02430nas a2200289 4500008004100000022001400041245005500055210005400110260001600164300001200180490000700192520155100199653002601750653003001776653001801806653002901824653004201853653001101895653001401906653001401920653003101934100002901965700002201994700002002016700002002036856008402056 2016 eng d a1367-481100aIntegrated gene set analysis for microRNA studies.0 aIntegrated gene set analysis for microRNA studies c2016 Sep 15 a2809-160 v323 a

MOTIVATION: Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes.

RESULTS: We analyze high-throughput sequencing data from 20 different cancer types and provide exhaustive reports of gene and Gene Ontology-term deregulation by miRNA action.

AVAILABILITY AND IMPLEMENTATION: The proposed methodology was implemented in the Bioconductor library mdgsa http://bioconductor.org/packages/mdgsa For the purpose of reproducibility all of the scripts are available at https://github.com/dmontaner-papers/gsa4mirna

CONTACT: : david.montaner@gmail.com

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

10aComputational Biology10aGene Expression Profiling10aGene ontology10aGene Regulatory Networks10aHigh-Throughput Nucleotide Sequencing10aHumans10aMicroRNAs10aNeoplasms10aReproducibility of Results1 aGarcia-Garcia, Francisco1 aPanadero, Joaquin1 aDopazo, Joaquin1 aMontaner, David uhttp://clinbioinfosspa.es/content/integrated-gene-set-analysis-microrna-studies03382nas a2200325 4500008004100000022001400041245007200055210006900127260001600196300000800212490000700220520234600227653001502573653002102588653004402609653002602653653002802679653001102707653003002718653001302748653001102761653004402772653003002816653003102846100002002877700001902897700002502916700002002941856009502961 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 a

BACKGROUND: 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 uhttp://clinbioinfosspa.es/content/gene-set-internal-coherence-context-functional-profiling