00806nas 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/S200103702100203800926nas a2200229 4500008004100000022001400041245021100055210006900266260001600335490000700351100002900358700002600387700002000413700003400433700002300467700003100490700003300521700002500554700002000579700001900599856007800618 2021 eng d a1868-707500aGenome-wide analysis of DNA methylation in Hirschsprung enteric precursor cells: unraveling the epigenetic landscape of enteric nervous system developmentAbstractBackgroundResultsConclusionsGraphic abstract0 aGenomewide analysis of DNA methylation in Hirschsprung enteric p cJan-12-20210 v131 aVillalba-Benito, Leticia1 aLópez-López, Daniel1 aTorroglosa, Ana1 aCasimiro-Soriguer, Carlos, S.1 aLuzón-Toro, Berta1 aFernández, Raquel, María1 aMoya-Jiménez, María, José1 aAntiňolo, Guillermo1 aDopazo, Joaquin1 aBorrego, Salud uhttp://link.springer.com/article/10.1186/s13148-021-01040-6/fulltext.html02950nas 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 a
Metabolic 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-landscape02581nas a2200517 4500008004100000022001400041245005200055210005100107260001500158300001200173490000800185520112500193653002301318653001701341653001101358653002001369653002501389653002301414653001801437653001301455653001501468653001701483653002101500653002001521653002701541653001401568100002401582700001801606700002201624700002801646700002601674700002101700700002001721700002401741700003101765700002001796700001701816700001801833700002401851700002101875700002001896700002601916700002301942700001801965856008001983 2018 eng d a1476-468700aGenomics of the origin and evolution of Citrus.0 aGenomics of the origin and evolution of Citrus c2018 02 15 a311-3160 v5543 aThe genus Citrus, comprising some of the most widely cultivated fruit crops worldwide, includes an uncertain number of species. Here we describe ten natural citrus species, using genomic, phylogenetic and biogeographic analyses of 60 accessions representing diverse citrus germ plasms, and propose that citrus diversified during the late Miocene epoch through a rapid southeast Asian radiation that correlates with a marked weakening of the monsoons. A second radiation enabled by migration across the Wallace line gave rise to the Australian limes in the early Pliocene epoch. Further identification and analyses of hybrids and admixed genomes provides insights into the genealogy of major commercial cultivars of citrus. Among mandarins and sweet orange, we find an extensive network of relatedness that illuminates the domestication of these groups. Widespread pummelo admixture among these mandarins and its correlation with fruit size and acidity suggests a plausible role of pummelo introgression in the selection of palatable mandarins. This work provides a new evolutionary framework for the genus Citrus.
10aAsia, Southeastern10aBiodiversity10acitrus10aCrop Production10aEvolution, Molecular10aGenetic Speciation10aGenome, Plant10aGenomics10aHaplotypes10aHeterozygote10aHistory, Ancient10aHuman Migration10aHybridization, Genetic10aPhylogeny1 aWu, Guohong, Albert1 aTerol, Javier1 aIbañez, Victoria1 aLópez-García, Antonio1 aPérez-Román, Estela1 aBorredá, Carles1 aDomingo, Concha1 aTadeo, Francisco, R1 aCarbonell-Caballero, José1 aAlonso, Roberto1 aCurk, Franck1 aDu, Dongliang1 aOllitrault, Patrick1 aRoose, Mikeal, L1 aDopazo, Joaquin1 aGmitter, Frederick, G1 aRokhsar, Daniel, S1 aTalon, Manuel uhttps://www.clinbioinfosspa.es/content/genomics-origin-and-evolution-citrus02800nas a2200445 4500008004100000022001400041245015700055210006900212260001600281300001600297490000600313520134500319653001001664653002501674653002601699653002001725653003801745653001301783653001501796653001101811653001801822653001601840653001601856653001401872653003501886100003001921700002401951700002201975700002601997700002902023700002202052700002602074700002502100700002402125700002102149700002002170700001802190700001702208856012902225 2017 eng d a1949-255300aGenomic expression differences between cutaneous cells from red hair color individuals and black hair color individuals based on bioinformatic analysis.0 aGenomic expression differences between cutaneous cells from red c2017 Feb 14 a11589-115990 v83 aThe MC1R gene plays a crucial role in pigmentation synthesis. Loss-of-function MC1R variants, which impair protein function, are associated with red hair color (RHC) phenotype and increased skin cancer risk. Cultured cutaneous cells bearing loss-of-function MC1R variants show a distinct gene expression profile compared to wild-type MC1R cultured cutaneous cells. We analysed the gene signature associated with RHC co-cultured melanocytes and keratinocytes by Protein-Protein interaction (PPI) network analysis to identify genes related with non-functional MC1R variants. From two detected networks, we selected 23 nodes as hub genes based on topological parameters. Differential expression of hub genes was then evaluated in healthy skin biopsies from RHC and black hair color (BHC) individuals. We also compared gene expression in melanoma tumors from individuals with RHC versus BHC. Gene expression in normal skin from RHC cutaneous cells showed dysregulation in 8 out of 23 hub genes (CLN3, ATG10, WIPI2, SNX2, GABARAPL2, YWHA, PCNA and GBAS). Hub genes did not differ between melanoma tumors in RHC versus BHC individuals. The study suggests that healthy skin cells from RHC individuals present a constitutive genomic deregulation associated with the red hair phenotype and identify novel genes involved in melanocyte biology.
