00616nas a2200157 4500008004100000245013100041210006900172260001600241300000900257490000600266100003100272700002800303700001800331700002000349856008900369 2021 eng d00aDeciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model0 aDeciphering Genomic Heterogeneity and the Internal Composition o cJan-11-2021 a28330 v91 aCarbonell-Caballero, José1 aLópez-Quílez, Antonio1 aConesa, David1 aDopazo, Joaquin uhttps://www.mdpi.com/2227-7390/9/21/2833https://www.mdpi.com/2227-7390/9/21/2833/pdf01818nas a2200265 4500008004100000022001400041245007700055210006900132260001500201300001400216490000700230520098400237653001501221653001101236653002301247653002401270653002001294653001801314100001901332700002201351700001801373700003101391700002001422856011001442 2019 eng d a1477-405400aA comparison of mechanistic signaling pathway activity analysis methods.0 acomparison of mechanistic signaling pathway activity analysis me c2019 09 27 a1655-16680 v203 a
Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.
10aAlgorithms10aHumans10aPostmortem Changes10aSignal Transduction10aSystems biology10aTranscriptome1 aAmadoz, Alicia1 aHidalgo, Marta, R1 aCubuk, Cankut1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/comparison-mechanistic-signaling-pathway-activity-analysis-methods02824nas a2200397 4500008004100000022001400041245011600055210006900171260000900240300000600249490000600255520157600261653002601837653002401863653001901887653002901906653001101935653001301946653003601959653002301995653001402018653001402032653001302046653001802059100001802077700002202095700001902117700001602136700002402152700002202176700002102198700002002219700003102239700002002270856013602290 2019 eng d a2056-718900aDifferential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.0 aDifferential metabolic activity and discovery of therapeutic tar c2019 a70 v53 aIn spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.
10aComputational Biology10aComputer Simulation10aDrug discovery10aGene Regulatory Networks10aHumans10aInternet10aMetabolic Networks and Pathways10aModels, Biological10aNeoplasms10aPhenotype10aSoftware10aTranscriptome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aRian, Kinza1 aSalavert, Francisco1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/differential-metabolic-activity-and-discovery-therapeutic-targets-using-summarized-metabolic02577nas a2200529 4500008004100000022001400041245008700055210006900142260001500211300000800226490000600234520103000240653001001270653001801280653001001298653001101308653002001319653001101339653002301350653002301373653001901396653002501415653001801440100002301458700002701481700002101508700002501529700001601554700001901570700001701589700002201606700002401628700003101652700002101683700002401704700001901728700002401747700001801771700001801789700002101807700002001828700002001848700002101868700002301889700002001912856011501932 2018 eng d a2041-172300aThe effects of death and post-mortem cold ischemia on human tissue transcriptomes.0 aeffects of death and postmortem cold ischemia on human tissue tr c2018 02 13 a4900 v93 aPost-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.
10aBlood10aCold Ischemia10aDeath10aFemale10agene expression10aHumans10aModels, Biological10aPostmortem Changes10aRNA, Messenger10aStochastic Processes10aTranscriptome1 aFerreira, Pedro, G1 aMuñoz-Aguirre, Manuel1 aReverter, Ferran1 aGodinho, Caio, P Sá1 aSousa, Abel1 aAmadoz, Alicia1 aSodaei, Reza1 aHidalgo, Marta, R1 aPervouchine, Dmitri1 aCarbonell-Caballero, José1 aNurtdinov, Ramil1 aBreschi, Alessandra1 aAmador, Raziel1 aOliveira, Patrícia1 aCubuk, Cankut1 aCurado, João1 aAguet, François1 aOliveira, Carla1 aDopazo, Joaquin1 aSammeth, Michael1 aArdlie, Kristin, G1 aGuigó, Roderic uhttps://www.clinbioinfosspa.es/content/effects-death-and-post-mortem-cold-ischemia-human-tissue-transcriptomes02950nas a2200445 4500008004100000022001400041245009500055210006900150260001500219300001400234490000700248520156700255653002101822653002101843653002401864653003001888653004301918653002901961653001101990653002602001653001502027653001302042653001402055653001402069653001402083653001402097653002702111653002702138653001802165653002202183100001802205700002202223700001902245700002202264700002102286700002002307700003102327700002002358856012602378 2018 eng d a1538-744500aGene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.0 aGene Expression Integration into Pathway Modules Reveals a PanCa c2018 11 01 a6059-60720 v783 aMetabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .
10aCell Line, Tumor10aCluster Analysis10aDisease Progression10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aGene Regulatory Networks10aHumans10aKaplan-Meier Estimate10aMetabolome10amutation10aNeoplasms10aOncogenes10aPhenotype10aPrognosis10aRNA, Small Interfering10aSequence Analysis, RNA10aTranscriptome10aTreatment Outcome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/gene-expression-integration-pathway-modules-reveals-pan-cancer-metabolic-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-citrus02239nas a2200277 4500008004100000022001400041245011400055210006900169260001500238300000700253490000700260520126600267653002601533653004301559653001101602653004201613653002401655653001801679653002401697100002201721700001901743700001801762700003101780700002001811856013001831 2018 eng d a1745-615000aModels of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.0 aModels of cell signaling uncover molecular mechanisms of highris c2018 08 22 a160 v133 aBACKGROUND: Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.
RESULTS: Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.
CONCLUSION: We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.
REVIEWERS: This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.
10aComputational Biology10aGene Expression Regulation, Neoplastic10aHumans10aJNK Mitogen-Activated Protein Kinases10aModels, Theoretical10aNeuroblastoma10aSignal Transduction1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aCubuk, Cankut1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/models-cell-signaling-uncover-molecular-mechanisms-high-risk-neuroblastoma-and-predict02954nas a2200541 4500008004100000022001400041245008900055210006900144260000900213300001300222490000700235520138200242653001001624653000901634653001801643653001101661653002901672653003201701653002601733653001101759653001401770653002901784653000901813653001601822653001301838653002201851653001801873653001401891653002701905100002101932700003101953700002001984700002402004700002402028700002602052700001602078700001902094700001902113700002502132700002002157700001902177700002002196700002002216700002402236700002002260700001902280856011302299 2018 eng d a1932-620300aThe modular network structure of the mutational landscape of Acute Myeloid Leukemia.0 amodular network structure of the mutational landscape of Acute M c2018 ae02029260 v133 aAcute myeloid leukemia (AML) is associated with the sequential accumulation of acquired genetic alterations. Although at diagnosis cytogenetic alterations are frequent in AML, roughly 50% of patients present an apparently normal karyotype (NK), leading to a highly heterogeneous prognosis. Due to this significant heterogeneity, it has been suggested that different molecular mechanisms may trigger the disease with diverse prognostic implications. We performed whole-exome sequencing (WES) of tumor-normal matched samples of de novo AML-NK patients lacking mutations in NPM1, CEBPA or FLT3-ITD to identify new gene mutations with potential prognostic and therapeutic relevance to patients with AML. Novel candidate-genes, together with others previously described, were targeted resequenced in an independent cohort of 100 de novo AML patients classified in the cytogenetic intermediate-risk (IR) category. A mean of 4.89 mutations per sample were detected in 73 genes, 35 of which were mutated in more than one patient. After a network enrichment analysis, we defined a single in silico model and established a set of seed-genes that may trigger leukemogenesis in patients with normal karyotype. The high heterogeneity of gene mutations observed in AML patients suggested that a specific alteration could not be as essential as the interaction of deregulated pathways.
