez, F.1 aGiudice, L., C.1 aPellicer, A.1 aSimon, C. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1869787002205nas a2200301 4500008004100000245007600041210006900117300001200186490000700198520130100205653001001506653002901516100001601545700002001561700001801581700002101599700001601620700001501636700002401651700002301675700001401698700001601712700002201728700001501750700001701765700001501782856010601797 2008 eng d00aGEPAS, a web-based tool for microarray data analysis and interpretation0 aGEPAS a webbased tool for microarray data analysis and interpret aW308-140 v363 aGene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.
10agepas10amicroarray data analysis1 aTarraga, J.1 aMedina, Ignacio1 aCarbonell, J.1 aHuerta-Cepas, J.1 aMinguez, P.1 aAlloza, E.1 aAl-Shahrour, Fatima1 aVegas-Azcarate, S.1 aGoetz, S.1 aEscobar, P.1 aGarcia-Garcia, F.1 aConesa, A.1 aMontaner, D.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1850880602503nas a2200385 4500008004100000022001400041245007700055210006900132260001600201300001200217490000700229520130100236653002201537653003701559653003001596653001301626653001301639653004401652653001301696100002301709700002001732700002101752700002401773700001901797700001601816700002501832700002801857700001801885700001901903700002901922700001601951700002001967700002001987856011002007 2008 eng d a1362-496200aGEPAS, a web-based tool for microarray data analysis and interpretation.0 aGEPAS a webbased tool for microarray data analysis and interpret c2008 Jul 01 aW308-140 v363 aGene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.
10aComputer Graphics10aDose-Response Relationship, Drug10aGene Expression Profiling10aInternet10aKinetics10aOligonucleotide Array Sequence Analysis10aSoftware1 aTárraga, Joaquín1 aMedina, Ignacio1 aCarbonell, José1 aHuerta-Cepas, Jaime1 aMinguez, Pablo1 aAlloza, Eva1 aAl-Shahrour, Fátima1 aVegas-Azcárate, Susana1 aGoetz, Stefan1 aEscobar, Pablo1 aGarcia-Garcia, Francisco1 aConesa, Ana1 aMontaner, David1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/gepas-web-based-tool-microarray-data-analysis-and-interpretation-003690nas a2200937 4500008004100000022001400041245007800055210006900133260001300202300001100215490000600226520100100232653002601233653003201259653002301291653003801314653001301352653002601365653002401391110002301415700002301438700001901461700001801480700002501498700001801523700001901541700002101560700001601581700001601597700002901613700001701642700001901659700002201678700002501700700003101725700002501756700001601781700001901797700001601816700002001832700002601852700002501878700001901903700001901922700001801941700001901959700001401978700001901992700002002011700002002031700001702051700002002068700002102088700002402109700002102133700002102154700002102175700002202196700001802218700002002236700002302256700002402279700002502303700002002328700002002348700002002368700002002388700002202408700002002430700002302450700001702473700001602490700002702506700001802533700001802551700001902569700002002588700001802608700002302626856010302649 2008 eng d a1477-405400aInteroperability with Moby 1.0--it's better than sharing your toothbrush!0 aInteroperability with Moby 10its better than sharing your toothb c2008 May a220-310 v93 aThe BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.
10aComputational Biology10aDatabase Management Systems10aDatabases, Factual10aInformation Storage and Retrieval10aInternet10aProgramming Languages10aSystems Integration1 aBioMoby Consortium1 aWilkinson, Mark, D1 aSenger, Martin1 aKawas, Edward1 aBruskiewich, Richard1 aGouzy, Jerome1 aNoirot, Celine1 aBardou, Philippe1 aNg, Ambrose1 aHaase, Dirk1 aSaiz, Enrique, de Andres1 aWang, Dennis1 aGibbons, Frank1 aGordon, Paul, M K1 aSensen, Christoph, W1 aCarrasco, Jose, Manuel Rod1 aFernández, José, M1 aShen, Lixin1 aLinks, Matthew1 aNg, Michael1 aOpushneva, Nina1 aNeerincx, Pieter, B T1 aLeunissen, Jack, A M1 aErnst, Rebecca1 aTwigger, Simon1 aUsadel, Bjorn1 aGood, Benjamin1 aWong, Yan1 aStein, Lincoln1 aCrosby, William1 aKarlsson, Johan1 aRoyo, Romina1 aPárraga, Iván1 aRamírez, Sergio1 aGelpi, Josep, Lluis1 aTrelles, Oswaldo1 aPisano, David, G1 aJimenez, Natalia1 aKerhornou, Arnaud1 aRosset, Roman1 aZamacola, Leire1 aTárraga, Joaquín1 aHuerta-Cepas, Jaime1 aCarazo, Jose, María1 aDopazo, Joaquin1 aGuigó, Roderic1 aNavarro, Arcadi1 aOrozco, Modesto1 aValencia, Alfonso1 aClaros, Gonzalo1 aPérez, Antonio, J1 aAldana, Jose1 aRojano, Mar1 aCruz, Raul, Fernandez-1 aNavas, Ismael1 aSchiltz, Gary1 aFarmer, Andrew1 aGessler, Damian1 aSchoof, Heiko1 aGroscurth, Andreas uhttps://www.clinbioinfosspa.es/content/interoperability-moby-10-its-better-sharing-your-toothbrush03310nas a2200841 4500008004100000245008100041210007100122300001100193490000600204520100100210653007501211653010801286100002201394700001501416700001401431700002001445700001401465700001501479700001501494700001101509700001401520700001401534700001301548700001601561700001901577700001901596700002101615700002201636700001301658700001401671700001101685700001801696700002101714700002201735700001401757700001601771700001501787700001301802700001301815700001401828700001501842700001701857700001301874700001601887700001601903700001801919700001601937700001901953700001601972700001801988700001502006700001702021700001602038700002102054700001902075700001502094700001402109700001602123700001502139700001702154700001902171700001802190700001502208700001902223700002602242700001402268700001602282700001502298700001602313700001502329700001802344856010602362 2008 eng d00aInteroperability with Moby 1.0–it’s better than sharing your toothbrush!0 aInteroperability with Moby 10–it s better than sharing your toot a220-310 v93 aThe BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.
10aComputational Biology/*methods *Database Management Systems *Databases10aFactual Information Storage and Retrieval/*methods *Internet *Programming Languages Systems Integration1 aWilkinson, M., D.1 aSenger, M.1 aKawas, E.1 aBruskiewich, R.1 aGouzy, J.1 aNoirot, C.1 aBardou, P.1 aNg, A.1 aHaase, D.1 aEde, Saiz1 aWang, D.1 aGibbons, F.1 aGordon, P., M.1 aSensen, C., W.1 aCarrasco, J., M.1 aFernandez, J., M.1 aShen, L.1 aLinks, M.1 aNg, M.1 aOpushneva, N.1 aNeerincx, P., B.1 aLeunissen, J., A.1 aErnst, R.1 aTwigger, S.1 aUsadel, B.1 aGood, B.1 aWong, Y.1 aStein, L.1 aCrosby, W.1 aKarlsson, J.1 aRoyo, R.1 aParraga, I.1 aRamirez, S.1 aGelpi, J., L.1 aTrelles, O.1 aPisano, D., G.1 aJimenez, N.1 aKerhornou, A.1 aRosset, R.1 aZamacola, L.1 aTarraga, J.1 aHuerta-Cepas, J.1 aCarazo, J., M.1 aDopazo, J.1 aGuigo, R.1 aNavarro, A.1 aOrozco, M.1 aValencia, A.1 aClaros, M., G.1 aPerez, A., J.1 aAldana, J.1 aRojano, M., M.1 aCruz, Fernandez-Santa1 aNavas, I.1 aSchiltz, G.1 aFarmer, A.1 aGessler, D.1 aSchoof, H.1 aGroscurth, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1823880402003nas a2200181 4500008004100000245007400041210006900115300001100184490000700195520123100202653013101433653008301564100002101647700001401668700001501682700001801697856010601715 2008 eng d00aPhylomeDB: a database for genome-wide collections of gene phylogenies0 aPhylomeDB a database for genomewide collections of gene phylogen aD491-60 v363 aThe complete collection of evolutionary histories of all genes in a genome, also known as phylome, constitutes a valuable source of information. The reconstruction of phylomes has been previously prevented by large demands of time and computer power, but is now feasible thanks to recent developments in computers and algorithms. To provide a publicly available repository of complete phylomes that allows researchers to access and store large-scale phylogenomic analyses, we have developed PhylomeDB. PhylomeDB is a database of complete phylomes derived for different genomes within a specific taxonomic range. All phylomes in the database are built using a high-quality phylogenetic pipeline that includes evolutionary model testing and alignment trimming phases. For each genome, PhylomeDB provides the alignments, phylogentic trees and tree-based orthology predictions for every single encoded protein. The current version of PhylomeDB includes the phylomes of Human, the yeast Saccharomyces cerevisiae and the bacterium Escherichia coli, comprising a total of 32 289 seed sequences with their corresponding alignments and 172 324 phylogenetic trees. PhylomeDB can be publicly accessed at http://phylomedb.bioinfo.cipf.es.10aAncient Humans *Phylogeny Proteins/classification/genetics Saccharomyces cerevisiae/classification/genetics Sequence Alignment10aBase Sequence Escherichia coli/classification/genetics Genes *Genomics History1 aHuerta-Cepas, J.1 aBueno, A.1 aDopazo, J.1 aGabaldón, T. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1796229702130nas a2200301 4500008004100000022001400041245007500055210006900130260001300199300001100212490000700223520123800230653001801468653002101486653001001507653001301517653002101530653001101551653001401562653001301576653002901589653002301618100002401641700001801665700002001683700002001703856010501723 2008 eng d a1362-496200aPhylomeDB: a database for genome-wide collections of gene phylogenies.0 aPhylomeDB a database for genomewide collections of gene phylogen c2008 Jan aD491-60 v363 aThe complete collection of evolutionary histories of all genes in a genome, also known as phylome, constitutes a valuable source of information. The reconstruction of phylomes has been previously prevented by large demands of time and computer power, but is now feasible thanks to recent developments in computers and algorithms. To provide a publicly available repository of complete phylomes that allows researchers to access and store large-scale phylogenomic analyses, we have developed PhylomeDB. PhylomeDB is a database of complete phylomes derived for different genomes within a specific taxonomic range. All phylomes in the database are built using a high-quality phylogenetic pipeline that includes evolutionary model testing and alignment trimming phases. For each genome, PhylomeDB provides the alignments, phylogentic trees and tree-based orthology predictions for every single encoded protein. The current version of PhylomeDB includes the phylomes of Human, the yeast Saccharomyces cerevisiae and the bacterium Escherichia coli, comprising a total of 32 289 seed sequences with their corresponding alignments and 172 324 phylogenetic trees. PhylomeDB can be publicly accessed at http://phylomedb.bioinfo.cipf.es.
10aBase Sequence10aEscherichia coli10aGenes10aGenomics10aHistory, Ancient10aHumans10aPhylogeny10aProteins10aSaccharomyces cerevisiae10aSequence Alignment1 aHuerta-Cepas, Jaime1 aBueno, Anibal1 aDopazo, Joaquin1 aGabaldón, Toni uhttps://www.clinbioinfosspa.es/content/phylomedb-database-genome-wide-collections-gene-phylogenies-003454nas a2200877 4500008004100000022001400041245006300055210006200118260001300180300001000193490000700203520101900210653001201229653002301241653002301264653001101287653001501298653002701313653001401340653003601354653002801390653000901418653002501427653002701452110002001479700001801499700001701517700003001534700001701564700002401581700001501605700001801620700002401638700002001662700001701682700001801699700001901717700001601736700002401752700002001776700001901796700002101815700001901836700001601855700001701871700001901888700001801907700001701925700002201942700001701964700001901981700001802000700002302018700001802041700002002059700002002079700001802099700002102117700003402138700002202172700002102194700002302215700002202238700002302260700002002283700002002303700002202323700002202345700002002367700002002387700001802407700002002425700001902445700002002464856009202484 2008 eng d a1546-171800aSNP and haplotype mapping for genetic analysis in the rat.0 aSNP and haplotype mapping for genetic analysis in the rat c2008 May a560-60 v403 aThe laboratory rat is one of the most extensively studied model organisms. Inbred laboratory rat strains originated from limited Rattus norvegicus founder populations, and the inherited genetic variation provides an excellent resource for the correlation of genotype to phenotype. Here, we report a survey of genetic variation based on almost 3 million newly identified SNPs. We obtained accurate and complete genotypes for a subset of 20,238 SNPs across 167 distinct inbred rat strains, two rat recombinant inbred panels and an F2 intercross. Using 81% of these SNPs, we constructed high-density genetic maps, creating a large dataset of fully characterized SNPs for disease gene mapping. Our data characterize the population structure and illustrate the degree of linkage disequilibrium. We provide a detailed SNP map and demonstrate its utility for mapping of quantitative trait loci. This community resource is openly available and augments the genetic tools for this workhorse of physiological studies.
