02250nas 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 a
The 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-genetic08213nas 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-mechanisms03248nas 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/s41588-021-00791-505409nas 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-checklist03601nas a2200505 4500008004100000022001400041245013900055210006900194260001500263300000700278490000700285520188200292653001702174653002602191653001402217653003202231653000902263653001102272653001502283653001702298653003202315653002902347653001402376100003502390700003102425700003302456700002002489700001802509700002802527700003202555700002902587700003002616700002602646700001902672700003602691700002202727700002902749700002802778700003102806700002602837700002602863700003302889700004002922856013302962 2021 eng d a1528-365800aTaxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.0 aTaxonomic variations in the gut microbiome of gout patients with c2021 05 24 a500 v273 aOBJECTIVE: To evaluate the taxonomic composition of the gut microbiome in gout patients with and without tophi formation, and predict bacterial functions that might have an impact on urate metabolism.
METHODS: Hypervariable V3-V4 regions of the bacterial 16S rRNA gene from fecal samples of gout patients with and without tophi (n = 33 and n = 25, respectively) were sequenced and compared to fecal samples from 53 healthy controls. We explored predictive functional profiles using bioinformatics in order to identify differences in taxonomy and metabolic pathways.
RESULTS: We identified a microbiome characterized by the lowest richness and a higher abundance of Phascolarctobacterium, Bacteroides, Akkermansia, and Ruminococcus_gnavus_group genera in patients with gout without tophi when compared to controls. The Proteobacteria phylum and the Escherichia-Shigella genus were more abundant in patients with tophaceous gout than in controls. Fold change analysis detected nine genera enriched in healthy controls compared to gout groups (Bifidobacterium, Butyricicoccus, Oscillobacter, Ruminococcaceae_UCG_010, Lachnospiraceae_ND2007_group, Haemophilus, Ruminococcus_1, Clostridium_sensu_stricto_1, and Ruminococcaceae_UGC_013). We found that the core microbiota of both gout groups shared Bacteroides caccae, Bacteroides stercoris ATCC 43183, and Bacteroides coprocola DSM 17136. These bacteria might perform functions linked to one-carbon metabolism, nucleotide binding, amino acid biosynthesis, and purine biosynthesis. Finally, we observed differences in key bacterial enzymes involved in urate synthesis, degradation, and elimination.
CONCLUSION: Our findings revealed that taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.
10aBiodiversity10aComputational Biology10aDysbiosis10aGastrointestinal Microbiome10aGout10aHumans10aMetagenome10ametagenomics10aProtein Interaction Mapping10aProtein Interaction Maps10aUric Acid1 aMéndez-Salazar, Eder, Orlando1 aVázquez-Mellado, Janitzia1 aCasimiro-Soriguer, Carlos, S1 aDopazo, Joaquin1 aCubuk, Cankut1 aZamudio-Cuevas, Yessica1 aFrancisco-Balderas, Adriana1 aMartínez-Flores, Karina1 aFernández-Torres, Javier1 aLozada-Pérez, Carlos1 aPineda, Carlos1 aSánchez-González, Austreberto1 aSilveira, Luis, H1 aBurguete-García, Ana, I1 aOrbe-Orihuela, Citlalli1 aLagunas-Martínez, Alfredo1 aVazquez-Gomez, Alonso1 aLópez-Reyes, Alberto1 aPalacios-González, Berenice1 aMartínez-Nava, Gabriela, Angélica uhttps://www.clinbioinfosspa.es/content/taxonomic-variations-gut-microbiome-gout-patients-and-without-tophi-might-have-functional00678nas 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/s41467-021-21254-903269nas a2200685 