10aAdult10aAged10aCytodiagnosis10aFemale10aGene Regulatory Networks10aGenetic Association Studies10aGenetic Heterogeneity10aHumans10aKaryotype10aLeukemia, Myeloid, Acute10aMale10aMiddle Aged10amutation10aNeoplasm Proteins10aNucleophosmin10aPrognosis10awhole exome sequencing1 aIbáñez, Mariam1 aCarbonell-Caballero, José1 aSuch, Esperanza1 aGarcía-Alonso, Luz1 aLiquori, Alessandro1 aLópez-Pavía, María1 aLLop, Marta1 aAlonso, Carmen1 aBarragán, Eva1 aGómez-Seguí, Inés1 aNeef, Alexander1 aHervás, David1 aMontesinos, Pau1 aSanz, Guillermo1 aSanz, Miguel, Angel1 aDopazo, Joaquin1 aCervera, José uhttps://www.clinbioinfosspa.es/content/modular-network-structure-mutational-landscape-acute-myeloid-leukemia02048nas a2200313 4500008004100000022001400041245012900055210006900184260001600253300001400269490000600283520099600289653002601285653002001311653002901331653001101360653001301371653001401384653002301398653002701421653002401448100002201472700001801494700001901512700002401531700003101555700002001586856012801606 2017 eng d a1949-255300aHigh throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.0 aHigh throughput estimation of functional cell activities reveals c2017 Jan 17 a5160-51780 v83 aUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.
10aComputational Biology10agene expression10aGene Regulatory Networks10aHumans10amutation10aNeoplasms10aPrecision Medicine10aSequence Analysis, RNA10aSignal Transduction1 aHidalgo, Marta, R1 aCubuk, Cankut1 aAmadoz, Alicia1 aSalavert, Francisco1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/high-throughput-estimation-functional-cell-activities-reveals-disease-mechanisms-and02685nas a2200349 4500008004100000022001400041245011500055210006900170260001600239300001400255490000700269520162000276653001201896653002201908653001101930653001301941653001301954653001101967653002401978653002002002653003102022653001302053100003102066700001902097700002002116700002202136700001802158700001802176700002802194700002002222856009302242 2017 eng d a1367-481100aReference genome assessment from a population scale perspective: an accurate profile of variability and noise.0 aReference genome assessment from a population scale perspective c2017 Nov 15 a3511-35170 v333 aMotivation: Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome.
Results: The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples.
Availability and implementation: This tool is freely available at http://gitlab.com/carbonell/ces.
Contact: jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es.
Supplementary information: Supplementary data are available at Bioinformatics online.
10aAnimals10aGenetic Variation10aGenome10aGenomics10aGenotype10aHumans10aModels, Statistical10aQuality Control10aReproducibility of Results10aSoftware1 aCarbonell-Caballero, José1 aAmadoz, Alicia1 aAlonso, Roberto1 aHidalgo, Marta, R1 aCubuk, Cankut1 aConesa, David1 aLópez-Quílez, Antonio1 aDopazo, Joaquin uhttps://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx48201971nas a2200301 4500008004100000022001400041245010200055210006900157260001500226520100600241653002101247653002201268653002601290653001901316653002101335653002601356653001501382653002401397100002401421700002101445700001901466700001801485700002001503700001901523700003101542700002101573856007501594 2016 eng d a1362-496200aActionable pathways: interactive discovery of therapeutic targets using signaling pathway models.0 aActionable pathways interactive discovery of therapeutic targets c2016 May 23 aThe discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.10aactionable genes10aDisease mechanism10adrug action mechanism10aDrug discovery10apathway analysis10apersonalized medicine10asignalling10atherapeutic targets1 aSalavert, Francisco1 aHidago, Marta, R1 aAmadoz, Alicia1 aCubuk, Cankut1 aMedina, Ignacio1 aCrespo, Daniel1 aCarbonell-Caballero, José1 aDopazo, Joaquín uhttp://nar.oxfordjournals.org/content/early/2016/05/02/nar.gkw369.full02864nas a2200433 4500008004100000022001400041245013500055210006900190260000900259300001300268490000700281520145800288653001001746653002901756653001801785653001101803653001901814653003501833653001301868653001801881653003601899653003101935100002101966700003101987700002402018700002002042700002902062700001902091700001902110700002602129700001602155700001902171700002502190700002002215700002002235700002002255700001902275856013602294 2016 eng d a1932-620300aThe Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations.0 aMutational Landscape of Acute Promyelocytic Leukemia Reveals an c2016 ae01483460 v113 aPreliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations.
