Complementarity of assembly-first and mapping-first approaches for alternative splicing annotation and differential analysis from RNAseq data. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Benoit-Pilven, Clara; Marchet, Camille; Chautard, Emilie; Lima, Leandro; Lambert, Marie-Pierre; Sacomoto, Gustavo; Rey, Amandine; Cologne, Audric; Terrone, Sophie; Dulaurier, Louis; Claude, Jean-Baptiste; Bourgeois, Cyril; Auboeuf, Didier; Lacroix, Vincent
• Is Part Of: Scientific reports. Volume 8:Issue 1(2018)
• Page Start: 1
• Page End: 13
• Publication Date: 2018-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-018-21770-7
• Languages: English
• ISSNs: 2045-2322

Abstract

Abstract Genome-wide analyses estimate that more than 90% of multi exonic human genes produce at least two transcripts through alternative splicing (AS). Various bioinformatics methods are available to analyze AS from RNAseq data. Most methods start by mapping the reads to an annotated reference genome, but some start by ade novo assembly of the reads. In this paper, we present a systematic comparison of a mapping-first approach (Fa RLine ) and an assembly-first approach (Kis Splice ). We applied these methods to two independent RNAseq datasets and found that the predictions of the two pipelines overlapped (70% of exon skipping events were common), but with noticeable differences. The assembly-first approach allowed to find more novel variants, including novel unannotated exons and splice sites. It also predicted AS in recently duplicated genes. The mapping-first approach allowed to find more lowly expressed splicing variants, and splice variants overlapping repeats. This work demonstrates that annotating AS with a single approach leads to missing out a large number of candidates, many of which are differentially regulated across conditions and can be validated experimentally. We therefore advocate for the combined use of both mapping-first and assembly-first approaches for the annotation and differential analysis of AS from RNAseq datasets.