10aAnimals10aChromosome Mapping10aDatabases, Genetic10aGenome10aHaplotypes10aLinkage Disequilibrium10aPhylogeny10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci10aRats10aRats, Inbred Strains10aRecombination, Genetic1 aSTAR Consortium1 aSaar, Kathrin1 aBeck, Alfred1 aBihoreau, Marie-Thérèse1 aBirney, Ewan1 aBrocklebank, Denise1 aChen, Yuan1 aCuppen, Edwin1 aDemonchy, Stephanie1 aDopazo, Joaquin1 aFlicek, Paul1 aFoglio, Mario1 aFujiyama, Asao1 aGut, Ivo, G1 aGauguier, Dominique1 aGuigó, Roderic1 aGuryev, Victor1 aHeinig, Matthias1 aHummel, Oliver1 aJahn, Niels1 aKlages, Sven1 aKren, Vladimir1 aKube, Michael1 aKuhl, Heiner1 aKuramoto, Takashi1 aKuroki, Yoko1 aLechner, Doris1 aLee, Young-Ae1 aLopez-Bigas, Nuria1 aLathrop, Mark1 aMashimo, Tomoji1 aMedina, Ignacio1 aMott, Richard1 aPatone, Giannino1 aPerrier-Cornet, Jeanne-Antide1 aPlatzer, Matthias1 aPravenec, Michal1 aReinhardt, Richard1 aSakaki, Yoshiyuki1 aSchilhabel, Markus1 aSchulz, Herbert1 aSerikawa, Tadao1 aShikhagaie, Medya1 aTatsumoto, Shouji1 aTaudien, Stefan1 aToyoda, Atsushi1 aVoigt, Birger1 aZelenika, Diana1 aZimdahl, Heike1 aHubner, Norbert uhttps://www.clinbioinfosspa.es/content/snp-and-haplotype-mapping-genetic-analysis-rat-003097nas a2200757 4500008004100000245006200041210006200103300001000165490000700175520101900182653004201201653001201243653007801255653004301333653006701376100001301443700001301456700002101469700001501490700002001505700001301525700001501538700001701553700001501570700001501585700001501600700001701615700001601632700001701648700001401665700001501679700001501694700001501709700001301724700001501737700001301752700001301765700001301778700001701791700001501808700001601823700001601839700002001855700002001875700001601895700002001911700001301931700001501944700002701959700001601986700001702002700001802019700001502037700001902052700001502071700001702086700001902103700001802122700001602140700001502156700001402171700001702185700001602202700001502218856010602233 2008 eng d00aSNP and haplotype mapping for genetic analysis in the rat0 aSNP and haplotype mapping for genetic analysis in the rat a560-60 v403 aThe laboratory rat is one of the most extensively studied model organisms. Inbred laboratory rat strains originated from limited Rattus norvegicus founder populations, and the inherited genetic variation provides an excellent resource for the correlation of genotype to phenotype. Here, we report a survey of genetic variation based on almost 3 million newly identified SNPs. We obtained accurate and complete genotypes for a subset of 20,238 SNPs across 167 distinct inbred rat strains, two rat recombinant inbred panels and an F2 intercross. Using 81% of these SNPs, we constructed high-density genetic maps, creating a large dataset of fully characterized SNPs for disease gene mapping. Our data characterize the population structure and illustrate the degree of linkage disequilibrium. We provide a detailed SNP map and demonstrate its utility for mapping of quantitative trait loci. This community resource is openly available and augments the genetic tools for this workhorse of physiological studies.
10aAnimals Chromosome Mapping *Databases10aGenetic10aGenetic Genome *Haplotypes Linkage Disequilibrium Phylogeny *Polymorphism10aInbred Strains/*genetics Recombination10aSingle Nucleotide *Quantitative Trait Loci Rats/*genetics Rats1 aSaar, K.1 aBeck, A.1 aBihoreau, M., T.1 aBirney, E.1 aBrocklebank, D.1 aChen, Y.1 aCuppen, E.1 aDemonchy, S.1 aDopazo, J.1 aFlicek, P.1 aFoglio, M.1 aFujiyama, A.1 aGut, I., G.1 aGauguier, D.1 aGuigo, R.1 aGuryev, V.1 aHeinig, M.1 aHummel, O.1 aJahn, N.1 aKlages, S.1 aKren, V.1 aKube, M.1 aKuhl, H.1 aKuramoto, T.1 aKuroki, Y.1 aLechner, D.1 aLee, Y., A.1 aLopez-Bigas, N.1 aLathrop, G., M.1 aMashimo, T.1 aMedina, Ignacio1 aMott, R.1 aPatone, G.1 aPerrier-Cornet, J., A.1 aPlatzer, M.1 aPravenec, M.1 aReinhardt, R.1 aSakaki, Y.1 aSchilhabel, M.1 aSchulz, H.1 aSerikawa, T.1 aShikhagaie, M.1 aTatsumoto, S.1 aTaudien, S.1 aToyoda, A.1 aVoigt, B.1 aZelenika, D.1 aZimdahl, H.1 aHubner, N. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1844359403447nas a2200253 4500008004100000245010700041210006900148300001200217490000700229520238400236653019002620653008902810100001402899700002402913700001702937700001602954700001502970700001602985700002403001700002603025700001703051700001903068856010603087 2008 eng d00aTime course profiling of the retinal transcriptome after optic nerve transection and optic nerve crush0 aTime course profiling of the retinal transcriptome after optic n a1050-630 v143 aPURPOSE: A time-course analysis of gene regulation in the adult rat retina after intraorbital nerve crush (IONC) and intraorbital nerve transection (IONT). METHODS: RNA was extracted from adult rat retinas undergoing either IONT or IONC at increasing times post-lesion. Affymetrix RAE230.2 arrays were hybridized and analyzed. Statistically regulated genes were annotated and functionally clustered. Arrays were validated by means of quantative reverse transcription polymerase chain reaction (qRT-PCR) on ten regulated genes at two times post-lesion. Western blotting and immunohistofluorescence for four pro-apoptotic proteins were performed on naive and injured retinas. Finally, custom signaling maps for IONT- and IONC-induced death response were generated (MetaCore, Genego Inc.). RESULTS: Here we show that over time, 3,219 sequences were regulated after IONT and 1,996 after IONC. Out of the total of regulated sequences, 1,078 were commonly regulated by both injuries. Interestingly, while IONT mainly triggers a gene upregulation-sustained over time, IONC causes a transitory downregulation. Functional clustering identified the regulation of high interest biologic processes, most importantly cell death wherein apoptosis was the most significant cluster. Ten death-related genes upregulated by both injuries were used for array validation by means of qRT-PCR. In addition, western blotting and immunohistofluorescence of total and active Caspase 3 (Casp3), tumor necrosis factor receptor type 1 associated death domain (TRADD), tumor necrosis factor receptor superfamily member 1a (TNFR1a), and c-fos were performed to confirm their protein regulation and expression pattern in naive and injured retinas. These analyses demonstrated that for these genes, protein regulation followed transcriptional regulation and that these pro-apoptotic proteins were expressed by retinal ganglion cells (RGCs). MetaCore-based death-signaling maps show that several apoptotic cascades were regulated in the retina following optic nerve injury and highlight the similarities and differences between IONT and IONC in cell death profiling. CONCLUSIONS: This comprehensive time course retinal transcriptome study comparing IONT and IONC lesions provides a unique valuable tool to understand the molecular mechanisms underlying optic nerve injury and to design neuroprotective protocols.10aAnimals Cell Death Cluster Analysis Female *Gene Expression Profiling Gene Expression Regulation *Nerve Crush Optic Nerve/*metabolism/*pathology Optic Nerve Injuries/*genetics Rats Rats10aSprague-Dawley Reproducibility of Results Retina/*metabolism/*pathology Time Factors1 aAgudo, M.1 aPerez-Marin, M., C.1 aLonngren, U.1 aSobrado, P.1 aConesa, A.1 aCanovas, I.1 aSalinas-Navarro, M.1 aMiralles-Imperial, J.1 aHallbook, F.1 aVidal-Sanz, M. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1855298002515nas a2200289 4500008004100000245007500041210006900116300001200185490000800197520143200205653002101637653001201658653007101670653010001741653010001841100002301941700001401964700001501978700001801993700002202011700002002033700001602053700001702069700001302086700002002099856010602119 2008 eng d00aTranscriptional profiling of mRNA expression in the mouse distal colon0 aTranscriptional profiling of mRNA expression in the mouse distal a2019-290 v1353 aBACKGROUND & AIMS: Intestinal epithelial cells and the myenteric plexus of the mouse gastrointestinal tract contain a circadian clock-based intrinsic time-keeping system. Because disruption of the biological clock has been associated with increased susceptibility to colon cancer and gastrointestinal symptoms, we aimed to identify rhythmically expressed genes in the mouse distal colon. METHODS: Microarray analysis was used to identify genes that were rhythmically expressed over a 24-hour light/dark cycle. The transcripts were then classified according to expression pattern, function, and association with physiologic and pathophysiologic processes of the colon. RESULTS: A circadian gene expression pattern was detected in approximately 3.7% of distal colonic genes. A large percentage of these genes were involved in cell signaling, differentiation, and proliferation and cell death. Of all the rhythmically expressed genes in the mouse colon, approximately 7% (64/906) have been associated with colorectal cancer formation (eg, B-cell leukemia/lymphoma-2 [Bcl2]) and 1.8% (18/906) with various colonic functions such as motility and secretion (eg, vasoactive intestinal polypeptide, cystic fibrosis transmembrane conductance regulator). CONCLUSIONS: A subset of genes in the murine colon follows a rhythmic expression pattern. These findings may have significant implications for colonic physiology and pathophysiology.10aAnimals Blotting10aGenetic10aInbred C57BL Microarray Analysis Proteins/*genetics/metabolism RNA10aMessenger/biosynthesis/*genetics Reverse Transcriptase Polymerase Chain Reaction *Transcription10aWestern Cell Proliferation Circadian Rhythm/*genetics Colon/cytology/*metabolism Male Mice Mice1 aHoogerwerf, W., A.1 aSinha, M.1 aConesa, A.1 aLuxon, B., A.1 aShahinian, V., B.1 aCornelissen, G.1 aHalberg, F.1 aBostwick, J.1 aTimm, J.1 aCassone, V., M. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1884855703144nas a2200277 4500008004100000245020600041210006900247300001200316490000700328520162000335653020401955653006002159653012202219653025902341100001602600700001502616700001502631700001402646700001502660700001802675700001802693700001702711700001702728700001502745856010602760 2008 eng d00aTranscriptome analysis provides new insights into liver changes induced in the rat upon dietary administration of the food additives butylated hydroxytoluene, curcumin, propyl gallate and thiabendazole0 aTranscriptome analysis provides new insights into liver changes a2616-280 v463 aTranscriptomics was performed to gain insight into mechanisms of food additives butylated hydroxytoluene (BHT), curcumin (CC), propyl gallate (PG), and thiabendazole (TB), additives for which interactions in the liver can not be excluded. Additives were administered in diets for 28 days to Sprague-Dawley rats and cDNA microarray experiments were performed on hepatic RNA. BHT induced changes in the expression of 10 genes, including phase I (CYP2B1/2; CYP3A9; CYP2C6) and phase II metabolism (GST mu2). The CYP2B1/2 and GST expression findings were confirmed by real time RT-PCR, western blotting, and increased GST activity towards DCNB. CC altered the expression of 12 genes. Three out of these were related to peroxisomes (phytanoyl-CoA dioxygenase, enoyl-CoA hydratase; CYP4A3). Increased cyanide insensitive palmitoyl-CoA oxidation was observed, suggesting that CC is a weak peroxisome proliferator. TB changed the expression of 12 genes, including CYP1A2. In line, CYP1A2 protein expression was increased. The expression level of five genes, associated with p53 was found to change upon TB treatment, including p53 itself, GADD45alpha, DN-7, protein kinase C beta and serum albumin. These array experiments led to the novel finding that TB is capable of inducing p53 at the protein level, at least at the highest dose levels employed above the current NOAEL. The expression of eight genes changed upon PG administration. This study shows the value of gene expression profiling in food toxicology in terms of generating novel hypotheses on the mechanisms of action of food additives in relation to pathology.10aAnimals Aryl Hydrocarbon Hydroxylases/metabolism Body Weight/drug effects Butylated Hydroxytoluene/toxicity Curcumin/toxicity Cytochrome P-450 CYP1A2/metabolism Cytochrome P-450 CYP2B1/metabolism DNA10aComplementary/biosynthesis/genetics Data Interpretation10aSprague-Dawley Reverse Transcriptase Polymerase Chain Reaction Steroid Hydroxylases/metabolism Thiabendazole/toxicity10aStatistical *Diet Food Additives/*toxicity Gene Expression/drug effects *Gene Expression Profiling Glutathione Transferase/metabolism Liver/*drug effects Male Organ Size/drug effects Oxidation-Reduction Palmitoyl Coenzyme A/metabolism Propyl Gallate/toxi1 aStierum, R.1 aConesa, A.1 aHeijne, W.1 aOmmen, B.1 aJunker, K.1 aScott, M., P.1 aPrice, R., J.1 aMeredith, C.1 aLake, B., G.1 aGroten, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1853937702714nas a2200253 4500008004100000245009200041210006900133300001300202490000700215520168800222653010801910653001202018653001902030653005802049653012102107100001802228700001502246700002302261700002202284700001902306700001402325700001502339856010602354 2007 eng d00aDiscovering gene expression patterns in time course microarray experiments by ANOVA-SCA0 aDiscovering gene expression patterns in time course microarray e a1792-8000 v233 aMOTIVATION: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. RESULTS: In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. AVAILABILITY: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.10aAlgorithms *Analysis of Variance Computational Biology/*methods Computer Simulation Data Interpretation10aGenetic10aGenetic Models10aStatistical Gene Expression Profiling/*methods Models10aStatistical Oligonucleotide Array Sequence Analysis/*methods Principal Component Analysis Time Factors Transcription1 aNueda, M., J.1 aConesa, A.1 aWesterhuis, J., A.1 aHoefsloot, H., C.1 aSmilde, A., K.1 aTalon, M.1 aFerrer, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1751925002784nas a2200301 4500008004100000245006900041210006800110300000800178490000600186520165900192653002501851653002201876653001901898653006101917653007001978653003402048653013602082100001802218700002102236700001702257700002402274700001402298700001402312700001602326700001502342700001902357856010602376 2007 eng d00aEvidence for systems-level molecular mechanisms of tumorigenesis0 aEvidence for systemslevel molecular mechanisms of tumorigenesis a1850 v83 aBACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth. RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as "CGPs") defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis. CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.10a*Cell Transformation10aBiological Models10aGenetic Models10aMessenger/metabolism Signal Transduction Systems Biology10aNeoplastic *Gene Expression Profiling *Gene Expression Regulation10aNeoplastic Humans Male Models10aStatistical Neoplasm Proteins/*physiology Neoplasms/etiology/*genetics Prostatic Neoplasms/genetics Protein Interaction Mapping RNA1 aHernandez, P.1 aHuerta-Cepas, J.1 aMontaner, D.1 aAl-Shahrour, Fatima1 aValls, J.1 aGomez, L.1 aCapella, G.1 aDopazo, J.1 aPujana, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1758491502990nas a2200421 4500008004100000022001400041245007000055210006800125260001600193300000800209490000600217520169900223653003601922653003001958653004301988653001102031653000902042653002302051653002002074653002402094653002202118653001402140653002402154653003202178653001902210653002402229653002002253100002202273700002402295700002002319700002502339700001602364700001702380700002202397700002002419700002602439856010302465 2007 eng d a1471-216400aEvidence for systems-level molecular mechanisms of tumorigenesis.0 aEvidence for systemslevel molecular mechanisms of tumorigenesis c2007 Jun 20 a1850 v83 aBACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth.
RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as "CGPs") defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis.
CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.
10aCell Transformation, Neoplastic10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aHumans10aMale10aModels, Biological10aModels, Genetic10aModels, Statistical10aNeoplasm Proteins10aNeoplasms10aProstatic Neoplasms10aProtein Interaction Mapping10aRNA, Messenger10aSignal Transduction10aSystems biology1 aHernández, Pilar1 aHuerta-Cepas, Jaime1 aMontaner, David1 aAl-Shahrour, Fátima1 aValls, Joan1 aGómez, Laia1 aCapellà, Gabriel1 aDopazo, Joaquin1 aPujana, Miguel, Angel uhttps://www.clinbioinfosspa.es/content/evidence-systems-level-molecular-mechanisms-tumorigenesis-002090nas a2200169 4500008004100000245011600041210006900157300000900226490000600235520123000241653007701471653008501548653014401633100001801777700001901795856010601814 2007 eng d00aFrom endosymbiont to host-controlled organelle: the hijacking of mitochondrial protein synthesis and metabolism0 aFrom endosymbiont to hostcontrolled organelle the hijacking of m ae2190 v33 aMitochondria are eukaryotic organelles that originated from the endosymbiosis of an alpha-proteobacterium. To gain insight into the evolution of the mitochondrial proteome as it proceeded through the transition from a free-living cell to a specialized organelle, we compared a reconstructed ancestral proteome of the mitochondrion with the proteomes of alpha-proteobacteria as well as with the mitochondrial proteomes in yeast and man. Overall, there has been a large turnover of the mitochondrial proteome during the evolution of mitochondria. Early in the evolution of the mitochondrion, proteins involved in cell envelope synthesis have virtually disappeared, whereas proteins involved in replication, transcription, cell division, transport, regulation, and signal transduction have been replaced by eukaryotic proteins. More than half of what remains from the mitochondrial ancestor in modern mitochondria corresponds to translation, including post-translational modifications, and to metabolic pathways that are directly, or indirectly, involved in energy conversion. Altogether, the results indicate that the eukaryotic host has hijacked the proto-mitochondrion, taking control of its protein synthesis and metabolism.10aComputer Simulation DNA Mutational Analysis/methods Evolution *Evolution10aGenetic Organelles/physiology Protein Biosynthesis/*genetics Symbiosis/*genetics10aMolecular Fungal Proteins/*physiology Genetic Variation/genetics Humans Mitochondria/*physiology Mitochondrial Proteins/*physiology *Models1 aGabaldón, T.1 aHuynen, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1798326502519nas a2200337 4500008004100000022001400041245007300055210006900128260001600197300000800213490000600221520148900227653001501716653002301731653002401754653003001778653002301808653002101831653002401852653001301876653002001889653002801909100002501937700002101962700002001983700002402003700001902027700002002046700002002066856009502086 2007 eng d a1471-210500aFrom genes to functional classes in the study of biological systems.0 aFrom genes to functional classes in the study of biological syst c2007 Apr 03 a1140 v83 aBACKGROUND: With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.
RESULTS: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.
CONCLUSION: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.
10aAlgorithms10aChromosome Mapping10aComputer Simulation10aGene Expression Profiling10aModels, Biological10aMultigene Family10aSignal Transduction10aSoftware10aSystems biology10aUser-Computer Interface1 aAl-Shahrour, Fátima1 aArbiza, Leonardo1 aDopazo, Hernán1 aHuerta-Cepas, Jaime1 aMinguez, Pablo1 aMontaner, David1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/genes-functional-classes-study-biological-systems-002367nas a2200229 4500008004100000245007200041210006900113300000800182490000600190520145600196653010501652653001501757653013601772100002401908700001501932700001501947700002101962700001601983700001701999700001502016856010602031 2007 eng d00aFrom genes to functional classes in the study of biological systems0 aFrom genes to functional classes in the study of biological syst a1140 v83 aBACKGROUND: With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed. RESULTS: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics. CONCLUSION: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.