10aAdult10aCoculture Techniques10aComputational Biology10agene expression10aGenetic Predisposition to Disease10aGenomics10aHair Color10aHumans10aKeratinocytes10aMelanocytes10aMiddle Aged10aPhenotype10aReceptor, Melanocortin, Type 11 aPuig-Butille, Joan, Anton1 aGimenez-Xavier, Pol1 aVisconti, Alessia1 aNsengimana, Jérémie1 aGarcia-Garcia, Francisco1 aTell-Marti, Gemma1 aEscamez, Maria, José1 aNewton-Bishop, Julia1 aBataille, Veronique1 aDel Rio, Marcela1 aDopazo, Joaquin1 aFalchi, Mario1 aPuig, Susana uhttp://www.impactjournals.com/oncotarget/index.php?journal=oncotarget&page=article&op=view&path%5B%5D=14140&path%5B%5D=4509401430nas a2200481 4500008004100000022001400041245007200055210006900127260001500196300001400211490000800225653000900233653002400242653003700266653003400303653001900337653001000356653002900366653001100395653002200406653002800428653001100456653001600467653001300483100002100496700002100517700002800538700002700566700002600593700002000619700002000639700002000659700002900679700002000708700002600728700002800754700002200782700002400804700002000828700002100848700002500869856005400894 2017 eng d a1533-440600aGGPS1 Mutation and Atypical Femoral Fractures with Bisphosphonates.0 aGGPS1 Mutation and Atypical Femoral Fractures with Bisphosphonat c2017 05 04 a1794-17950 v37610aAged10aAmino Acid Sequence10aBone Density Conservation Agents10aDimethylallyltranstransferase10aDiphosphonates10aExome10aFarnesyltranstransferase10aFemale10aFemoral Fractures10aGeranyltranstransferase10aHumans10aMiddle Aged10amutation1 aRoca-Ayats, Neus1 aBalcells, Susana1 aGarcia-Giralt, Natàlia1 aFalcó-Mascaró, Maite1 aMartínez-Gil, Núria1 aAbril, Josep, F1 aUrreizti, Roser1 aDopazo, Joaquin1 aQuesada-Gómez, José, M1 aNogués, Xavier1 aMellibovsky, Leonardo1 aPrieto-Alhambra, Daniel1 aDunford, James, E1 aJavaid, Muhammad, K1 aRussell, Graham1 aGrinberg, Daniel1 aDíez-Pérez, Adolfo uhttp://www.nejm.org/doi/full/10.1056/NEJMc161280402585nas a2200349 4500008004100000022001400041245014600055210006900201260001500270300001200285490000700297520142200304653001201726653001501738653002001753653002001773653003501793653003001828653002101858653002401879653002301903653001201926653001601938653001801954653002101972100002301993700002902016700002002045700001702065700001902082856013402101 2017 eng d a1460-219900aGlobal Transcriptome Analysis of Primary Cerebrocortical Cells: Identification of Genes Regulated by Triiodothyronine in Specific Cell Types.0 aGlobal Transcriptome Analysis of Primary Cerebrocortical Cells I c2017 01 01 a706-7170 v273 aThyroid hormones, thyroxine, and triiodothyronine (T3) are crucial for cerebral cortex development acting through regulation of gene expression. To define the transcriptional program under T3 regulation, we have performed RNA-Seq of T3-treated and untreated primary mouse cerebrocortical cells. The expression of 1145 genes or 7.7% of expressed genes was changed upon T3 addition, of which 371 responded to T3 in the presence of cycloheximide indicating direct transcriptional regulation. The results were compared with available transcriptomic datasets of defined cellular types. In this way, we could identify targets of T3 within genes enriched in astrocytes and neurons, in specific layers including the subplate, and in specific neurons such as prepronociceptin, cholecystokinin, or cortistatin neurons. The subplate and the prepronociceptin neurons appear as potentially major targets of T3 action. T3 upregulates mostly genes related to cell membrane events, such as G-protein signaling, neurotransmission, and ion transport and downregulates genes involved in nuclear events associated with the M phase of cell cycle, such as chromosome organization and segregation. Remarkably, the transcriptomic changes induced by T3 sustain the transition from fetal to adult patterns of gene expression. The results allow defining in molecular terms the elusive role of thyroid hormones on neocortical development.
