SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions

By J. Trincado, J. Entizne, Gerald Hysenaj, Babita Singh, Miha Škalič, D. Elliott, Eduardo Eyras. Genome Biology, 2018

593 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/297631516484b728962ae1c714b34fc0c4d5d20d

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In Molecular Biology

1. Charlotte Soneson
2. Michael I. Love
3. Rob Patro
4. Mark D. Robinson
5. Lieven Clement
6. K. Van den Berge
7. Katharina M. Hembach
8. Simone Tiberi

Citations in the corpus

• RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Some of the main DS tools based on PSIs include: rMATS [176] , which uses a LRT, and SUPPA2 [177] , whose test is based on comparing the observed difference in PSIs across conditions to the empirical cumulative density function of the within­replicates differences of PSIs of splice junctions fro”