@article {439, title = {Integrated gene set analysis for microRNA studies.}, journal = {Bioinformatics}, volume = {32}, year = {2016}, month = {2016 Sep 15}, pages = {2809-16}, abstract = {

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.

}, keywords = {Computational Biology, Gene Expression Profiling, Gene ontology, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Humans, MicroRNAs, Neoplasms, Reproducibility of Results}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw334}, author = {Garcia-Garcia, Francisco and Panadero, Joaquin and Dopazo, Joaquin and Montaner, David} } @article {517, title = {Inferring the regulatory network behind a gene expression experiment.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Jul}, pages = {W168-72}, abstract = {

Transcription factors (TFs) and miRNAs are the most important dynamic regulators in the control of gene expression in multicellular organisms. These regulatory elements play crucial roles in development, cell cycling and cell signaling, and they have also been associated with many diseases. The Regulatory Network Analysis Tool (RENATO) web server makes the exploration of regulatory networks easy, enabling a better understanding of functional modularity and network integrity under specific perturbations. RENATO is suitable for the analysis of the result of expression profiling experiments. The program analyses lists of genes and search for the regulators compatible with its activation or deactivation. Tests of single enrichment or gene set enrichment allow the selection of the subset of TFs or miRNAs significantly involved in the regulation of the query genes. RENATO also offers an interactive advanced graphical interface that allows exploring the regulatory network found.RENATO is available at: http://renato.bioinfo.cipf.es/.

}, keywords = {Binding Sites, Databases, Genetic, Fanconi Anemia, Gene Regulatory Networks, Internet, MicroRNAs, Software, Transcription Factors, Transcriptome}, issn = {1362-4962}, doi = {10.1093/nar/gks573}, author = {Bleda, Marta and Medina, Ignacio and Alonso, Roberto and De Maria, Alejandro and Salavert, Francisco and Dopazo, Joaquin} } @article {596, title = {Joint annotation of coding and non-coding single nucleotide polymorphisms and mutations in the SNPeffect and PupaSuite databases.}, journal = {Nucleic Acids Res}, volume = {36}, year = {2008}, month = {2008 Jan}, pages = {D825-9}, abstract = {

Single nucleotide polymorphisms (SNPs) are, together with copy number variation, the primary source of variation in the human genome. SNPs are associated with altered response to drug treatment, susceptibility to disease and other phenotypic variation. Furthermore, during genetic screens for disease-associated mutations in groups of patients and control individuals, the distinction between disease causing mutation and polymorphism is often unclear. Annotation of the functional and structural implications of single nucleotide changes thus provides valuable information to interpret and guide experiments. The SNPeffect and PupaSuite databases are now synchronized to deliver annotations for both non-coding and coding SNP, as well as annotations for the SwissProt set of human disease mutations. In addition, SNPeffect now contains predictions of Tango2: an improved aggregation detector, and Waltz: a novel predictor of amyloid-forming sequences, as well as improved predictors for regions that are recognized by the Hsp70 family of chaperones. The new PupaSuite version incorporates predictions for SNPs in silencers and miRNAs including their targets, as well as additional methods for predicting SNPs in TFBSs and splice sites. Also predictions for mouse and rat genomes have been added. In addition, a PupaSuite web service has been developed to enable data access, programmatically. The combined database holds annotations for 4,965,073 regulatory as well as 133,505 coding human SNPs and 14,935 disease mutations, and phenotypic descriptions of 43,797 human proteins and is accessible via http://snpeffect.vib.be and http://pupasuite.bioinfo.cipf.es/.

}, keywords = {Amino Acid Substitution, Animals, Databases, Genetic, Genetic Diseases, Inborn, HSP70 Heat-Shock Proteins, Humans, Internet, Mice, MicroRNAs, mutation, Polymorphism, Single Nucleotide, Proteins, Rats, RNA Splice Sites, Transcription Factors}, issn = {1362-4962}, doi = {10.1093/nar/gkm979}, author = {Reumers, Joke and Conde, Lucia and Medina, Ignacio and Maurer-Stroh, Sebastian and Van Durme, Joost and Dopazo, Joaquin and Rousseau, Frederic and Schymkowitz, Joost} }