10aAnimals10aAstrocytes10aCells, Cultured10aCerebral Cortex10aFluorescent Antibody Technique10aGene Expression Profiling10aMice, 129 Strain10aMice, Inbred BALB C10aMice, Inbred C57BL10aNeurons10aPiperazines10aTranscriptome10aTriiodothyronine1 aGil-Ibañez, Pilar1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aBernal, Juan1 aMorte, Beatriz uhttps://www.clinbioinfosspa.es/content/global-transcriptome-analysis-primary-cerebrocortical-cells-identification-genes-regulated00523nas a2200121 4500008004100000245008800041210006900129260001600198490000700214100002000221700001700241856014300258 2017 eng d00aGraph-theoretical comparison of normal and tumor networks in identifying BRCA genes0 aGraphtheoretical comparison of normal and tumor networks in iden cJan-12-20170 v111 aDopazo, Joaquin1 aErten, Cesim uhttps://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-017-0495-0http://link.springer.com/content/pdf/10.1186/s12918-017-0495-0.pdf03368nas a2200241 4500008004100000022001400041245008900055210006900144260001600213300000800229490000700237520258700244653002102831653001102852653002902863653002802892653001102920653002402931653001302955100002002968700001702988856012103005 2017 eng d a1752-050900aGraph-theoretical comparison of normal and tumor networks in identifying BRCA genes.0 aGraphtheoretical comparison of normal and tumor networks in iden c2017 Nov 22 a1100 v113 aBACKGROUND: Identification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally.
RESULTS: We provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. The network measures under consideration are based on appropriate formulations of various centrality measures: betweenness, clustering coefficients, degree centrality, random walk distances, graph-theoretical distances, and Jaccard index centrality.
CONCLUSIONS: Among all the studied centrality-based graph-theoretical properties, we show that a betweenness-based measure differentiates BRCA genes across all normal versus tumor network pairs, than the rest of the popular centrality-based measures. The AUROC and AUPR values of the gene lists ordered with respect to the measures under study as compared to NCBI BioSystems pathway and the COSMIC database of cancer genes are the largest with the betweenness-based differentiation, followed by the measure based on degree centrality. In order to test the robustness of the suggested measures in prioritizing cancer genes, we further tested the two most promising measures, those based on betweenness and degree centralities, on randomly rewired networks. We show that both measures are quite resilient to noise in the input interaction network. We also compared the same measures against a state-of-the-art alternative disease gene prioritization method, MUFFFINN. We show that both our graph-theoretical measures outperform MUFFINN prioritizations in terms of ROC and precions/recall analysis. Finally, we filter the ordered list of the best measure, the betweenness-based differentiation, via a maximum-weight independent set formulation and investigate the top 50 genes in regards to literature verification. We show that almost all genes in the list are verified by the breast cancer literature and three genes are presented as novel genes that may potentialy be BRCA-related but missing in literature.