10aAlgorithms Chromosome Mapping/*methods Computer Simulation Gene Expression Profiling/methods *Models10ababelomics10aBiological Multigene Family/*physiology Signal Transduction/*physiology *Software Systems Biology/*methods *User-Computer Interface1 aAl-Shahrour, Fatima1 aArbiza, L.1 aDopazo, H.1 aHuerta-Cepas, J.1 aMinguez, P.1 aMontaner, D.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1740759602363nas a2200193 4500008004100000245002200041210001800063300000900081490000600090520176400096653003301860653000801893653009301901100002101994700001502015700001502030700001802045856010602063 2007 eng d00aThe human phylome0 ahuman phylome aR1090 v83 aBACKGROUND: Phylogenomics analyses serve to establish evolutionary relationships among organisms and their genes. A phylome, the complete collection of all gene phylogenies in a genome, constitutes a valuable source of information, but its use in large genomes still constitutes a technical challenge. The use of phylomes also requires the development of new methods that help us to interpret them. RESULTS: We reconstruct here the human phylome, which includes the evolutionary relationships of all human proteins and their homologs among 39 fully sequenced eukaryotes. Phylogenetic techniques used include alignment trimming, branch length optimization, evolutionary model testing and maximum likelihood and Bayesian methods. Although differences with alternative topologies are minor, most of the trees support the Coelomata and Unikont hypotheses as well as the grouping of primates with laurasatheria to the exclusion of rodents. We assess the extent of gene duplication events and their relationship with the functional roles of the protein families involved. We find support for at least one, and probably two, rounds of whole genome duplications before vertebrate radiation. Using a novel algorithm that is independent from a species phylogeny, we derive orthology and paralogy relationships of human proteins among eukaryotic genomes. CONCLUSION: Topological variations among phylogenies for different genes are to be expected, highlighting the danger of gene-sampling effects in phylogenomic analyses. Several links can be established between the functions of gene families duplicated at certain phylogenetic splits and major evolutionary transitions in those lineages. The pipeline implemented here can be easily adapted for use in other organisms.10aAnimals *Evolution Evolution10aDNA10aMolecular Gene Duplication *Genome Humans *Phylogeny Proteins/genetics Sequence Analysis1 aHuerta-Cepas, J.1 aDopazo, H.1 aDopazo, J.1 aGabaldón, T. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1756792402580nas a2200253 4500008004100000245009100041210006900132300001200201490000700213520154400220653002301764653025501787100001702042700001702059700002502076700001802101700002102119700002002140700001302160700002002173700001302193700001402206856010602220 2007 eng d00aPeroxisomeDB: a database for the peroxisomal proteome, functional genomics and disease0 aPeroxisomeDB a database for the peroxisomal proteome functional aD815-220 v353 aPeroxisomes are essential organelles of eukaryotic origin, ubiquitously distributed in cells and organisms, playing key roles in lipid and antioxidant metabolism. Loss or malfunction of peroxisomes causes more than 20 fatal inherited conditions. We have created a peroxisomal database (http://www.peroxisomeDB.org) that includes the complete peroxisomal proteome of Homo sapiens and Saccharomyces cerevisiae, by gathering, updating and integrating the available genetic and functional information on peroxisomal genes. PeroxisomeDB is structured in interrelated sections ’Genes’, ’Functions’, ’Metabolic pathways’ and ’Diseases’, that include hyperlinks to selected features of NCBI, ENSEMBL and UCSC databases. We have designed graphical depictions of the main peroxisomal metabolic routes and have included updated flow charts for diagnosis. Precomputed BLAST, PSI-BLAST, multiple sequence alignment (MUSCLE) and phylogenetic trees are provided to assist in direct multispecies comparison to study evolutionary conserved functions and pathways. Highlights of the PeroxisomeDB include new tools developed for facilitating (i) identification of novel peroxisomal proteins, by means of identifying proteins carrying peroxisome targeting signal (PTS) motifs, (ii) detection of peroxisomes in silico, particularly useful for screening the deluge of newly sequenced genomes. PeroxisomeDB should contribute to the systematic characterization of the peroxisomal proteome and facilitate system biology approaches on the organelle.10aAnimals *Databases10aProtein Genomics Humans Internet Mice Peroxisomal Disorders/*genetics Peroxisomes/*metabolism Protein Sorting Signals Proteome/chemistry/*genetics/*physiology Rats Saccharomyces cerevisiae Proteins/genetics/physiology Software User-Computer Interface1 aSchluter, A.1 aFourcade, S.1 aDomenech-Estevez, E.1 aGabaldón, T.1 aHuerta-Cepas, J.1 aBerthommier, G.1 aRipp, R.1 aWanders, R., J.1 aPoch, O.1 aPujol, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1713519002437nas a2200265 4500008004100000245009200041210006900133300001100202490000700213520143600220653005301656653002601709653002201735653005701757653004501814653008601859100001601945700002001961700001501981700002101996700001802017700001502035700001502050856010602065 2007 eng d00aPhylemon: a suite of web tools for molecular evolution, phylogenetics and phylogenomics0 aPhylemon a suite of web tools for molecular evolution phylogenet aW38-420 v353 aPhylemon is an online platform for phylogenetic and evolutionary analyses of molecular sequence data. It has been developed as a web server that integrates a suite of different tools selected among the most popular stand-alone programs in phylogenetic and evolutionary analysis. It has been conceived as a natural response to the increasing demand of data analysis of many experimental scientists wishing to add a molecular evolution and phylogenetics insight into their research. Tools included in Phylemon cover a wide yet selected range of programs: from the most basic for multiple sequence alignment to elaborate statistical methods of phylogenetic reconstruction including methods for evolutionary rates analyses and molecular adaptation. Phylemon has several features that differentiates it from other resources: (i) It offers an integrated environment that enables the direct concatenation of evolutionary analyses, the storage of results and handles required data format conversions, (ii) Once an outfile is produced, Phylemon suggests the next possible analyses, thus guiding the user and facilitating the integration of multi-step analyses, and (iii) users can define and save complete pipelines for specific phylogenetic analysis to be automatically used on many genes in subsequent sessions or multiple genes in a single session (phylogenomics). The Phylemon web server is available at http://phylemon.bioinfo.cipf.es.10aAnimals Computational Biology/*methods Databases10aDNA Sequence Analysis10aGenetic Evolution10aMolecular Genetic Techniques Humans *Internet Models10aProtein Software User-Computer Interface10aStatistical *Phylogeny Programming Languages Sequence Alignment Sequence Analysis1 aTarraga, J.1 aMedina, Ignacio1 aArbiza, L.1 aHuerta-Cepas, J.1 aGabaldón, T.1 aDopazo, J.1 aDopazo, H. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1745234600409nas a2200109 4500008004100000245004200041210004200083260002400125100001800149700001900167856011300186 2007 eng d00aReconstruction of ancestral proteomes0 aReconstruction of ancestral proteomes aOxfordbD. Liberles1 aGabaldón, T.1 aHuynen, M., A. uhttp://www.us.oup.com/us/catalog/general/subject/LifeSciences/EvolutionaryBiology/?view=usa&ci=978019929918802376nas a2200229 4500008004100000245009300041210006900134300001200203490000800215520165600223653001501879100001701894700001301911700001501924700001801939700001701957700001901974700001801993700001502011700001402026856010602040 2006 eng d00aBlast2GO goes grid: developing a grid-enabled prototype for functional genomics analysis0 aBlast2GO goes grid developing a gridenabled prototype for functi a194-2040 v1203 aThe vast amount in complexity of data generated in Genomic Research implies that new dedicated and powerful computational tools need to be developed to meet their analysis requirements. Blast2GO (B2G) is a bioinformatics tool for Gene Ontology-based DNA or protein sequence annotation and function-based data mining. The application has been developed with the aim of affering an easy-to-use tool for functional genomics research. Typical B2G users are middle size genomics labs carrying out sequencing, ETS and microarray projects, handling datasets up to several thousand sequences. In the current version of B2G. The power and analytical potential of both annotation and function data-mining is somehow restricted to the computational power behind each particular installation. In order to be able to offer the possibility of an enhanced computational capacity within this bioinformatics application, a Grid component is being developed. A prototype has been conceived for the particular problem of speeding up the Blast searches to obtain fast results for large datasets. Many efforts have been done in the literature concerning the speeding up of Blast searches, but few of them deal with the use of large heterogeneous production Grid Infrastructures. These are the infrastructures that could reach the largest number of resources and the best load balancing for data access. The Grid Service under development will analyse requests based on the number of sequences, splitting them accordingly to the available resources. Lower-level computation will be performed through MPIBLAST. The software architecture is based on the WSRF standard.
10ababelomics1 aAparicio, G.1 aGotz, S.1 aConesa, A.1 aSegrelles, D.1 aBlanquer, I.1 aGarcia, J., M.1 aHernandez, V.1 aRobles, M.1 aTalon, M. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1682313801882nas a2200313 4500008004100000245005200041210005100093300001200144490000700156520100300163653001001166653002901176100001701205700001601222700002101238700001601259700002301275700001401298700001601312700001301328700001801341700001401359700001901373700001501392700001601407700002401423700001501447856010601462 2006 eng d00aNext station in microarray data analysis: GEPAS0 aNext station in microarray data analysis GEPAS aW486-910 v343 aThe Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.
10agepas10amicroarray data analysis1 aMontaner, D.1 aTarraga, J.1 aHuerta-Cepas, J.1 aBurguet, J.1 aVaquerizas, J., M.1 aConde, L.1 aMinguez, P.1 aVera, J.1 aMukherjee, S.1 aValls, J.1 aPujana, M., A.1 aAlloza, E.1 aHerrero, J.1 aAl-Shahrour, Fatima1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1684505602737nas a2200181 4500008004100000245005300041210005300094300000600147490000600153520218900159100001802348700001302366700002102379700001602400700001402416700001902430856010602449 2006 eng d00aOrigin and evolution of the peroxisomal proteome0 aOrigin and evolution of the peroxisomal proteome a80 v13 aBACKGROUND: Peroxisomes are ubiquitous eukaryotic organelles involved in various oxidative reactions. Their enzymatic content varies between species, but the presence of common protein import and organelle biogenesis systems support a single evolutionary origin. The precise scenario for this origin remains however to be established. The ability of peroxisomes to divide and import proteins post-translationally, just like mitochondria and chloroplasts, supports an endosymbiotic origin. However, this view has been challenged by recent discoveries that mutant, peroxisome-less cells restore peroxisomes upon introduction of the wild-type gene, and that peroxisomes are formed from the Endoplasmic Reticulum. The lack of a peroxisomal genome precludes the use of classical analyses, as those performed with mitochondria or chloroplasts, to settle the debate. We therefore conducted large-scale phylogenetic analyses of the yeast and rat peroxisomal proteomes. RESULTS : Our results show that most peroxisomal proteins (39-58%) are of eukaryotic origin, comprising all proteins involved in organelle biogenesis or maintenance. A significant fraction (13-18%), consisting mainly of enzymes, has an alpha-proteobacterial origin and appears to be the result of the recruitment of proteins originally targeted to mitochondria. Consistent with the findings that peroxisomes are formed in the Endoplasmic Reticulum, we find that the most universally conserved Peroxisome biogenesis and maintenance proteins are homologous to proteins from the Endoplasmic Reticulum Assisted Decay pathway. CONCLUSION: Altogether our results indicate that the peroxisome does not have an endosymbiotic origin and that its proteins were recruited from pools existing within the primitive eukaryote. Moreover the reconstruction of primitive peroxisomal proteomes suggests that ontogenetically as well as phylogenetically, peroxisomes stem from the Endoplasmic Reticulum. REVIEWERS: This article was reviewed by Arcady Mushegian, Gaspar Jekely and John Logsdon. OPEN PEER REVIEW: Reviewed by Arcady Mushegian, Gaspar Jekely and John Logsdon. For the full reviews, please go to the Reviewers’ comments section.1 aGabaldón, T.1 aSnel, B.1 avan Zimmeren, F.1 aHemrika, W.1 aTabak, H.1 aHuynen, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1655631400503nas a2200133 4500008004100000022001600041245010000057210006900157260003700226100001900263700001300282700001700295856005700312 2006 eng d a0-387-3452700aReliable and specific protein function prediction by combining homology with genomic(s) context0 aReliable and specific protein function prediction by combining h bF. Eisenhaber, Landes Bioscience1 aHuynen, M., A.1 aSnel, B.1 aT, Gabaldón uhttp://www.landesbioscience.com/iu/output.php?id=47902676nas a2200313 4500008004100000245005400041210005100095300000900146490000800155520141200163653011201575653025901687100001401946700002101960700002601981700002102007700001502028700001802043700002102061700003202082700002002114700002602134700002002160700001802180700001702198700001902215700002202234856010602256 2005 eng d00aAn anaerobic mitochondrion that produces hydrogen0 aanaerobic mitochondrion that produces hydrogen a74-90 v4343 aHydrogenosomes are organelles that produce ATP and hydrogen, and are found in various unrelated eukaryotes, such as anaerobic flagellates, chytridiomycete fungi and ciliates. Although all of these organelles generate hydrogen, the hydrogenosomes from these organisms are structurally and metabolically quite different, just like mitochondria where large differences also exist. These differences have led to a continuing debate about the evolutionary origin of hydrogenosomes. Here we show that the hydrogenosomes of the anaerobic ciliate Nyctotherus ovalis, which thrives in the hindgut of cockroaches, have retained a rudimentary genome encoding components of a mitochondrial electron transport chain. Phylogenetic analyses reveal that those proteins cluster with their homologues from aerobic ciliates. In addition, several nucleus-encoded components of the mitochondrial proteome, such as pyruvate dehydrogenase and complex II, were identified. The N. ovalis hydrogenosome is sensitive to inhibitors of mitochondrial complex I and produces succinate as a major metabolic end product–biochemical traits typical of anaerobic mitochondria. The production of hydrogen, together with the presence of a genome encoding respiratory chain components, and biochemical features characteristic of anaerobic mitochondria, identify the N. ovalis organelle as a missing link between mitochondria and hydrogenosomes.10a*Anaerobiosis Animals Ciliophora/*cytology/genetics/*metabolism/ultrastructure Cockroaches/parasitology DNA10aMitochondrial/genetics Electron Transport Electron Transport Complex I/antagonists & inhibitors/metabolism Genome Glucose/metabolism Hydrogen/*metabolism Mitochondria/enzymology/genetics/*metabolism/ultrastructure Molecular Sequence Data Open Reading Fra1 aBoxma, B.1 ade Graaf, R., M.1 avan der Staay, G., W.1 avan Alen, T., A.1 aRicard, G.1 aGabaldón, T.1 avan Hoek, A., H.1 avan der Staay, S., Y. Moon-1 aKoopman, W., J.1 avan Hellemond, J., J.1 aTielens, A., G.1 aFriedrich, T.1 aVeenhuis, M.1 aHuynen, M., A.1 aHackstein, J., H. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1574430201615nas a2200181 4500008004100000245012100041210006900162300001000231490000800241520089300249653003301142653008301175100001901258700001901277700001801296700001301314856010601327 2005 eng d00aCombining data from genomes, Y2H and 3D structure indicates that BolA is a reductase interacting with a glutaredoxin0 aCombining data from genomes Y2H and 3D structure indicates that a591-60 v5793 aGenomes, functional genomics data and 3D structure reflect different aspects of protein function. Here, we combine these data to predict that BolA, a widely distributed protein family with unknown function, is a reductase that interacts with a glutaredoxin. Comparisons at the 3D structure level as well as at the sequence profile level indicate homology between BolA and OsmC, an enzyme that reduces organic peroxides. Complementary to this, comparative analyses of genomes and genomics data provide strong evidence of an interaction between BolA and the mono-thiol glutaredoxin family. The interaction between BolA and a mono-thiol glutaredoxin is of particular interest because BolA does not, in contrast to its homolog OsmC, have evolutionarily conserved cysteines to provide it with reducing equivalents. We propose that BolA uses the mono-thiol glutaredoxin as the source for these.10a*Genome Glutaredoxins Models10aMolecular Oxidoreductases/chemistry/*metabolism Phylogeny Protein Conformation1 aHuynen, M., A.1 aSpronk, C., A.1 aGabaldón, T.1 aSnel, B. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1567081302167nas a2200301 4500008004100000245008600041210006900127300001100196490000700207520100200214653012701216653002601343653013701369653003901506653001801545100002001563700001901583700001901602700001901621700001401640700002401654700001701678700001801695700001301713700001901726700001401745856010601759 2005 eng d00aThe C-type lectin fold as an evolutionary solution for massive sequence variation0 aCtype lectin fold as an evolutionary solution for massive sequen a886-920 v123 aOnly few instances are known of protein folds that tolerate massive sequence variation for the sake of binding diversity. The most extensively characterized is the immunoglobulin fold. We now add to this the C-type lectin (CLec) fold, as found in the major tropism determinant (Mtd), a retroelement-encoded receptor-binding protein of Bordetella bacteriophage. Variation in Mtd, with its approximately 10(13) possible sequences, enables phage adaptation to Bordetella spp. Mtd is an intertwined, pyramid-shaped trimer, with variable residues organized by its CLec fold into discrete receptor-binding sites. The CLec fold provides a highly static scaffold for combinatorial display of variable residues, probably reflecting a different evolutionary solution for balancing diversity against stability from that in the immunoglobulin fold. Mtd variants are biased toward the receptor pertactin, and there is evidence that the CLec fold is used broadly for sequence variation by related retroelements.10aAmino Acid Sequence Bacterial Outer Membrane Proteins/*chemistry Bacteriophages/*metabolism Bordetella/*virology Evolution10aBordetella/*chemistry10aC-Type/*chemistry Molecular Sequence Data Protein Conformation Protein Folding Viral Proteins/*chemistry/*genetics Virulence Factors10aMolecular Genetic Variation Genome10aViral Lectins1 aMcMahon, S., A.1 aMiller, J., L.1 aLawton, J., A.1 aKerkow, D., E.1 aHodes, A.1 aMarti-Renom, M., A.1 aDoulatov, S.1 aNarayanan, E.1 aSali, A.1 aMiller, J., F.1 aGhosh, P. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1617032403705nas a2200805 4500008004100000245010800041210006900149300001100218490000700229520133100236653002501567653010901592653000801701653010501709653007501814100001601889700001401905700001501919700001701934700001501951700001501966700001301981700001501994700001602009700002002025700001502045700002102060700001502081700001802096700001502114700002902129700001502158700001702173700001502190700002802205700001502233700001602248700001402264700002602278700001602304700001502320700002102335700001602356700001902372700002002391700001702411700002702428700001702455700001502472700001602487700001502503700002502518700002002543700001302563700001602576700002202592700001302614700001602627700001402643700001402657700001402671700001402685700001502699700001502714700001602729700001402745700001702759700001702776856010602793 2005 eng d00aDevelopment of a citrus genome-wide EST collection and cDNA microarray as resources for genomic studies0 aDevelopment of a citrus genomewide EST collection and cDNA micro a375-910 v573 aA functional genomics project has been initiated to approach the molecular characterization of the main biological and agronomical traits of citrus. As a key part of this project, a citrus EST collection has been generated from 25 cDNA libraries covering different tissues, developmental stages and stress conditions. The collection includes a total of 22,635 high-quality ESTs, grouped in 11,836 putative unigenes, which represent at least one third of the estimated number of genes in the citrus genome. Functional annotation of unigenes which have Arabidopsis orthologues (68% of all unigenes) revealed gene representation in every major functional category, suggesting that a genome-wide EST collection was obtained. A Citrus clementina Hort. ex Tan. cv. Clemenules genomic library, that will contribute to further characterization of relevant genes, has also been constructed. To initiate the analysis of citrus transcriptome, we have developed a cDNA microarray containing 12,672 probes corresponding to 6875 putative unigenes of the collection. Technical characterization of the microarray showed high intra- and inter-array reproducibility, as well as a good range of sensitivity. We have also validated gene expression data achieved with this microarray through an independent technique such as RNA gel blot analysis.10aCitrus/*genetics DNA10aComplementary/chemistry/genetics *Expressed Sequence Tags Gene Expression Profiling Gene Library *Genome10aDNA10aPlant Genomics/*methods Molecular Sequence Data Oligonucleotide Array Sequence Analysis/*methods RNA10aPlant/genetics/metabolism Reproducibility of Results Sequence Analysis1 aForment, J.1 aGadea, J.1 aHuerta, L.1 aAbizanda, L.1 aAgusti, J.1 aAlamar, S.1 aAlos, E.1 aAndres, F.1 aArribas, R.1 aBeltran, J., P.1 aBerbel, A.1 aBlazquez, M., A.1 aBrumos, J.1 aCanas, L., A.1 aCercos, M.1 aColmenero-Flores, J., M.1 aConesa, A.1 aEstables, B.1 aGandia, M.1 aGarcia-Martinez, J., L.1 aGimeno, J.1 aGisbert, A.1 aGomez, G.1 aGonzalez-Candelas, L.1 aGranell, A.1 aGuerri, J.1 aLafuente, M., T.1 aMadueno, F.1 aMarcos, J., F.1 aMarques, M., C.1 aMartinez, F.1 aMartinez-Godoy, M., A.1 aMiralles, S.1 aMoreno, P.1 aNavarro, L.1 aPallas, V.1 aPerez-Amador, M., A.1 aPerez-Valle, J.1 aPons, C.1 aRodrigo, I.1 aRodriguez, P., L.1 aRoyo, C.1 aSerrano, R.1 aSoler, G.1 aTadeo, F.1 aTalon, M.1 aTerol, J.1 aTrenor, M.1 aVaello, L.1 aVicente, O.1 aVidal, Ch1 aZacarias, L.1 aConejero, V. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1583012802188nas a2200241 4500008004100000245009500041210006900136300001200205490000700217520141200224653001001636653002901646100002301675700001401698700002001712700001601732700001601748700002101764700002401785700001601809700001501825856010601840 2005 eng d00aGEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data0 aGEPAS an experimentoriented pipeline for the analysis of microar aW616-200 v333 aThe Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With >76,000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.
10agepas10amicroarray data analysis1 aVaquerizas, J., M.1 aConde, L.1 aYankilevich, P.1 aCabezon, A.1 aMinguez, P.1 aDiaz-Uriarte, R.1 aAl-Shahrour, Fatima1 aHerrero, J.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1598054801413nas a2200181 4500008004100000245005600041210005500097300001100152490000700163520072100170653007700891653008700968100001701055700001501072700002101087700001701108856010601125 2005 eng d00aHCAD, closing the gap between breakpoints and genes0 aHCAD closing the gap between breakpoints and genes aD511-30 v333 aRecurrent chromosome aberrations are an important resource when associating human pathologies to specific genes. However, for technical reasons a large number of chromosome breakpoints are defined only at the level of cytobands and many of the genes involved remain unidentified. We developed a web-based information system that mines the scientific literature and generates textual and comprehensive information on all human breakpoints. We show that the statistical analysis of this textual information and its combination with genomic data can identify genes directly involved in DNA rearrangements. The Human Chromosome Aberration Database (HCAD) is publicly accessible at http://www.pdg.cnb.uam.es/UniPub/HCAD/.10a*Chromosome Breakage Chromosome Disorders/diagnosis/*genetics *Databases10aGenetic Genes *Genetic Predisposition to Disease Humans PubMed Systems Integration1 aHoffmann, R.1 aDopazo, J.1 aCigudosa, J., C.1 aValencia, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1560825001787nas a2200181 4500008004100000245012500041210006900166300001300235490001500248520091200263653004401175653003901219653014701258653005701405100001801462700001901480856010601499 2005 eng d00aLineage-specific gene loss following mitochondrial endosymbiosis and its potential for function prediction in eukaryotes0 aLineagespecific gene loss following mitochondrial endosymbiosis aii144-500 v21 Suppl 23 aMOTIVATION: The endosymbiotic origin of mitochondria has resulted in a massive horizontal transfer of genetic material from an alpha-proteobacterium to the early eukaryotes. Using large-scale phylogenetic analysis we have previously identified 630 orthologous groups of proteins derived from this event. Here we show that this proto-mitochondrial protein set has undergone extensive lineage-specific gene loss in the eukaryotes, with an average of three losses per orthologous group in a phylogeny of nine species. This gene loss has resulted in a high variability of the alphaproteobacterial-derived gene content of present-day eukaryotic genomes that might reflect functional adaptation to different environments. Proteins functioning in the same biochemical pathway tend to have a similar history of gene loss events, and we use this property to predict functional interactions among proteins in our set.10aAnimals Chromosome Mapping/*methods DNA10aMitochondrial/*genetics *Evolution10aMolecular *Gene Deletion Genetic Variation/genetics Humans Linkage Disequilibrium/*genetics Mitochondrial Proteins/*genetics Sequence Homology10aNucleic Acid Species Specificity Symbiosis/*genetics1 aGabaldón, T.1 aHuynen, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1620409402687nas a2200337 4500008004100000245010400041210006900145300001000214490000700224520138200231653003401613653006501647653002501712653001001737653003201747653003301779653003201812653019101844100001502035700002202050700002602072700001602098700002502114700001402139700002202153700001602175700001502191700002102206700001602227856010602243 2005 eng d00aA novel candidate region linked to development of both pheochromocytoma and head/neck paraganglioma0 anovel candidate region linked to development of both pheochromoc a260-80 v423 aAlthough the histologic distinction between pheochromocytomas and head and neck paragangliomas is clear, little is known about the genetic differences between them. To date, various sets of genes have been found to be involved in inherited susceptibility to developing both tumor types, but the genes involved in sporadic pathogenesis are still unknown. To define new candidate regions, we performed CGH analysis on 29 pheochromocytomas and on 24 paragangliomas mainly of head and neck origin (20 of 24), which allowed us to differentiate between the two tumor types. Loss of 3q was significantly more frequent in pheochromocytomas, and loss of 1q appeared only in paragangliomas. We also found gain of 11q13 to be a significantly frequent alteration in malignant cases of both types. In addition, recurrent loss of 8p22-23 was found in 62% of pheochromocytomas (including all malignant cases) versus in 33% of paragangliomas, suggesting that this region contains candidate genes involved in the pathogenesis of this abnormality. Using FISH analysis on tissue microarrays, we confirmed genomic deletion of this region in 55% of pheochromocytomas compared to 12% of paragangliomas. Loss of 8p22-23 appears to be an important event in the sporadic development of these tumors, and additional molecular studies are necessary to identify candidate genes in this chromosomal region.10a80 and over Child Chromosomes10aAdolescent Adrenal Gland Neoplasms/*genetics Adult Aged Aged10aBiological/*genetics10aHuman10aPair 1/genetics Chromosomes10aPair 11/genetics Chromosomes10aPair 3/genetics Chromosomes10aPair 8/genetics Female Gene Deletion Head and Neck Neoplasms/*genetics Humans Male Middle Aged Nucleic Acid Hybridization Paraganglioma/*genetics Pheochromocytoma/*genetics Tumor Markers1 aCascon, A.1 aRuiz-Llorente, S.1 aRodriguez-Perales, S.1 aHonrado, E.1 aMartinez-Ramirez, A.1 aLeton, R.1 aMontero-Conde, C.1 aBenitez, J.1 aDopazo, J.1 aCigudosa, J., C.1 aRobledo, M. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1560934702968nas a2200337 4500008004100000245012900041210006900170300000900239490000700248520175600255653010902011653003502120653001702155653006002172653007102232100001702303700001602320700001502336700001602351700001502367700001602382700001802398700002102416700001302437700001502450700001402465700001502479700001402494700001602508856010602524 2005 eng d00aPhenotypic characterization of BRCA1 and BRCA2 tumors based in a tissue microarray study with 37 immunohistochemical markers0 aPhenotypic characterization of BRCA1 and BRCA2 tumors based in a a5-140 v903 aFamilial breast cancers that are associated with BRCA1 or BRCA2 germline mutations differ in both their morphological and immunohistochemical characteristics. To further characterize the molecular difference between genotypes, the authors evaluated the expression of 37 immunohistochemical markers in a tissue microarray (TMA) containing cores from 20 BRCA1, 14 BRCA2, and 59 sporadic age-matched breast carcinomas. Markers analyzed included, amog others, common markers in breast cancer, such as hormone receptors, p53 and HER2, along with 15 molecules involved in cell cycle regulation, such as cyclins, cyclin dependent kinases (CDK) and CDK inhibitors (CDKI), apoptosis markers, such as BCL2 and active caspase 3, and two basal/myoepithelial markers (CK 5/6 and P-cadherin). In addition, we analyzed the amplification of CCND1, CCNE, HER2 and MYC by FISH.Unsupervised cluster data analysis of both hereditary and sporadic cases using the complete set of immunohistochemical markers demonstrated that most BRCA1-associated carcinomas grouped in a branch of ER-, HER2-negative tumors that expressed basal cell markers and/or p53 and had higher expression of activated caspase 3. The cell cycle proteins associated with these tumors were E2F6, cyclins A, B1 and E, SKP2 and Topo IIalpha. In contrast, most BRCA2-associated carcinomas grouped in a branch composed by ER/PR/BCL2-positive tumors with a higher expression of the cell cycle proteins cyclin D1, cyclin D3, p27, p16, p21, CDK4, CDK2 and CDK1. In conclusion, our study in hereditary breast cancer tumors analyzing 37 immunohistochemical markers, define the molecular differences between BRCA1 and BRCA2 tumors with respect to hormonal receptors, cell cycle, apoptosis and basal cell markers.10aAdult Apoptosis Breast Neoplasms/*genetics/*pathology Cell Cycle Proteins Cluster Analysis Female *Genes10aBiological/genetics/metabolism10aBRCA1 *Genes10aBRCA2 Humans Immunohistochemistry In Situ Hybridization10aFluorescence Phenotype Spain *Tissue Array Analysis *Tumor Markers1 aPalacios, J.1 aHonrado, E.1 aOsorio, A.1 aCazorla, A.1 aSarrio, D.1 aBarroso, A.1 aRodriguez, S.1 aCigudosa, J., C.1 aDiez, O.1 aAlonso, C.1 aLerma, E.1 aDopazo, J.1 aRivas, C.1 aBenitez, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1577052102881nas a2200397 4500008004100000245016800041210006900209300001200278490000700290520143600297653010101733653002201834653001001856653008301866653003301949653003301982653003302015653003202048653005102080100001602131700002102147700001502168700001602183700001602199700001602215700001602231700001802247700001602265700001402281700001302295700002102308700001502329700001702344700001602361856010602377 2005 eng d00aA predictor based on the somatic genomic changes of the BRCA1/BRCA2 breast cancer tumors identifies the non-BRCA1/BRCA2 tumors with BRCA1 promoter hypermethylation0 apredictor based on the somatic genomic changes of the BRCA1BRCA2 a1146-530 v113 aThe genetic changes underlying in the development and progression of familial breast cancer are poorly understood. To identify a somatic genetic signature of tumor progression for each familial group, BRCA1, BRCA2, and non-BRCA1/BRCA2 (BRCAX) tumors, by high-resolution comparative genomic hybridization, we have analyzed 77 tumors previously characterized for BRCA1 and BRCA2 germ line mutations. Based on a combination of the somatic genetic changes observed at the six most different chromosomal regions and the status of the estrogen receptor, we developed using random forests a molecular classifier, which assigns to a given tumor a probability to belong either to the BRCA1 or to the BRCA2 class. Because 76.5% (26 of 34) of the BRCAX cases were classified with our predictor to the BRCA1 class with a probability of >50%, we analyzed the BRCA1 promoter region for aberrant methylation in all the BRCAX cases. We found that 15 of the 34 BRCAX analyzed tumors had hypermethylation of the BRCA1 gene. When we considered the predictor, we observed that all the cases with this epigenetic event were assigned to the BRCA1 class with a probability of >50%. Interestingly, 84.6% of the cases (11 of 13) assigned to the BRCA1 class with a probability >80% had an aberrant methylation of the BRCA1 promoter. This fact suggests that somatic BRCA1 inactivation could modify the profile of tumor progression in most of the BRCAX cases.10aBRCA1 Protein/*genetics BRCA2 Protein/*genetics Breast Neoplasms/*genetics/pathology Chromosomes10aGenetic/*genetics10aHuman10aHuman Humans Male Mutation Nucleic Acid Hybridization/methods Promoter Regions10aPair 12/genetics Chromosomes10aPair 15/genetics Chromosomes10aPair 18/genetics Chromosomes10aPair 2/genetics Chromosomes10aPair 8/genetics *DNA Methylation Female Genome1 aAlvarez, S.1 aDiaz-Uriarte, R.1 aOsorio, A.1 aBarroso, A.1 aMelchor, L.1 aPaz, M., F.1 aHonrado, E.1 aRodriguez, R.1 aUrioste, M.1 aValle, L.1 aDiez, O.1 aCigudosa, J., C.1 aDopazo, J.1 aEsteller, M.1 aBenitez, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1570918200738nas a2200229 4500008004100000245005000041210004900091260010900140300001200249490000600261100001400267700001300281700001400294700001400308700001900322700001300341700001500354700001600369700002200385700001300407856008800420 2005 eng d00aSalinibacter ruber: genomics and biogeography0 aSalinibacter ruber genomics and biogeography aDordrecht, NetherlandsbNina Gunde-Cimerman, Ana Plemenitas, and Aharon Oren. Kluwer Academic Publishers a257-2660 v91 aAntón, J1 aPeña, A1 aValens, M1 aSantos, F1 aGlöckner, F.O1 aBauer, M1 aDopazo, J.1 aHerrero, J.1 aRosselló-Mora, R1 aAmann, R uhttps://www.clinbioinfosspa.es/content/salinibacter-ruber-genomics-and-biogeography02633nas a2200181 4500008004100000245011500041210006900156300001100225490000800236520169000244653015501934653019202089653001202281100001802293700001502311700001902326856010602345 2005 eng d00aTracing the evolution of a large protein complex in the eukaryotes, NADH:ubiquinone oxidoreductase (Complex I)0 aTracing the evolution of a large protein complex in the eukaryot a857-700 v3483 aThe increasing availability of sequenced genomes enables the reconstruction of the evolutionary history of large protein complexes. Here, we trace the evolution of NADH:ubiquinone oxidoreductase (Complex I), which has increased in size, by so-called supernumary subunits, from 14 subunits in the bacteria to 30 in the plants and algae, 37 in the fungi and 46 in the mammals. Using a combination of pair-wise and profile-based sequence comparisons at the levels of proteins and the DNA of the sequenced eukaryotic genomes, combined with phylogenetic analyses to establish orthology relationships, we were able to (1) trace the origin of six of the supernumerary subunits to the alpha-proteobacterial ancestor of the mitochondria, (2) detect previously unidentified homology relations between subunits from fungi and mammals, (3) detect previously unidentified subunits in the genomes of several species and (4) document several cases of gene duplications among supernumerary subunits in the eukaryotes. One of these, a duplication of N7BM (B17.2), is particularly interesting as it has been lost from genomes that have also lost Complex I proteins, making it a candidate for a Complex I interacting protein. A parsimonious reconstruction of eukaryotic Complex I evolution shows an initial increase in size that predates the separation of plants, fungi and metazoa, followed by a gradual adding and incidental losses of subunits in the various evolutionary lineages. This evolutionary scenario is in contrast to that for Complex I in the prokaryotes, for which the combination of several separate, and previously independently functioning modules into a single complex has been proposed.10aAmino Acid Sequence Animals Computational Biology Electron Transport Complex I/*chemistry/*genetics/metabolism Eukaryotic Cells/*enzymology *Evolution10aMolecular Humans Molecular Sequence Data Photosynthesis Phylogeny Plastids/enzymology Protein Binding Protein Subunits/chemistry/genetics/metabolism Sequence Alignment Structural Homology10aProtein1 aGabaldón, T.1 aRainey, D.1 aHuynen, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1584301801857nas a2200169 4500008004100000245008800041210006900129300001200198490000800210520119800218653001501416653010001431100001901531700001801550700001301568856010601581 2005 eng d00aVariation and evolution of biomolecular systems: searching for functional relevance0 aVariation and evolution of biomolecular systems searching for fu a1839-450 v5793 aThe availability of genome sequences and functional genomics data from multiple species enables us to compare the composition of biomolecular systems like biochemical pathways and protein complexes between species. Here, we review small- and large-scale, "genomics-based" approaches to biomolecular systems variation. In general, caution is required when comparing the results of bioinformatics analyses of genomes or of functional genomics data between species. Limitations to the sensitivity of sequence analysis tools and the noisy nature of genomics data tend to lead to systematic overestimates of the amount of variation. Nevertheless, the results from detailed manual analyses, and of large-scale analyses that filter out systematic biases, point to a large amount of variation in the composition of biomolecular systems. Such observations challenge our understanding of the function of the systems and their individual components and can potentially facilitate the identification and functional characterization of sub-systems within a system. Mapping the inter-species variation of complex biomolecular systems on a phylogenetic species tree allows one to reconstruct their evolution.10a*Evolution10aMolecular Genetic Variation Multiprotein Complexes/*genetics Phylogeny Protein Binding/genetics1 aHuynen, M., A.1 aGabaldón, T.1 aSnel, B. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1576356103269nas a2200337 4500008004100000245010000041210006900141300001200210490000700222520201600229653008002245653005102325653005202376653014002428100001502568700001402583700001602597700002402613700001802637700001902655700001702674700001402691700002402705700001402729700001302743700001902756700001802775700001902793700001302812856010602825 2004 eng d00aMODBASE, a database of annotated comparative protein structure models, and associated resources0 aMODBASE a database of annotated comparative protein structure mo aD217-220 v323 aMODBASE (http://salilab.org/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on the MODELLER package for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE uses the MySQL relational database management system for flexible querying and CHIMERA for viewing the sequences and structures (http://www.cgl.ucsf.edu/chimera/). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different data sets. The largest data set contains 1,26,629 models for domains in 659,495 out of 1,182,126 unique protein sequences in the complete Swiss-Prot/TrEMBL database (August 25, 2003); only models based on alignments with significant similarity scores and models assessed to have the correct fold despite insignificant alignments are included. Another model data set supports target selection and structure-based annotation by the New York Structural Genomics Research Consortium; e.g. the 53 new structures produced by the consortium allowed us to characterize structurally 24,113 sequences. MODBASE also contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Our other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI, http://salilab.org/dbali) as well as web servers for automated comparative modeling with MODPIPE (MODWEB, http://salilab. org/modweb), modeling of loops in protein structures (MODLOOP, http://salilab.org/modloop) and predicting functional consequences of single nucleotide polymorphisms (SNPWEB, http://salilab. org/snpweb).10aAmino Acid Sequence Animals Binding Sites *Computational Biology *Databases10aMolecular Molecular Sequence Data Polymorphism10aProtein Genomics Humans Internet Ligands Models10aSingle Nucleotide Protein Binding Protein Conformation Proteins/*chemistry/genetics Sequence Alignment Software User-Computer Interface1 aPieper, U.1 aEswar, N.1 aBraberg, H.1 aMadhusudhan, M., S.1 aDavis, F., P.1 aStuart, A., C.1 aMirkovic, N.1 aRossi, A.1 aMarti-Renom, M., A.1 aFiser, A.1 aWebb, B.1 aGreenblatt, D.1 aHuang, C., C.1 aFerrin, T., E.1 aSali, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1468139802102nas a2200217 4500008004100000245007500041210006900116300001200185490000700197520140300204653001001607653002901617100001601646700002301662700002401685700001401709700001501723700002501738700001501763856010601778 2004 eng d00aNew challenges in gene expression data analysis and the extended GEPAS0 aNew challenges in gene expression data analysis and the extended aW485-910 v323 aSince the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across sets of experimental conditions, implying, essentially, the use of clustering techniques. Recently, however, interest has focussed more on finding genes differentially expressed among distinct classes of experiments, or correlated to diverse clinical outcomes, as well as in building predictors. In addition to this, the availability of accurate genomic data and the recent implementation of CGH arrays has made mapping expression and genomic data on the chromosomes possible. There is also a clear demand for methods that allow the automatic transfer of biological information to the results of microarray experiments. Different initiatives, such as the Gene Ontology (GO) consortium, pathways databases, protein functional motifs, etc., provide curated annotations for genes. Whereas many resources on the web focus mainly on clustering methods, GEPAS has evolved to cope with the aforementioned new challenges that have recently arisen in the field of microarray data analysis. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://gepas.bioinfo.cnio.es.
