Systematic evaluation of differential splicing tools for RNA-seq studies

• Published in: Briefings in bioinformatics Vol. 21; no. 6; pp. 2052 - 2065
• Main Authors: Mehmood, Arfa, Laiho, Asta, Venäläinen, Mikko S, McGlinchey, Aidan J, Wang, Ning, Elo, Laura L
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.12.2020; Oxford Publishing Limited (England)
• Subjects: Biological activity; Computer applications; Data analysis; differential splicing; Evaluation; event-based methods; exon-based methods; Exons; Genes; isoform-based methods; Post-transcription; Protein Isoforms; Reproducibility of Results; Review; Ribonucleic acid; RNA; RNA Splicing; RNA-Seq; Sequence Analysis, RNA - methods; Splicing; splicing events; isoform-based methods; RNA-seq; differential splicing; splicing events; event-based methods; exon-based methods
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbz126

Abstract Differential splicing (DS) is a post-transcriptional biological process with critical, wide-ranging effects on a plethora of cellular activities and disease processes. To date, a number of computational approaches have been developed to identify and quantify differentially spliced genes from RNA-seq data, but a comprehensive intercomparison and appraisal of these approaches is currently lacking. In this study, we systematically evaluated 10 DS analysis tools for consistency and reproducibility, precision, recall and false discovery rate, agreement upon reported differentially spliced genes and functional enrichment. The tools were selected to represent the three different methodological categories: exon-based (DEXSeq, edgeR, JunctionSeq, limma), isoform-based (cuffdiff2, DiffSplice) and event-based methods (dSpliceType, MAJIQ, rMATS, SUPPA). Overall, all the exon-based methods and two event-based methods (MAJIQ and rMATS) scored well on the selected measures. Of the 10 tools tested, the exon-based methods performed generally better than the isoform-based and event-based methods. However, overall, the different data analysis tools performed strikingly differently across different data sets or numbers of samples.