10aBreast Neoplasms10aFemale10aGene Regulatory Networks10aGenes, Tumor Suppressor10aHumans10aModels, Theoretical10amutation1 aDopazo, Joaquin1 aErten, Cesim uhttps://www.clinbioinfosspa.es/content/graph-theoretical-comparison-normal-and-tumor-networks-identifying-brca-genes01427nas a2200229 4500008004100000022001400041245005200055210005100107260001600158300001100174490000700185520080300192653002000995653001901015653002301034653001701057653001301074653002101087653002101108100002001129856004801149 2014 eng d a1878-583200aGenomics and transcriptomics in drug discovery.0 aGenomics and transcriptomics in drug discovery c2013 Jun 14 a126-320 v193 aThe popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery.10aadverse effects10aDrug discovery10adrug repositioning10ametagenomics10amodeling10anetwork analysis10apathway analysis1 aDopazo, Joaquin uhttp://www.ncbi.nlm.nih.gov/pubmed/2377386002468nas a2200325 4500008004100000022001400041245005000055210004800105260001500153300001200168490000700180520163100187653000801818653001801826653001001844653001501854653003101869653000801900653000801908653000801916100002001924700002401944700002101968700002401989700002002013700001902033700001702052700002102069856005202090 2013 eng d a1362-496200aGenome Maps, a new generation genome browser.0 aGenome Maps a new generation genome browser c2013 Jun 8 aW41-W460 v413 aGenome browsers have gained importance as more genomes and related genomic information become available. However, the increase of information brought about by new generation sequencing technologies is, at the same time, causing a subtle but continuous decrease in the efficiency of conventional genome browsers. Here, we present Genome Maps, a genome browser that implements an innovative model of data transfer and management. The program uses highly efficient technologies from the new HTML5 standard, such as scalable vector graphics, that optimize workloads at both server and client sides and ensure future scalability. Thus, data management and representation are entirely carried out by the browser, without the need of any Java Applet, Flash or other plug-in technology installation. Relevant biological data on genes, transcripts, exons, regulatory features, single-nucleotide polymorphisms, karyotype and so forth, are imported from web services and are available as tracks. In addition, several DAS servers are already included in Genome Maps. As a novelty, this web-based genome browser allows the local upload of huge genomic data files (e.g. VCF or BAM) that can be dynamically visualized in real time at the client side, thus facilitating the management of medical data affected by privacy restrictions. Finally, Genome Maps can easily be integrated in any web application by including only a few lines of code. Genome Maps is an open source collaborative initiative available in the GitHub repository (https://github.com/compbio-bigdata-viz/genome-maps). Genome Maps is available at: http://www.genomemaps.org.10aBAM10agenome viewer10aHTML510ajavascript10aNext Generation Sequencing10aNGS10aSVG10aVCF1 aMedina, Ignacio1 aSalavert, Francisco1 aSánchez, Rubén1 aDe Maria, Alejandro1 aAlonso, Roberto1 aEscobar, Pablo1 aBleda, Marta1 aDopazo, Joaquín uhttp://nar.oxfordjournals.org/content/41/W1/W4102403nas a2200229 4500008004100000022001400041245013500055210006900190260001600259520156900275100002601844700002301870700002001893700002801913700002901941700002101970700002601991700002902017700002202046700002002068856008502088 2013 eng d a1460-218000aGrape antioxidant dietary fiber (GADF) inhibits intestinal polyposis in ApcMin/+ mice: relation to cell cycle and immune response.0 aGrape antioxidant dietary fiber GADF inhibits intestinal polypos c2013 Apr 243 aEpidemiological and experimental studies suggest that fiber and phenolic compounds might have a protective effect on the development of colon cancer in humans. Accordingly, we assessed the chemopreventive