4500008004100000022001400041245011800055210006900173260001500242300000900257490000700266520115400273653001901427653005101446653001701497653002201514653002101536653002601557653002201583653002001605653003001625653001901655653001301674653001101687653003101698653001301729653001401742653002101756653003501777653004101812653002201853100002301875700001701898700001901915700001801934700002301952700001901975700001501994700001702009700001602026700002002042700001602062700002402078700001902102700001802121700002402139700001802163700002502181700001702206700001902223700002302242700002702265700002002292700002502312700002002337700002102357700002602378710005702404856012202461 2019 eng d a2041-172300aCommunity assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.0 aCommunity assessment to advance computational prediction of canc c2019 06 17 a26740 v103 aThe effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
10aADAM17 Protein10aAntineoplastic Combined Chemotherapy Protocols10aBenchmarking10aBiomarkers, Tumor10aCell Line, Tumor10aComputational Biology10aDatasets as Topic10aDrug Antagonism10aDrug Resistance, Neoplasm10aDrug Synergism10aGenomics10aHumans10aMolecular Targeted Therapy10amutation10aNeoplasms10apharmacogenetics10aPhosphatidylinositol 3-Kinases10aPhosphoinositide-3 Kinase Inhibitors10aTreatment Outcome1 aMenden, Michael, P1 aWang, Dennis1 aMason, Mike, J1 aSzalai, Bence1 aBulusu, Krishna, C1 aGuan, Yuanfang1 aYu, Thomas1 aKang, Jaewoo1 aJeon, Minji1 aWolfinger, Russ1 aNguyen, Tin1 aZaslavskiy, Mikhail1 aJang, In, Sock1 aGhazoui, Zara1 aAhsen, Mehmet, Eren1 aVogel, Robert1 aNeto, Elias, Chaibub1 aNorman, Thea1 aK Y Tang, Eric1 aGarnett, Mathew, J1 aDi Veroli, Giovanni, Y1 aFawell, Stephen1 aStolovitzky, Gustavo1 aGuinney, Justin1 aDry, Jonathan, R1 aSaez-Rodriguez, Julio1 aAstraZeneca-Sanger Drug Combination DREAM Consortium uhttps://www.clinbioinfosspa.es/content/community-assessment-advance-computational-prediction-cancer-drug-combinations03226nas a2200697 4500008004100000022001400041245013600055210006900191260001500260300001000275490000600285520117300291653000901464653001201473653002601485653002001511653001901531653003801550653001801588653002801606653001001634653001701644653001101661653003101672653002101703653002501724653001501749653001101764653000901775653000901784653001601793653003601809653003201845653001101877653002401888653003601912653002501948653001001973653002101983653002602004100001402030700002402044700001602068700001602084700001702100700002402117700002702141700002402168700002102192700001302213700002302226700001402249700002702263700002002290700002302310700001402333700002402347700001402371700001302385856013002398 2016 eng d a2045-232200aIdentification of the Photoreceptor Transcriptional Co-Repressor SAMD11 as Novel Cause of Autosomal Recessive Retinitis Pigmentosa.0 aIdentification of the Photoreceptor Transcriptional CoRepressor c2016 10 13 a353700 v63 aRetinitis pigmentosa (RP), the most frequent form of inherited retinal dystrophy is characterized by progressive photoreceptor degeneration. Many genes have been implicated in RP development, but several others remain to be identified. Using a combination of homozygosity mapping, whole-exome and targeted next-generation sequencing, we found a novel homozygous nonsense mutation in SAMD11 in five individuals diagnosed with adult-onset RP from two unrelated consanguineous Spanish families. SAMD11 is ortholog to the mouse major retinal SAM domain (mr-s) protein that is implicated in CRX-mediated transcriptional regulation in the retina. Accordingly, protein-protein network analysis revealed a significant interaction of SAMD11 with CRX. Immunoblotting analysis confirmed strong expression of SAMD11 in human retina. Immunolocalization studies revealed SAMD11 was detected in the three nuclear layers of the human retina and interestingly differential expression between cone and rod photoreceptors was observed. Our study strongly implicates SAMD11 as novel cause of RP playing an important role in the pathogenesis of human degeneration of photoreceptors.