10aExome10aGene Regulatory Networks10aGenome, Human10aHumans10aINDEL Mutation10aLeukemia, Promyelocytic, Acute10amutation10aMutation Rate10aPolymorphism, Single Nucleotide10aReproducibility of Results1 aIbáñez, Mariam1 aCarbonell-Caballero, José1 aGarcía-Alonso, Luz1 aSuch, Esperanza1 aJiménez-Almazán, Jorge1 aVidal, Enrique1 aBarragán, Eva1 aLópez-Pavía, María1 aLLop, Marta1 aMartín, Iván1 aGómez-Seguí, Inés1 aMontesinos, Pau1 aSanz, Miguel, A1 aDopazo, Joaquin1 aCervera, José uhttps://www.clinbioinfosspa.es/content/mutational-landscape-acute-promyelocytic-leukemia-reveals-interacting-network-co-occurrences00573nas a2200145 4500008004100000245005300041210004800094260001600142490000600158100002200164700001700186700003100203700002000234856017300254 2016 eng d00aThe pan-cancer pathological regulatory landscape0 apancancer pathological regulatory landscape cJan-12-20160 v61 aFalco, Matias, M.1 aBleda, Marta1 aCarbonell-Caballero, José1 aDopazo, Joaquin uhttp://www.nature.com/articles/srep39709http://www.nature.com/articles/srep39709.pdfhttp://www.nature.com/articles/srep39709.pdfhttp://www.nature.com/articles/srep3970901876nas a2200181 4500008004100000022001400041245005400055210004800109260001600157300001000173490000600183520137000189100002101559700001701580700003101597700002101628856004501649 2016 eng d a2045-232200aThe pan-cancer pathological regulatory landscape.0 apancancer pathological regulatory landscape c2016 Dec 21 a397090 v63 aDysregulation of the normal gene expression program is the cause of a broad range of diseases, including cancer. Detecting the specific perturbed regulators that have an effect on the generation and the development of the disease is crucial for understanding the disease mechanism and for taking decisions on efficient preventive and curative therapies. Moreover, detecting such perturbations at the patient level is even more important from the perspective of personalized medicine. We applied the Transcription Factor Target Enrichment Analysis, a method that detects the activity of transcription factors based on the quantification of the collective transcriptional activation of their targets, to a large collection of 5607 cancer samples covering eleven cancer types. We produced for the first time a comprehensive catalogue of altered transcription factor activities in cancer, a considerable number of them significantly associated to patient’s survival. Moreover, we described several interesting TFs whose activity do not change substantially in the cancer with respect to the normal tissue but ultimately play an important role in patient prognostic determination, which suggest they might be promising therapeutic targets. An additional advantage of this method is that it allows obtaining personalized TF activity estimations for individual patients.1 aFalco, Matias, M1 aBleda, Marta1 aCarbonell-Caballero, José1 aDopazo, Joaquín uhttp://www.nature.com/articles/srep3970902445nas a2200469 4500008004100000022001400041245008300055210006900138260001600207300001400223490000700237520108700244653001501331653002101346653002201367653001601389653002101405653000801426653001201434653002001446653002001466100002001486700002401506700002901530700003101559700001701590700002401607700002301631700002201654700002401676700002101700700001801721700002201739700001901761700003601780700002301816700002301839700002001862700002001882700002001902856005301922 2015 eng d a1362-496200aBabelomics 5.0: functional interpretation for new generations of genomic data.0 aBabelomics 50 functional interpretation for new generations of g c2015 Apr 20 aW117-W1210 v433 aBabelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.10ababelomics10adata integration10agene set analysis10ainteractome10anetwork analysis10aNGS10aRNA-seq10aSystems biology10atranscriptomics1 aAlonso, Roberto1 aSalavert, Francisco1 aGarcia-Garcia, Francisco1 aCarbonell-Caballero, José1 aBleda, Marta1 aGarcía-Alonso, Luz1 aSanchis-Juan, Alba1 aPerez-Gil, Daniel1 aMarin-Garcia, Pablo1 aSánchez, Rubén1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aHernansaiz-Ballesteros, Rosa, D1 aAlemán, Alejandro1 aTárraga, Joaquín1 aMontaner, David1 aMedina, Ignacio1 aDopazo, Joaquin uhttp://nar.oxfordjournals.org/content/43/W1/W11703376nas a2200793 