efficacy and associated mechanisms of action of a lyophilized red grape pomace containing proanthocyanidin-rich dietary fiber (Grape Antioxidant Dietary Fiber, GADF) on spontaneous intestinal tumorigenesis in the Apc(Min/+) mouse model. Mice were fed a standard diet (control group) or a 1% (w/w) GADF-supplemented diet (GADF group) for 6 weeks. GADF supplementation greatly reduced intestinal tumorigenesis, significantly decreasing the total number of polyps by 76%. Moreover, size distribution analysis showed a considerable reduction in all polyp size categories [diameter <1 mm (65%), 1-2 mm (67%) and >2 mm (87%)]. In terms of polyp formation in the proximal, middle and distal portions of the small intestine a decrease of 76%, 81% and 73% was observed respectively. Putative molecular mechanisms underlying the inhibition of intestinal tumorigenesis were investigated by comparison of microarray expression profiles of GADF-treated and non-treated mice. We observed that the effects of GADF are mainly associated with the induction of a G1 cell cycle arrest and the downregulation of genes related to the immune response and inflammation. Our findings show for the first time the efficacy and associated mechanisms of action of GADF against intestinal tumorigenesis in Apc(Min/+) mice, suggesting its potential for the prevention of colorectal cancer.1 aSánchez-Tena, Susana1 aLizarraga, Daneida1 aMiranda, Anibal1 aVinardell, Maria, Pilar1 aGarcia-Garcia, Francisco1 aDopazo, Joaquín1 aTorres, Josep, Lluís1 aSaura-Calixto, Fulgencio1 aCapellà, Gabriel1 aCascante, Marta uhttp://carcin.oxfordjournals.org/content/early/2013/04/23/carcin.bgt140.abstract03047nas a2200481 4500008004100000022001400041245012800055210006900183260001300252300001100265490000700276520158000283653001201863653001701875653001601892653001901908653001501927653002701942653002501969653001801994653002402012653002002036653001302056653001702069653002502086653002202111653002102133653000902154653000902163653001802172653001002190100002602200700002302226700002002249700002402269700002902293700002002322700002102342700002902363700002202392700002002414856013102434 2013 eng d a1460-218000aGrape antioxidant dietary fiber inhibits intestinal polyposis in ApcMin/+ mice: relation to cell cycle and immune response.0 aGrape antioxidant dietary fiber inhibits intestinal polyposis in c2013 Aug a1881-80 v343 aEpidemiological and experimental studies suggest that fiber and phenolic compounds might have a protective effect on the development of colon cancer in humans. Accordingly, we assessed the chemopreventive efficacy and associated mechanisms of action of a lyophilized red grape pomace containing proanthocyanidin (PA)-rich dietary fiber [grape antioxidant dietary fiber (GADF)] on spontaneous intestinal tumorigenesis in the Apc(Min/+) mouse model. Mice were fed a standard diet (control group) or a 1% (w/w) GADF-supplemented diet (GADF group) for 6 weeks. GADF supplementation greatly reduced intestinal tumorigenesis, significantly decreasing the total number of polyps by 76%. Moreover, size distribution analysis showed a considerable reduction in all polyp size categories [diameter <1mm (65%), 1-2mm (67%) and >2mm (87%)]. In terms of polyp formation in the proximal, middle and distal portions of the small intestine, a decrease of 76, 81 and 73% was observed, respectively. Putative molecular mechanisms underlying the inhibition of intestinal tumorigenesis were investigated by comparison of microarray expression profiles of GADF-treated and non-treated mice. We observed that the effects of GADF are mainly associated with the induction of a G1 cell cycle arrest and the downregulation of genes related to the immune response and inflammation. Our findings show for the first time the efficacy and associated mechanisms of action of GADF against intestinal tumorigenesis in Apc(Min/+) mice, suggesting its potential for the prevention of colorectal cancer.
