Annotation and differential analysis of alternative splicing using de novo assembly of RNAseq data
Résumé
Genome-wide analyses reveal 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 a
de novo
assembly
of the reads. In this paper, we present a systematic comparison of
a mapping-first approach (
FaRLine
) and an assembly-first approach
(
KisSplice
). These two approaches are event-based, as they focus
on the regions of the transcripts that vary in their exon content. We
applied these methods to an RNAseq dataset from a neuroblastoma
SK-N-SH cell line (ENCODE) differentiated or not using retinoic acid.
We 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 families of paralog genes. The mapping-first approach allowed to
find more lowly expressed splicing variants, and was better in predicting exons overlapping repeated elements. This work demonstrates that
annotating AS with a single approach leads to missing a large number of candidates. We further show that these candidates cannot be
neglected, since many of them are differentially regulated across conditions, and can be validated experimentally. We therefore advocate for
the combine use of both mapping-first and assembly-first approaches
for the annotation and differential analysis of AS from RNAseq data.
Origine : Fichiers produits par l'(les) auteur(s)