Journal
BIOINFORMATICS
Volume 36, Issue 7, Pages 2134-2141Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btz915
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Funding
- European Union [CardioNeT-ITN-289600, CardioNext-608027]
- Spanish Ministry of Economy and Competitiveness [SAF2015-65722-R, SAF2012-31451]
- Spanish Ministry of Science, Innovation and Universities [RTI2018-102084-B-I00]
- Instituto de salud Carlos III (ISCIII) [CPII14/00027, RD012/0042/0066]
- Madrid Regional Government [2010-BMD-2321]
- Plan Estatal de IthornDthornI 2013-2016 -European Regional Development Fund (ERDF) 'A way of making Europe', Spain
- Instituto de Salud Carlos III (ISCIII)
- Ministerio de Ciencia, Innovacion y Universidades (MCNU)
- Pro CNIC Foundation
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Motivation: Alternative splicing (AS) is an important mechanism in the generation of transcript diversity across mammals. AS patterns are dynamically regulated during development and in response to environmental changes. Defects or perturbations in its regulation may lead to cancer or neurological disorders, among other pathological conditions. The regulatory mechanisms controlling AS in a given biological context are typically inferred using a two-step framework: differential AS analysis followed by enrichment methods. These strategies require setting rather arbitrary thresholds and are prone to error propagation along the analysis. Results: To overcome these limitations, we propose dSreg, a Bayesian model that integrates RNA-seq with data from regulatory features, e.g. binding sites of RNA-binding proteins. dSreg identifies the key underlying regulators controlling AS changes and quantifies their activity while simultaneously estimating the changes in exon inclusion rates. dSreg increased both the sensitivity and the specificity of the identified AS changes in simulated data, even at low read coverage. dSreg also showed improved performance when analyzing a collection of knock-down RNA-binding proteins' experiments from ENCODE, as opposed to traditional enrichment methods, such as over-representation analysis and gene set enrichment analysis. dSreg opens the possibility to integrate a large amount of readily available RNA-seq datasets at low coverage for AS analysis and allows more cost-effective RNA-seq experiments.
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