4500008004100000022001400041245011500055210006900170260001600239520112300255653001101378653000801389653002001397100001901417700002601436700001201462700001801474700002201492700002701514700002201541700002201563700001901585700002901604700002301633700002001656700002001676700002301696700002501719700002201744700002201766700002101788700004001809700001201849700001701861700001201878700001601890700001901906700001801925700002101943700001601964700002001980700002402000700002002024700001802044700001302062700001702075700001802092700002102110700002402131700002002155700002102175700001702196700002102213700002502234700002102259700001902280700001902299700002102318700001602339700001402355700001702369700001502386700001302401700003102414700002102445700002102466700002002487856007502507 2015 eng d a1548-710500aCombining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.0 aCombining tumor genome simulation with crowdsourcing to benchmar c2015 May 183 aThe detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.10acancer10aNGS10avariant calling1 aEwing, Adam, D1 aHoulahan, Kathleen, E1 aHu, Yin1 aEllrott, Kyle1 aCaloian, Cristian1 aYamaguchi, Takafumi, N1 aBare, Christopher1 aP’ng, Christine1 aWaggott, Daryl1 aSabelnykova, Veronica, Y1 aKellen, Michael, R1 aNorman, Thea, C1 aHaussler, David1 aFriend, Stephen, H1 aStolovitzky, Gustavo1 aMargolin, Adam, A1 aStuart, Joshua, M1 aBoutros, Paul, C1 aparticipants, ICGC-TCGA, DREAM Soma1 aXi, Liu1 aDewal, Ninad1 aFan, Yu1 aWang, Wenyi1 aWheeler, David1 aWilm, Andreas1 aTing, Grace, Hui1 aLi, Chenhao1 aBertrand, Denis1 aNagarajan, Niranjan1 aChen, Qing-Rong1 aHsu, Chih-Hao1 aHu, Ying1 aYan, Chunhua1 aKibbe, Warren1 aMeerzaman, Daoud1 aCibulskis, Kristian1 aRosenberg, Mara1 aBergelson, Louis1 aKiezun, Adam1 aRadenbaugh, Amie1 aSertier, Anne-Sophie1 aFerrari, Anthony1 aTonton, Laurie1 aBhutani, Kunal1 aHansen, Nancy, F1 aWang, Difei1 aSong, Lei1 aLai, Zhongwu1 aLiao, Yang1 aShi, Wei1 aCarbonell-Caballero, José1 aDopazo, Joaquín1 aLau, Cheryl, C K1 aGuinney, Justin uhttp://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3407.html02086nas a2200253 4500008004100000022001400041245014200055210006900197260001600266300001400282490000700296520127300303653001601576653001101592653001401603653000801617100003101625700002001656700002201676700001801698700001801716700002001734856007801754 2015 eng d a1537-171900aA phylogenetic analysis of 34 chloroplast genomes elucidates the relationships between wild and domestic species within the genus Citrus.0 aphylogenetic analysis of 34 chloroplast genomes elucidates the r c2015 Apr 14 a2015-20350 v323 aCitrus genus includes some of the most important cultivated fruit trees worldwide. Despite being extensively studied because of its commercial relevance, the origin of cultivated citrus species and the history of its domestication still remain an open question. Here we present a phylogenetic analysis of the chloroplast genomes of 34 citrus genotypes which constitutes the most comprehensive and detailed study to date on the evolution and variability of the genus Citrus. A statistical model was used to estimate divergence times between the major citrus groups. Additionally, a complete map of the variability across the genome of different citrus species was produced, including single nucleotide variants, heteroplasmic positions, indels and large structural variants. The distribution of all these variants provided further independent support to the phylogeny obtained. An unexpected finding was the high level of heteroplasmy found in several of the analysed genomes. The use of the complete chloroplast DNA not only paves the way for a better understanding of the phylogenetic relationships within the Citrus genus, but also provides original insights into other elusive evolutionary processes such as chloroplast inheritance, heteroplasmy and gene selection.10achloroplast10acitrus10aPhylogeny10aWGS1 aCarbonell-Caballero, José1 aAlonso, Roberto1 aIbañez, Victoria1 aTerol, Javier1 aTalon, Manuel1 aDopazo, Joaquin uhttp://mbe.oxfordjournals.org/content/early/2015/04/27/molbev.msv082.full02937nas a2200397 4500008004100000022001400041245010000055210006900155260001600224300000800240490000700248520172500255653001201980653001001992653001702002653002202019653002502041653001802066653001302084653001102097653002002108653001302128653001402141653002502155653002902180653002702209653001102236100002402247700002902271700003102300700002202331700002702353700002502380700002002405856011402425 2014 eng d a1744-429200aThe role of the interactome in the maintenance of deleterious variability in human populations.0 arole of the interactome in the maintenance of deleterious variab c2014 Sep 26 a7520 v103 aRecent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins.