10aAnimals10aAntioxidants10aBody Weight10aCarcinogenesis10aCell Cycle10aCell Cycle Checkpoints10aColorectal Neoplasms10aDietary Fiber10aDietary Supplements10aDown-Regulation10aG1 Phase10aInflammation10aIntestinal Polyposis10aIntestinal Polyps10aIntestine, Small10aMale10aMice10aTranscriptome10aVitis1 aSánchez-Tena, Susana1 aLizarraga, Daneida1 aMiranda, Anibal1 aVinardell, Maria, P1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aTorres, Josep, L1 aSaura-Calixto, Fulgencio1 aCapellà, Gabriel1 aCascante, Marta uhttps://www.clinbioinfosspa.es/content/grape-antioxidant-dietary-fiber-inhibits-intestinal-polyposis-apcmin-mice-relation-cell02529nas a2200169 4500008004100000245006800041210006600109260000900175300001200184490000600196520197400202100002102176700002102197700002002218700001802238856010302256 2011 eng d00aGenome-wide heterogeneity of nucleotide substitution model fit.0 aGenomewide heterogeneity of nucleotide substitution model fit c2011 a896-9080 v33 aAt a genomic scale, the patterns that have shaped molecular evolution are believed to be largely heterogeneous. Consequently, comparative analyses should use appropriate probabilistic substitution models that capture the main features under which different genomic regions have evolved. While efforts have concentrated in the development and understanding of model selection techniques, no descriptions of overall relative substitution model fit at the genome level have been reported. Here, we provide a characterization of best-fit substitution models across three genomic data sets including coding regions from mammals, vertebrates, and Drosophila (24,000 alignments). According to the Akaike Information Criterion (AIC), 82 of 88 models considered were selected as best-fit models at least in one occasion, although with very different frequencies. Most parameter estimates also varied broadly among genes. Patterns found for vertebrates and Drosophila were quite similar and often more complex than those found in mammals. Phylogenetic trees derived from models in the 95% confidence interval set showed much less variance and were significantly closer to the tree estimated under the best-fit model than trees derived from models outside this interval. Although alternative criteria selected simpler models than the AIC, they suggested similar patterns. All together our results show that at a genomic scale, different gene alignments for the same set of taxa are best explained by a large variety of different substitution models and that model choice has implications on different parameter estimates including the inferred phylogenetic trees. After taking into account the differences related to sample size, our results suggest a noticeable diversity in the underlying evolutionary process. All together, we conclude that the use of model selection techniques is important to obtain consistent phylogenetic estimates from real data at a genomic scale.
1 aArbiza, Leonardo1 aPatricio, Mateus1 aDopazo, Hernán1 aPosada, David uhttps://www.clinbioinfosspa.es/content/genome-wide-heterogeneity-nucleotide-substitution-model-fit03387nas a2200325 4500008004100000022001400041245007200055210006900127260001600196300000800212490000700220520234600227653001502573653002102588653004402609653002602653653002802679653001102707653003002718653001302748653001102761653004402772653003002816653003102846100002002877700001902897700002502916700002002941856010002961 2009 eng d a1471-216400aGene set internal coherence in the context of functional profiling.0 aGene set internal coherence in the context of functional profili c2009 Apr 27 a1970 v103 aBACKGROUND: Functional profiling methods have been extensively used in the context of high-throughput experiments and, in particular, in microarray data analysis. Such methods use available biological information to define different types of functional gene modules (e.g. gene ontology -GO-, KEGG pathways, etc.) whose representation in a pre-defined list of genes is further studied. In the most popular type of microarray experimental designs (e.g. up- or down-regulated genes, clusters of co-expressing genes, etc.) or in other genomic experiments (e.g. Chip-on-chip, epigenomics, etc.) these lists are composed by genes with a high degree of co-expression. Therefore, an implicit assumption in the application of functional profiling methods within this context is that the genes corresponding to the modules tested are effectively defining sets of co-expressing genes. Nevertheless not all the functional modules are biologically coherent entities in terms of co-expression, which will eventually hinder its detection with conventional methods of functional enrichment.
RESULTS: Using a large collection of microarray data we have carried out a detailed survey of internal correlation in GO terms and KEGG pathways, providing a coherence index to be used for measuring functional module co-regulation. An unexpected low level of internal correlation was found among the modules studied. Only around 30% of the modules defined by GO terms and 57% of the modules defined by KEGG pathways display an internal correlation higher than the expected by chance.This information on the internal correlation of the genes within the functional modules can be used in the context of a logistic regression model in a simple way to improve their detection in gene expression experiments.
CONCLUSION: For the first time, an exhaustive study on the internal co-expression of the most popular functional categories has been carried out. Interestingly, the real level of coexpression within many of them is lower than expected (or even inexistent), which will preclude its detection by means of most conventional functional profiling methods. If the gene-to-function correlation information is used in functional profiling methods, the results obtained improve the ones obtained by conventional enrichment methods.
10aAlgorithms10aBreast Neoplasms10aCarcinoma, Intraductal, Noninfiltrating10aComputational Biology10aDatabases, Nucleic Acid10aFemale10aGene Expression Profiling10aGenomics10aHumans10aOligonucleotide Array Sequence Analysis10aPapillomavirus Infections10aReproducibility of Results1 aMontaner, David1 aMinguez, Pablo1 aAl-Shahrour, Fátima1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/gene-set-internal-coherence-context-functional-profiling02028nas a2200349 4500008004100000022001400041245014400055210006900199260001300268300001100281490000700292520085700299653002501156653002101181653001101202653001001213653002201223653003401245653001101279653003601290653001301326653002801339100002001367700002001387700002101407700002601428700002101454700002301475700002501498700002001523856013501543 2009 eng d a1362-496200aGene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies.0 aGene setbased analysis of polymorphisms finding pathways or biol c2009 Jul aW340-40 v373 aGenome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/.