10agepas10amicroarray data analysis1 aHerrero, J.1 aVaquerizas, J., M.1 aAl-Shahrour, Fatima1 aConde, L.1 aMateos, A.1 aDiaz-Uriarte, J., S.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1521543402058nas a2200169 4500008004100000245006600041210006600107300001100173490000700184520119200191653019701383653012101580653004401701100001801745700001901763856010601782 2004 eng d00aPrediction of protein function and pathways in the genome era0 aPrediction of protein function and pathways in the genome era a930-440 v613 aThe growing number of completely sequenced genomes adds new dimensions to the use of sequence analysis to predict protein function. Compared with the classical knowledge transfer from one protein to a similar sequence (homology-based function prediction), knowledge about the corresponding genes in other genomes (orthology-based function prediction) provides more specific information about the protein’s function, while the analysis of the sequence in its genomic context (context-based function prediction) provides information about its functional context. Whereas homology-based methods predict the molecular function of a protein, genomic context methods predict the biological process in which it plays a role. These complementary approaches can be combined to elucidate complete functional networks and biochemical pathways from the genome sequence of an organism. Here we review recent advances in the field of genomic-context based methods of protein function prediction. Techniques are highlighted with examples, including an analysis that combines information from genomic-context with homology to predict a role of the RNase L inhibitor in the maturation of ribosomal RNA.10aATP-Binding Cassette Transporters/genetics/metabolism Amino Acid Sequence Animals Artificial Gene Fusion Base Sequence Chaperonins/genetics/metabolism Chromosomes/genetics/metabolism Evolution10aMolecular *Genome Genomics Humans Molecular Sequence Data Phylogeny *Proteins/classification/genetics/metabolism RNA10aRibosomal/metabolism Sequence Alignment1 aGabaldón, T.1 aHuynen, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1509501301755nas a2200145 4500008004100000245003900041210003900080300001100119490000900130520117400139653015301313100001801466700001901484856010601503 2004 eng d00aShaping the mitochondrial proteome0 aShaping the mitochondrial proteome a212-200 v16593 aMitochondria are eukaryotic organelles that originated from a single bacterial endosymbiosis some 2 billion years ago. The transition from the ancestral endosymbiont to the modern mitochondrion has been accompanied by major changes in its protein content, the so-called proteome. These changes included complete loss of some bacterial pathways, amelioration of others and gain of completely new complexes of eukaryotic origin such as the ATP/ADP translocase and most of the mitochondrial protein import machinery. This renewal of proteins has been so extensive that only 14-16% of modern mitochondrial proteome has an origin that can be traced back to the bacterial endosymbiont. The rest consists of proteins of diverse origin that were eventually recruited to function in the organelle. This shaping of the proteome content reflects the transformation of mitochondria into a highly specialized organelle that, besides ATP production, comprises a variety of functions within the eukaryotic metabolism. Here we review recent advances in the fields of comparative genomics and proteomics that are throwing light on the origin and evolution of the mitochondrial proteome.10aAnimals Biological Transport Energy Metabolism Eukaryotic Cells/physiology *Evolution Humans Mitochondria/*physiology Phylogeny Proteome/*physiology1 aGabaldón, T.1 aHuynen, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1557605401309nas a2200145 4500008004100000245010100041210006900142300001100211490000600222520077700228100001601005700002101021700001501042856010601057 2003 eng d00aAn approach to inferring transcriptional regulation among genes from large-scale expression data0 aapproach to inferring transcriptional regulation among genes fro a148-540 v43 aThe use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time-course experiments allow researchers to study the dynamics of gene interactions. The inference of genetic networks from such measures can give important insights for the understanding of a variety of biological problems. Most of the existing methods for genetic network reconstruction require many experimental data points, or can only be applied to the reconstruction of small subnetworks. Here we present a method that reduces the dimensionality of the dataset and then extracts the significant dynamic correlations among genes. The method requires a number of points achievable in common time-course experiments.1 aHerrero, J.1 aDiaz-Uriarte, R.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1862909702356nas a2200265 4500008004100000245006200041210006200103300001000165490000700175520124200182653003001424653007301454653007501527653004901602653004201651653004301693653005001736653007501786653005801861100001901919700001601938700001501954700001501969856010601984 2003 eng d00aComparing bacterial genomes through conservation profiles0 aComparing bacterial genomes through conservation profiles a991-80 v133 aWe constructed two-dimensional representations of profiles of gene conservation across different genomes using the genome of Escherichia coli as a model. These profiles permit both the visualization at the genome level of different traits in the organism studied and, at the same time, reveal features related to the genomes analyzed (such as defective genomes or genomes that lack a particular system). Conserved genes are not uniformly distributed along the E. coli genome but tend to cluster together. The study of gene distribution patterns across genomes is important for the understanding of how sets of genes seem to be dependent on each other, probably having some functional link. This provides additional evidence that can be used for the elucidation of the function of unannotated genes. Clustering these patterns produces families of genes which can be arranged in a hierarchy of closeness. In this way, functions can be defined at different levels of generality depending on the level of the hierarchy that is studied. The combined study of conservation and phenotypic traits opens up the possibility of defining phenotype/genotype associations, and ultimately inferring the gene or genes responsible for a particular trait.10aBacterial Genotype Models10aBacterial/genetics Cluster Analysis Conserved Sequence/*genetics DNA10aBacterial/genetics Escherichia coli/classification/*genetics Evolution10aBacterial/genetics Gene Order/genetics Genes10aBacterial/genetics/physiology *Genome10aChromosome Mapping/methods Chromosomes10aGenetic Phenotype Phylogeny Sequence Homology10aMolecular Gene Expression Profiling/methods Gene Expression Regulation10aNucleic Acid Species Specificity Terminology as Topic1 aMartin, M., J.1 aHerrero, J.1 aMateos, A.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1269532402305nas a2200205 4500008004100000245007400041210006900115300001200184490000800196520129400204653014101498653005201639653021501691653002601906100001101932700001501943700001701958700001801975856010601993 2003 eng d00aExamining the role of glutamic acid 183 in chloroperoxidase catalysis0 aExamining the role of glutamic acid 183 in chloroperoxidase cata a13855-90 v2783 aSite-directed mutagenesis has been used to investigate the role of glutamic acid 183 in chloroperoxidase catalysis. Based on the x-ray crystallographic structure of chloroperoxidase, Glu-183 is postulated to function on distal side of the heme prosthetic group as an acid-base catalyst in facilitating the reaction between the peroxidase and hydrogen peroxide with the formation of Compound I. In contrast, the other members of the heme peroxidase family use a histidine residue in this role. Plasmids have now been constructed in which the codon for Glu-183 is replaced with a histidine codon. The mutant recombinant gene has been expressed in Aspergillus niger. An analysis of the produced mutant gene shows that the substitution of Glu-183 with a His residue is detrimental to the chlorination and dismutation activity of chloroperoxidase. The activity is reduced by 85 and 50% of wild type activity, respectively. However, quite unexpectedly, the epoxidation activity of the mutant enzyme is significantly enhanced approximately 2.5-fold. These results show that Glu-183 is important but not essential for the chlorination activity of chloroperoxidase. It is possible that the increased epoxidation of the mutant enzyme is based on an increase in the hydrophobicity of the active site.10aAspergillus niger/metabolism Catalase/metabolism Catalysis Chloride Peroxidase/*chemistry/*metabolism Chlorine/metabolism Chromatography10aIon Exchange Circular Dichroism Crystallography10aPolyacrylamide Gel Fungi/enzymology Glutamic Acid/*chemistry Histidine/chemistry/metabolism Hydrogen-Ion Concentration Immunoblotting Isoelectric Focusing Mutation Oxidoreductases/metabolism Plasmids/metabolism10aX-Ray Electrophoresis1 aYi, X.1 aConesa, A.1 aPunt, P., J.1 aHager, L., P. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1257647701226nas a2200169 4500008004100000245003900041210003900080300001000119490000700129520049600136653021400632653005200846100001600898700002100914700001500935856010600950 2003 eng d00aGene expression data preprocessing0 aGene expression data preprocessing a655-60 v193 aWe present an interactive web tool for preprocessing microarray gene expression data. It analyses the data, suggests the most appropriate transformations and proceeds with them after user agreement. The normal preprocessing steps include scale transformations, management of missing values, replicate handling, flat pattern filtering and pattern standardization and they are required before performing any pattern analysis. The processed data set can be sent to other pattern analysis tools.10a*Database Management Systems Gene Expression Profiling/*methods Information Storage and Retrieval/methods Internet Oligonucleotide Array Sequence Analysis/*methods Sequence Alignment/*methods Sequence Analysis10aDNA/*methods *Software *User-Computer Interface1 aHerrero, J.1 aDiaz-Uriarte, R.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1265172601500nas a2200217 4500008004100000245007700041210006900118300001100187490000700198520080200205653001001007653002901017100001601046700002401062700002101086700001501107700002301122700001601145700001501161856010601176 2003 eng d00aGEPAS: A web-based resource for microarray gene expression data analysis0 aGEPAS A webbased resource for microarray gene expression data an a3461-70 v313 aWe present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite (http://gepas.bioinfo.cnio.es). GEPAS is composed of different interconnected modules which include tools for data pre-processing, two-conditions comparison, unsupervised and supervised clustering (which include some of the most popular methods as well as home made algorithms) and several tests for differential gene expression among different classes, continuous variables or survival analysis. A multiple purpose tool for data mining, based on Gene Ontology, is also linked to the tools, which constitutes a very convenient way of analysing clustering results. On-line tutorials are available from our main web server (http://bioinfo.cnio.es).