10aAlleles10aExome10aGene Library10aGenetic Variation10aGenetics, Population10aGenome, Human10aGenomics10aHumans10aModels, Genetic10amutation10aPhenotype10aProtein Conformation10aProtein Interaction Maps10aSequence Analysis, DNA10aWhites1 aGarcía-Alonso, Luz1 aJiménez-Almazán, Jorge1 aCarbonell-Caballero, José1 aVela-Boza, Alicia1 aSantoyo-López, Javier1 aAntiňolo, Guillermo1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/role-interactome-maintenance-deleterious-variability-human-populations02925nas a2200409 4500008004100000022001400041245011400055210006900169260001300238300001000251490000700261520165700268653001201925653001501937653001101952653003901963653001802002653001902020653001202039653003102051653001302082653000902095653001402104653001602118653002702134653002402161653001802185100002102203700002502224700003102249700001602280700002002296700001802316700002502334700003202359856012402391 2014 eng d a1096-093700aSequencing and functional analysis of the genome of a nematode egg-parasitic fungus, Pochonia chlamydosporia.0 aSequencing and functional analysis of the genome of a nematode e c2014 Apr a69-800 v653 aPochonia chlamydosporia is a worldwide-distributed soil fungus with a great capacity to infect and destroy the eggs and kill females of plant-parasitic nematodes. Additionally, it has the ability to colonize endophytically roots of economically-important crop plants, thereby promoting their growth and eliciting plant defenses. This multitrophic behavior makes P. chlamydosporia a potentially useful tool for sustainable agriculture approaches. We sequenced and assembled ∼41 Mb of P. chlamydosporia genomic DNA and predicted 12,122 gene models, of which many were homologous to genes of fungal pathogens of invertebrates and fungal plant pathogens. Predicted genes (65%) were functionally annotated according to Gene Ontology, and 16% of them found to share homology with genes in the Pathogen Host Interactions (PHI) database. The genome of this fungus is highly enriched in genes encoding hydrolytic enzymes, such as proteases, glycoside hydrolases and carbohydrate esterases. We used RNA-Seq technology in order to identify the genes expressed during endophytic behavior of P. chlamydosporia when colonizing barley roots. Functional annotation of these genes showed that hydrolytic enzymes and transporters are expressed during endophytism. This structural and functional analysis of the P. chlamydosporia genome provides a starting point for understanding the molecular mechanisms involved in the multitrophic lifestyle of this fungus. The genomic information provided here should also prove useful for enhancing the capabilities of this fungus as a biocontrol agent of plant-parasitic nematodes and as a plant growth-promoting organism.
10aAnimals10aAscomycota10aFemale10aGene Expression Regulation, Fungal10aGene ontology10aGenome, Fungal10aHordeum10aHost-Pathogen Interactions10aNematoda10aOvum10aPhylogeny10aPlant Roots10aSequence Analysis, DNA10aSignal Transduction10aTranscriptome1 aLarriba, Eduardo1 aJaime, María, D L A1 aCarbonell-Caballero, José1 aConesa, Ana1 aDopazo, Joaquin1 aNislow, Corey1 aMartín-Nieto, José1 aLopez-Llorca, Luis, Vicente uhttps://www.clinbioinfosspa.es/content/sequencing-and-functional-analysis-genome-nematode-egg-parasitic-fungus-pochonia