10aBiological Phenomena10aBreast Neoplasms10aFemale10aGenes10aGenetic Variation10aGenome-Wide Association Study10aHumans10aPolymorphism, Single Nucleotide10aSoftware10aUser-Computer Interface1 aMedina, Ignacio1 aMontaner, David1 aBonifaci, Núria1 aPujana, Miguel, Angel1 aCarbonell, José1 aTárraga, Joaquín1 aAl-Shahrour, Fátima1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/gene-set-based-analysis-polymorphisms-finding-pathways-or-biological-processes-associated-001715nas a2200265 4500008004100000245014300041210006900184300001300253490000700266520085600273653001501129653001301144653001101157653002701168653000801195100002001203700002001223700002101243700002601264700002101290700002301311700002401334700002001358856007101378 2009 eng d00aGene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies0 aGene setbased analysis of polymorphisms finding pathways or biol aW340-3440 v373 aGenome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/
10ababelomics10agene set10aGESBAP10apathway-based analysis10aSNP1 aMedina, Ignacio1 aMontaner, David1 aBonifaci, Núria1 aPujana, Miguel, Angel1 aCarbonell, José1 aTárraga, Joaquín1 aAl-Shahrour, Fatima1 aDopazo, Joaquin uhttp://nar.oxfordjournals.org/cgi/content/abstract/37/suppl_2/W34001750nas a2200181 4500008004100000022002200041245009600063210007100159260002600230300001000256520107600266100002101342700001501363700001501378700001501393700001601408856014401424 2009 eng d a978-84-92910-06-900aGenómica Comparativa y Selección Natural. Aplicaciones en el Genoma Humano. Capítulo 1.60 aGenómica Comparativa y Selección Natural Aplicaciones en el Geno aValenciabObrapropia. a51-593 aLa búsqueda de los eventos adaptativos a nivel molecular que ha diferenciado el genoma humano del de nuestro pariente vivo más cercano, el chimpancé, ha sido una de las áreas de mayor investigación en genómica comparativa. Paralelamente, la predicción funcional de variantes genéticas en nuestra especie ha sido un área de intenso desarrollo en bioinformática. En este trabajo discutiremos resultados previos y otros más recientes que dan cuenta de estos desarrollos. Veremos que en todos los casos la estimación de las presiones selectivas a nivel de los genes individuales o de los residuos de las proteínas son el denominador común para discutir ambos aspectos. Finalmente mostraremos cómo el análisis de estas presiones selectivas por grupos funcionales de genes resulta una alternativa viable y con suficiente poder estadístico para el análisis de la adaptación y de las restricciones evolutivas a nivel genómico.
