02945nas a2200445 4500008004100000022001400041245009500055210006900150260001500219300001400234490000700248520156700255653002101822653002101843653002401864653003001888653004301918653002901961653001101990653002602001653001502027653001302042653001402055653001402069653001402083653001402097653002702111653002702138653001802165653002202183100001802205700002202223700001902245700002202264700002102286700002002307700003102327700002002358856012102378 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 uhttp://clinbioinfosspa.es/content/gene-expression-integration-pathway-modules-reveals-pan-cancer-metabolic-landscape02375nas a2200313 4500008004100000022001400041245011300055210006900168260001600237300000800253490000700261520133500268653001201603653002301615653003001638653004201668653001301710653002701723653001801750653002801768100002101796700002201817700002201839700002101861700002101882700002001903700001901923856011901942 2017 eng d a1471-210500aATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data.0 aATGC transcriptomics a webbased application to integrate explore c2017 Feb 22 a1210 v183 aBACKGROUND: In the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem is especially significant for small research groups working with non-model species.
RESULTS: We developed a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface that allows users to work with new generation sequencing (NGS) transcriptomic analysis results using an ontology-driven database. This new application simplifies data exploration, visualization, and integration for a better comprehension of the results.
CONCLUSIONS: ATGC transcriptomics provides access to non-expert computer users and small research groups to a scalable storage option and simple data integration, including database administration and management. The software is freely available under the terms of GNU public license at http://atgcinta.sourceforge.net .
10aAnimals10aDatabases, Genetic10aGene Expression Profiling10aHigh-Throughput Nucleotide Sequencing10aInternet10aSequence Analysis, RNA10aTranscriptome10aUser-Computer Interface1 aGonzalez, Sergio1 aClavijo, Bernardo1 aRivarola, Máximo1 aMoreno, Patricio1 aFernandez, Paula1 aDopazo, Joaquin1 aPaniego, Norma uhttp://clinbioinfosspa.es/content/atgc-transcriptomics-web-based-application-integrate-explore-and-analyze-de-novo02043nas a2200313 4500008004100000022001400041245012900055210006900184260001600253300001400269490000600283520099600289653002601285653002001311653002901331653001101360653001301371653001401384653002301398653002701421653002401448100002201472700001801494700001901512700002401531700003101555700002001586856012301606 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 uhttp://clinbioinfosspa.es/content/high-throughput-estimation-functional-cell-activities-reveals-disease-mechanisms-and01693nas a2200337 4500008004100000022001400041245007000055210006800125260001300193300001100206490000700217520067800224653001300902653004200915653001100957653003200968653002701000653001801027100001401045700001601059700001701075700001801092700001901110700001601129700002301145700002301168700002601191700002501217700001401242856009901256 2016 eng d a1756-166300aHighly sensitive and ultrafast read mapping for RNA-seq analysis.0 aHighly sensitive and ultrafast read mapping for RNAseq analysis c2016 Apr a93-1000 v233 aAs sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available.
10aGenomics10aHigh-Throughput Nucleotide Sequencing10aHumans10aSensitivity and Specificity10aSequence Analysis, RNA10aTranscriptome1 aMedina, I1 aTárraga, J1 aMartínez, H1 aBarrachina, S1 aCastillo, M, I1 aPaschall, J1 aSalavert-Torres, J1 aBlanquer-Espert, I1 aHernández-García, V1 aQuintana-Ortí, E, S1 aDopazo, J uhttp://clinbioinfosspa.es/content/highly-sensitive-and-ultrafast-read-mapping-rna-seq-analysis