Systematic evaluation of differential splicing tools for RNA-seq studies

Mehmood, Arfa (author): Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland; Department of Physiology, University of Turku, Turku, Finland

Laiho, Asta (author): Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

Venäläinen, Mikko S. (author): Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

McGlinchey, Aidan J., 1984- (author): Örebro universitet,Institutionen för medicinska vetenskaper,Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

Wang, Ning (author): Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

Elo, Laura L. (author): Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

(creator_code:org_t)

2019-12-05
2020
English.
In: Briefings in Bioinformatics. - : Oxford University Press. - 1467-5463 .- 1477-4054. ; 21:6, s. 2052-2065

Related links:: https://doi.org/10.1...; show more...; https://academic.oup...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Abstract Subject headings

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.

By the author/editor: Mehmood, Arfa; Laiho, Asta; Venäläinen, Mikk ...; McGlinchey, Aida ...; Wang, Ning; Elo, Laura L.

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Industrial Biote ...; and Medical Biotechn ...

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