10agepas10amicroarray data analysis1 aHerrero, J.1 aAl-Shahrour, Fatima1 aDiaz-Uriarte, R.1 aMateos, A.1 aVaquerizas, J., M.1 aSantoyo, J.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1282434500772nas a2200145 4500008004100000245005700041210005600098300000800154490000800162653016300170653015000333100001800483700001900501856010600520 2003 eng d00aReconstruction of the proto-mitochondrial metabolism0 aReconstruction of the protomitochondrial metabolism a6090 v30110aAerobiosis Algorithms Alphaproteobacteria/chemistry/genetics/*metabolism Amino Acids/metabolism Animals Bacterial Proteins/chemistry/*metabolism Genome Genome10aBacterial Glycerol/metabolism Humans Lipid Metabolism Mitochondria/chemistry/genetics/*metabolism Phylogeny *Proteome Symbiosis Yeasts/metabolism1 aGabaldón, T.1 aHuynen, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1289393400720nas a2200181 4500008004100000245013000041210006900171260003000240300001000270653001500280100002400295700001600319700001500335700001600350700002100366700001500387856013600402 2003 eng d00aUsing Gene Ontology on genome-scale studies to find significant associations of biologically relevant terms to group of genes0 aUsing Gene Ontology on genomescale studies to find significant a aNew York, USAbIEEE Press a43-5210ababelomics1 aAl-Shahrour, Fatima1 aHerrero, J.1 aMateos, A.1 aSantoyo, J.1 aDíaz-Uriarte, R1 aDopazo, J. uhttps://www.clinbioinfosspa.es/content/using-gene-ontology-genome-scale-studies-find-significant-associations-biologically-relevant01648nas a2200241 4500008004100000245010900041210006900150300001100219490000700230520055300237653010300790653018900893653008001082100001601162700001401178700001601192700001501208700001701223700002201240700002101262700001701283856010601300 2002 eng d00aBioinformatics methods for the analysis of expression arrays: data clustering and information extraction0 aBioinformatics methods for the analysis of expression arrays dat a269-830 v983 aExpression arrays facilitate the monitoring of changes in the expression patterns of large collections of genes. The analysis of expression array data has become a computationally-intensive task that requires the development of bioinformatics technology for a number of key stages in the process, such as image analysis, database storage, gene clustering and information extraction. Here, we review the current trends in each of these areas, with particular emphasis on the development of the related technology being carried out within our groups.10aAbstracting and Indexing as Topic/methods *Cluster Analysis *Database Management Systems Databases10aComputer-Assisted/methods Information Storage and Retrieval/*methods Internet Medline National Library of Medicine (U.S.) Oligonucleotide Array Sequence Analysis/*methods United States10aGenetic Gene Expression Gene Expression Profiling/*methods Image Processing1 aTamames, J.1 aClark, D.1 aHerrero, J.1 aDopazo, J.1 aBlaschke, C.1 aFernandez, J., M.1 aOliveros, J., C.1 aValencia, A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1214199201191nas a2200157 4500008004100000245011600041210006900157300001100226490000600237520046000243653007400703653011900777100001600896700001500912856010600927 2002 eng d00aCombining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns0 aCombining hierarchical clustering and selforganizing maps for ex a467-700 v13 aSelf-organizing maps (SOM) constitute an alternative to classical clustering methods because of its linear run times and superior performance to deal with noisy data. Nevertheless, the clustering obtained with SOM is dependent on the relative sizes of the clusters. Here, we show how the combination of SOM with hierarchical clustering methods constitutes an excellent tool for exploratory analysis of massive data like DNA microarray expression patterns.10aCluster Analysis Computational Biology/methods *Gene Expression Genes10aFungal/genetics *Genome Oligonucleotide Array Sequence Analysis/*methods Statistics as Topic/*methods Time Factors1 aHerrero, J.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1264591903136nas a2200397 4500008004100000245010000041210006900141300001200210490000800222520146400230653015401694653005901848653001301907653008901920653015002009653006702159653003702226653008702263653001102350100001502361700001902376700001402395700001502409700001602424700001802440700003002458700002602488700001802514700001402532700001802546700001602564700001902580700001502599700001802614856010602632 2002 eng d00aIdentification of genes involved in resistance to interferon-alpha in cutaneous T-cell lymphoma0 aIdentification of genes involved in resistance to interferonalph a1825-370 v1613 aInterferon-alpha therapy has been shown to be active in the treatment of mycosis fungoides although the individual response to this therapy is unpredictable and dependent on essentially unknown factors. In an effort to better understand the molecular mechanisms of interferon-alpha resistance we have developed an interferon-alpha resistant variant from a sensitive cutaneous T-cell lymphoma cell line. We have performed expression analysis to detect genes differentially expressed between both variants using a cDNA microarray including 6386 cancer-implicated genes. The experiments showed that resistance to interferon-alpha is consistently associated with changes in the expression of a set of 39 genes, involved in signal transduction, apoptosis, transcription regulation, and cell growth. Additional studies performed confirm that STAT1 and STAT3 expression and interferon-alpha induction and activation are not altered between both variants. The gene MAL, highly overexpressed by resistant cells, was also found to be expressed by tumoral cells in a series of cutaneous T-cell lymphoma patients treated with interferon-alpha and/or photochemotherapy. MAL expression was associated with longer time to complete remission. Time-course experiments of the sensitive and resistant cells showed a differential expression of a subset of genes involved in interferon-response (1 to 4 hours), cell growth and apoptosis (24 to 48 hours.), and signal transduction.10aAntineoplastic Agents/*pharmacology/therapeutic use Carrier Proteins/biosynthesis/genetics DNA-Binding Proteins/biosynthesis/genetics Drug Resistance10aBiological Oligonucleotide Array Sequence Analysis RNA10aCultured10aCutaneous/diagnosis/drug therapy/*genetics/metabolism *Membrane Glycoproteins Models10aInterleukin-1 Reproducibility of Results STAT1 Transcription Factor STAT3 Transcription Factor Trans-Activators/biosynthesis/genetics Tumor Cells10aNeoplasm Gene Expression Profiling *Gene Expression Regulation10aNeoplasm/biosynthesis *Receptors10aNeoplastic Humans Interferon-alpha/*pharmacology/therapeutic use Kinetics Lymphoma10aT-Cell1 aTracey, L.1 aVilluendas, R.1 aOrtiz, P.1 aDopazo, A.1 aSpiteri, I.1 aLombardia, L.1 aRodriguez-Peralto, J., L.1 aFernandez-Herrera, J.1 aHernandez, A.1 aFraga, J.1 aDominguez, O.1 aHerrero, J.1 aAlonso, M., A.1 aDopazo, J.1 aPiris, M., A. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1241452900792nam a2200169 4500008004100000245017300041210006900214260003900283300001300322100001900335700002600354700001900380700002000399700002000419700002000439856016300459 2002 eng d00aMethods of Microarray Data Analysis IISupervised Neural Networks for Clustering Conditions in DNA Array Data After Reducing Noise by Clustering Gene Expression Profiles0 aMethods of Microarray Data Analysis IISupervised Neural Networks aBostonbKluwer Academic Publishers a91 - 1031 aLin, Simon, M.1 aJohnson, Kimberly, F.1 aMateos, Alvaro1 aHerrero, Javier1 aTamames, Javier1 aDopazo, Joaquin uhttp://www.springerlink.com/index/10.1007/b112982http://link.springer.com/10.1007/0-306-47598-7_7http://www.springerlink.com/index/pdf/10.1007/0-306-47598-7_700612nas a2200145 4500008004100000245013500041210006900176260002000245300001100265100001500276700001600291700001600307700001500323856012800338 2002 eng d00aSupervised Neural Networks For Clustering Conditions In DNA Array Data After Reducing Noise By Clustering Gene Expression Profiles0 aSupervised Neural Networks For Clustering Conditions In DNA Arra bKluwer Academic a91-1031 aMateos, A.1 aHerrero, J.1 aTamames, J.1 aDopazo, J. uhttps://www.clinbioinfosspa.es/content/supervised-neural-networks-clustering-conditions-dna-array-data-after-reducing-noise00641nas a2200133 4500008004100000245018000041210006900221260003200290100001400322700001500336700001600351700001500367856012500382 2002 eng d00aUnsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data0 aUnsupervised reduction of the dimensionality followed by supervi aMartigny, SwitzerlandbIEEE1 aConde, L.1 aMateos, A.1 aHerrero, J.1 aDopazo, J. uhttp://ieeexplore.ieee.org/document/1030019/http://xplorestaging.ieee.org/ielx5/8007/22134/01030019.pdf?arnumber=103001900621nas a2200133 4500008004100000245016500041210006900206260002000275300001200295100001500307700001600322700001500338856013400353 2002 eng d00aUsing perceptrons for supervised classification of DNA microarray samples: obtaining the optimal level of information and finding differentially expressed genes0 aUsing perceptrons for supervised classification of DNA microarra bJ.R. Dorronsoro a577-5821 aMateos, A.1 aHerrero, J.1 aDopazo, J. uhttps://www.clinbioinfosspa.es/content/using-perceptrons-supervised-classification-dna-microarray-samples-obtaining-optimal-level02311nas a2200361 4500008004100000245009500041210006900136300001100205490000600216520111600222653009801338653003901436653003101475653000801506653005901514100001501573700001601588700001601604700001601620700001601636700001601652700001701668700002101685700001501706700002101721700001601742700001401758700001401772700001501786700001601801700002601817856010601843 2001 eng d00aAnnotated draft genomic sequence from a Streptococcus pneumoniae type 19F clinical isolate0 aAnnotated draft genomic sequence from a Streptococcus pneumoniae a99-1250 v73 aThe public availability of numerous microbial genomes is enabling the analysis of bacterial biology in great detail and with an unprecedented, organism-wide and taxon-wide, broad scope. Streptococcus pneumoniae is one of the most important bacterial pathogens throughout the world. We present here sequences and functional annotations for 2.1-Mbp of pneumococcal DNA, covering more than 90% of the total estimated size of the genome. The sequenced strain is a clinical isolate resistant to macrolides and tetracycline. It carries a type 19F capsular locus, but multilocus sequence typing for several conserved genetic loci suggests that the strain sequenced belongs to a pneumococcal lineage that most often expresses a serotype 15 capsular polysaccharide. A total of 2,046 putative open reading frames (ORFs) longer than 100 amino acids were identified (average of 1,009 bp per ORF), including all described two-component systems and aminoacyl tRNA synthetases. Comparisons to other complete, or nearly complete, bacterial genomes were made and are presented in a graphical form for all the predicted proteins.10aBacterial Molecular Sequence Data Pneumococcal Infections/*microbiology Prokaryotic Cells RNA10aBacterial/chemistry/genetics Genes10aBacterial/genetics *Genome10aDNA10aTransfer/metabolism Streptococcus pneumoniae/*genetics1 aDopazo, J.1 aMendoza, A.1 aHerrero, J.1 aCaldara, F.1 aHumbert, Y.1 aFriedli, L.1 aGuerrier, M.1 aGrand-Schenk, E.1 aGandin, C.1 ade Francesco, M.1 aPolissi, A.1 aBuell, G.1 aFeger, G.1 aGarcia, E.1 aPeitsch, M.1 aGarcia-Bustos, J., F. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1144234802638nas a2200157 4500008004100000245009500041210006900136300001100205490000700216520196500223653013802188100001602326700001702342700001502359856010602374 2001 eng d00aA hierarchical unsupervised growing neural network for clustering gene expression patterns0 ahierarchical unsupervised growing neural network for clustering a126-360 v173 aMOTIVATION: We describe a new approach to the analysis of gene expression data coming from DNA array experiments, using an unsupervised neural network. DNA array technologies allow monitoring thousands of genes rapidly and efficiently. One of the interests of these studies is the search for correlated gene expression patterns, and this is usually achieved by clustering them. The Self-Organising Tree Algorithm, (SOTA) (Dopazo,J. and Carazo,J.M. (1997) J. Mol. Evol., 44, 226-233), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network. RESULTS: SOTA clustering confers several advantages over classical hierarchical clustering methods. SOTA is a divisive method: the clustering process is performed from top to bottom, i.e. the highest hierarchical levels are resolved before going to the details of the lowest levels. The growing can be stopped at the desired hierarchical level. Moreover, a criterion to stop the growing of the tree, based on the approximate distribution of probability obtained by randomisation of the original data set, is provided. By means of this criterion, a statistical support for the definition of clusters is proposed. In addition, obtaining average gene expression patterns is a built-in feature of the algorithm. Different neurons defining the different hierarchical levels represent the averages of the gene expression patterns contained in the clusters. Since SOTA runtimes are approximately linear with the number of items to be classified, it is especially suitable for dealing with huge amounts of data. The method proposed is very general and applies to any data providing that they can be coded as a series of numbers and that a computable measure of similarity between data items can be used. AVAILABILITY: A server running the program can be found at: http://bioinfo.cnio.es/sotarray.10a*Algorithms Automatic Data Processing *Gene Expression Profiling *Neural Networks (Computer) *Oligonucleotide Array Sequence Analysis1 aHerrero, J.1 aValencia, A.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11238068