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ASpli: Integrative analysis of splicing landscapes through RNA-Seq assays (Mar, btab141, 2021)

bioinformatics 2021年第12期37卷 1783-1783页

作者： Mancini, Estefania Rabinovich, Andres Iserte, Javier Yanovsky, Marcelo Chernomoretz, Ariel Gene Regulation Stem Cells and Cancer Program Centre for Genomic Regulation (CRG) The Barcelona Institute of Science and Technology Barcelona Spain Integrative Systems Biology Fundacion Instituto Leloir Buenos Aires Argentina Instituto de Investigaciones Bioquímicas de Buenos Aires Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Buenos Aires Argentina DStructural Bioinformatics Fundacion Instituto Leloir Buenos Aires Argentina Comparative Genomics of Plant Development Fundacion Instituto Leloir Buenos Aires Argentina

Motivation Genome-wide analysis of alternative splicing has been a very active field of research since the early days of next generation sequencing technologies. Since then, ever-growing data availability and the development of increasingly sophisticated analysis methods have uncovered the complexity of the general splicing repertoire. A large number of splicing analysis methodologies exist, each of them presenting its own strengths and weaknesses. For instance, methods exclusively relying on junction information do not take advantage of the large majority of reads produced in an RNA-seq assay, isoform reconstruction methods might not detect novel intron retention events, some solutions can only handle canonical splicing events, and many existing methods can only perform pairwise *** In this contribution, we present ASpli, a computational suite implemented in R statistical language, that allows the identification of changes in both, annotated and novel alternative-splicing events and can deal with simple, multi-factor or paired experimental designs. Our integrative computational workflow, that considers the same GLM model applied to different sets of reads and junctions, allows computation of complementary splicing signals. Analyzing simulated and real data, we found that the consolidation of these signals resulted in a robust proxy of the occurrence of splicing alterations. While the analysis of junctions allowed us to uncover annotated as well as non-annotated events, read coverage signals notably increased recall capabilities at a very competitive performance when compared against other state-of-the-art splicing analysis *** and implementation ASpli is freely available from the Bioconductor project site https://***/doi:10.18129/*** information Supplementary data are available at bioinformatics online.

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