1 aSerra, François1 aArbiza, L.1 aDopazo, H.1 aDopazo, H.1 aNavarro, A. uhttps://www.clinbioinfosspa.es/content/gen%C3%B3mica-comparativa-y-selecci%C3%B3n-natural-aplicaciones-en-el-genoma-humano-cap%C3%ADtulo-1602205nas 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-002221nas a2200157 4500008004100000245005800041210005600099300000800155490000600163520157700169653010501746653007601851100001501927700001501942856010601957 2005 eng d00aGenome-scale evidence of the nematode-arthropod clade0 aGenomescale evidence of the nematodearthropod clade aR410 v63 aBACKGROUND: The issue of whether coelomates form a single clade, the Coelomata, or whether all animals that moult an exoskeleton (such as the coelomate arthropods and the pseudocoelomate nematodes) form a distinct clade, the Ecdysozoa, is the most puzzling issue in animal systematics and a major open-ended subject in evolutionary biology. Previous single-gene and genome-scale analyses designed to resolve the issue have produced contradictory results. Here we present the first genome-scale phylogenetic evidence that strongly supports the Ecdysozoa hypothesis. RESULTS: Through the most extensive phylogenetic analysis carried out to date, the complete genomes of 11 eukaryotic species have been analyzed in order to find homologous sequences derived from 18 human chromosomes. Phylogenetic analysis of datasets showing an increased adjustment to equal evolutionary rates between nematode and arthropod sequences produced a gradual change from support for Coelomata to support for Ecdysozoa. Transition between topologies occurred when fast-evolving sequences of Caenorhabditis elegans were removed. When chordate, nematode and arthropod sequences were constrained to fit equal evolutionary rates, the Ecdysozoa topology was statistically accepted whereas Coelomata was rejected. CONCLUSIONS: The reliability of a monophyletic group clustering arthropods and nematodes was unequivocally accepted in datasets where traces of the long-branch attraction effect were removed. This is the first phylogenomic evidence to strongly support the ’moulting clade’ hypothesis.10aAnimals Arthropods/*classification/genetics Caenorhabditis elegans/classification/genetics Evolution10aMolecular *Genome Genomics Nematoda/*classification/genetics *Phylogeny1 aDopazo, H.1 aDopazo, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1589286902188nas 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=1598054802990nas a2200325 4500008004100000245013600041210006900177300001100246490000700257520176100264653001602025653002602041653001002067653003402077653003402111653011302145653008802258100001702346700002102363700001602384700002502400700002702425700001502452700002102467700001402488700001502502700002502517700001602542856010602558 2004 eng d00aGene expression analysis of chromosomal regions with gain or loss of genetic material detected by comparative genomic hybridization0 aGene expression analysis of chromosomal regions with gain or los a353-650 v413 aComparative genomic hybridization (CGH) has been widely used to detect copy number alterations in cancer and to identify regions containing candidate tumor-responsible genes; however, gene expression changes have been described only in highly amplified regions (amplicons). To study the overall impact of slight copy number changes on gene expression, we analyzed 16 T-cell lymphomas by using CGH and a custom-designed cDNA microarray containing 7,657 genes and expressed sequence tags related to tumorigenesis. We evaluated mean gene expression and variability within CGH-altered regions and explored the relationship between the effects of the gene and its position within these regions. Minimally overlapping CGH candidate areas (6q25, 13q21-q22, and 19q13.1) revealed a weak relationship between altered genomic content and gene expression. However, some candidate genes showed modified expression within these regions in the majority of tumors; these candidate genes were evaluated and confirmed in another independent series of 23 T-cell lymphomas by use of the same cDNA microarray and by FISH on a tissue microarray. When all the CGH regions detected for each tumor were considered, we found a significant increase or decrease in the mean expression of the genes contained in gained or lost regions, respectively. In addition, we found that the expression of a gene was dependent not only on its position within an altered region but also on its own mechanism of regulation: genes in the same altered region responded very differently to the gain or loss of genetic material. Supplementary material for this article can be found on the Genes, Chromosomes, and Cancer website at http://www.interscience.wiley.com/jpages/1045-2257/suppmat/index.html.10aChromosomes10aFluorescence Lymphoma10aHuman10aPair 13/*genetics Chromosomes10aPair 19/*genetics Chromosomes10aPair 6/*genetics Expressed Sequence Tags *Gene Dosage Gene Expression Profiling Humans In Situ Hybridization10aT-Cell/*genetics Nucleic Acid Hybridization Oligonucleotide Array Sequence Analysis1 aMelendez, B.1 aDiaz-Uriarte, R.1 aCuadros, M.1 aMartinez-Ramirez, A.1 aFernandez-Piqueras, J.1 aDopazo, A.1 aCigudosa, J., C.1 aRivas, C.1 aDopazo, J.1 aMartinez-Delgado, B.1 aBenitez, J. uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1538226100556nas a2200133 4500008004100000245011100041210006900152300001000221100001200231700001500243700001900258700001500277856013000292 2004 eng d00aGene expression Correlation and Gene Ontology-Based Similarity: An Assessment of Quantitative Relationship0 aGene expression Correlation and Gene OntologyBased Similarity An a25-311 aWang, H1 aAzuaje, F.1 aBodenreider, O1 aDopazo, J. uhttps://www.clinbioinfosspa.es/content/gene-expression-correlation-and-gene-ontology-based-similarity-assessment-quantitative01226nas 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=12824345