TAILIEUCHUNG - SUPPA2: Fast, accurate, and uncertaintyaware differential splicing analysis across multiple conditions

Despite the many approaches to study differential splicing from RNA-seq, many challenges remain unsolved, including computing capacity and sequencing depth requirements. Here we present SUPPA2, a new method that addresses these challenges, and enables streamlined analysis across multiple conditions taking into account biological variability. | Trincado et al. Genome Biology 2018 19 40 https s13059-018-1417-1 METHOD Open Access SUPPA2 fast accurate and uncertainty- aware differential splicing analysis across multiple conditions Juan L. Trincado1 Juan C. Entizne2 Gerald Hysenaj3 Babita Singh1 Miha Skalic1 David J. Elliott3 and Eduardo Eyras1 4 Abstract Despite the many approaches to study differential splicing from RNA-seq many challenges remain unsolved including computing capacity and sequencing depth requirements. Here we present SUPPA2 a new method that addresses these challenges and enables streamlined analysis across multiple conditions taking into account biological variability. Using experimental and simulated data we show that SUPPA2 achieves higher accuracy compared to other methods especially at low sequencing depth and short read length. We use SUPPA2 to identify novel Transformer2-regulated exons novel microexons induced during differentiation of bipolar neurons and novel intron retention events during erythroblast differentiation. Keywords Differential splicing Alternative splicing RNA-seq Uncertainty Biological variability Differentiation Background include the limitations in processing time for current Alternative splicing is related to a change in the relative methods the computational and storage capacity required abundance of transcript isoforms produced from the same as well as the constraints in the number of sequencing gene 1 . Multiple approaches have been proposed to study reads needed to achieve high enough accuracy. differential splicing from RNA sequencing RNA-seq data An additional challenge for RNA-seq analysis is the lack 2 3 . These methods generally involve the analysis of of robust methods to account for biological variability either transcript isoforms 4 7 clusters of splice junctions between replicates or to perform meaningful analyses of 8 9 alternative splicing events 10 11 or exonic regions differential splicing across multiple conditions. Although 12

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