10aAged10aAnimals10aCo-Repressor Proteins10aCodon, Nonsense10aCohort Studies10aComparative Genomic Hybridization10aConsanguinity10aDNA Mutational Analysis10aExome10aEye Proteins10aFemale10aGene Expression Regulation10aGenes, Recessive10aHomeodomain Proteins10aHomozygote10aHumans10aMale10aMice10aMiddle Aged10aPolymorphism, Single Nucleotide10aProtein Interaction Mapping10aRetina10aRetinal Dystrophies10aRetinal Rod Photoreceptor Cells10aRetinitis pigmentosa10aSpain10aTrans-Activators10aTranscription Factors1 aCorton, M1 aAvila-Fernández, A1 aCampello, L1 aSánchez, M1 aBenavides, B1 aLópez-Molina, M, I1 aFernández-Sánchez, L1 aSánchez-Alcudia, R1 ada Silva, L, R J1 aReyes, N1 aMartín-Garrido, E1 aZurita, O1 aSan José, Fernández-1 aPérez-Carro, R1 aGarcía-García, F1 aDopazo, J1 aGarcía-Sandoval, B1 aCuenca, N1 aAyuso, C uhttps://www.clinbioinfosspa.es/content/identification-photoreceptor-transcriptional-co-repressor-samd11-novel-cause-autosomal02147nas 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.html07842nas 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.html03045nas a2200565 4500008004100000022001400041245009600055210006900151260001300220300001400233490000700247520132400254653002401578653001201602653002501614653002801639653002401667653002501691653001701716653001101733653002101744653002201765653001101787653000901798653002801807653001301835653001301848653003601861653003201897653002501929653001001954653003301964100002201997700001902019700002602038700003302064700002002097700001802117700002302135700002202158700001802180700002402198700001902222700002102241700002202262700002002284700002702304700002502331856012302356 2010 eng d a1098-100400aMutation spectrum of EYS in Spanish patients with autosomal recessive retinitis pigmentosa.0 aMutation spectrum of EYS in Spanish patients with autosomal rece c2010 Nov aE1772-8000 v313 aRetinitis pigmentosa (RP) is a heterogeneous group of inherited retinal dystrophies characterised ultimately by the loss of photoreceptor cells. We have recently identified a new gene(EYS) encoding an ortholog of Drosophila space maker (spam) as a commonly mutated gene in autosomal recessive RP. In the present study, we report the identification of 73 sequence variations in EYS, of which 28 are novel. Of these, 42.9% (12/28) are very likely pathogenic, 17.9% (5/28)are possibly pathogenic, whereas 39.3% (11/28) are SNPs. In addition, we have detected 3 pathogenic changes previously reported in other populations. We are also presenting the characterisation of EYS homologues in different species, and a detailed analysis of the EYS domains, with the identification of an interesting novel feature: a putative coiled-coil domain.Majority of the mutations in the arRP patients have been found within the domain structures of EYS. The minimum observed prevalence of distinct EYS mutations in our group of patients is of 15.9% (15/94), confirming a major involvement of EYS in the pathogenesis of arRP in the Spanish population. Along with the detection of three recurrent mutations in Caucasian population, our hypothesis of EYS being the first prevalent gene in arRP has been reinforced in the present study.
10aAmino Acid Sequence10aAnimals10aCase-Control Studies10aDNA Mutational Analysis10aDrosophila Proteins10aEvolution, Molecular10aEye Proteins10aFemale10aGenes, Recessive10aGenetic Variation10aHumans10aMale10aMolecular Sequence Data10amutation10aPedigree10aPolymorphism, Single Nucleotide10aProtein Structure, Tertiary10aRetinitis pigmentosa10aSpain10aStructural Homology, Protein1 aBarragán, Isabel1 aBorrego, Salud1 aPieras, Juan, Ignacio1 adel Pozo, María, González-1 aSantoyo, Javier1 aAyuso, Carmen1 aBaiget, Montserrat1 aMillán, José, M1 aMena, Marcela1 aEl-Aziz, Mai, M Abd1 aAudo, Isabelle1 aZeitz, Christina1 aLittink, Karin, W1 aDopazo, Joaquin1 aBhattacharya, Shomi, S1 aAntiňolo, Guillermo uhttps://www.clinbioinfosspa.es/content/mutation-spectrum-eys-spanish-patients-autosomal-recessive-retinitis-pigmentosa03690nas 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=1823880403454nas 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=1844359403705nas 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=1583012801764nas a2200169 4500008004100000245006700041210006700108300001100175490000800186520121300194100001501407700001601422700001601438700001701454700001701471856010601488 2001 eng d00aMethods and approaches in the analysis of gene expression data0 aMethods and approaches in the analysis of gene expression data a93-1120 v2503 aThe application of high-density DNA array technology to monitor gene transcription has been responsible for a real paradigm shift in biology. The majority of research groups now have the ability to measure the expression of a significant proportion of the human genome in a single experiment, resulting in an unprecedented volume of data being made available to the scientific community. As a consequence of this, the storage, analysis and interpretation of this information present a major challenge. In the field of immunology the analysis of gene expression profiles has opened new areas of investigation. The study of cellular responses has revealed that cells respond to an activation signal with waves of co-ordinated gene expression profiles and that the components of these responses are the key to understanding the specific mechanisms which lead to phenotypic differentiation. The discovery of ’cell type specific’ gene expression signatures have also helped the interpretation of the mechanisms leading to disease progression. Here we review the principles behind the most commonly used data analysis methods and discuss the approaches that have been employed in immunological research.
1 aDopazo, J.1 aZanders, E.1 aDragoni, I.1 aAmphlett, G.1 aFalciani, F. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11251224