ASpli: Integrative analysis of splicing landscapes through RNA-Seq assays

Motivation: Genome-wide analysis of alternative splicing has been a very active field of research since the early days of next generation sequencing technologies. Since then, ever-growing data availability and the development of increasingly sophisticated analysis methods have uncovered the complexi...

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Bibliographic Details
Main Authors: Chernomoretz, A. (Author), Iserte, J. (Author), Mancini, E. (Author), Rabinovich, A. (Author), Yanovsky, M. (Author)
Format: Article
Language:English
Published: Oxford University Press 2021
Online Access:View Fulltext in Publisher
LEADER 02381nam a2200181Ia 4500
001 10.1093-bioinformatics-btab141
008 220427s2021 CNT 000 0 und d
020 |a 13674803 (ISSN) 
245 1 0 |a ASpli: Integrative analysis of splicing landscapes through RNA-Seq assays 
260 0 |b Oxford University Press  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1093/bioinformatics/btab141 
520 3 |a Motivation: Genome-wide analysis of alternative splicing has been a very active field of research since the early days of next generation sequencing technologies. Since then, ever-growing data availability and the development of increasingly sophisticated analysis methods have uncovered the complexity of the general splicing repertoire. A large number of splicing analysis methodologies exist, each of them presenting its own strengths and weaknesses. For instance, methods exclusively relying on junction information do not take advantage of the large majority of reads produced in an RNA-seq assay, isoform reconstruction methods might not detect novel intron retention events, some solutions can only handle canonical splicing events, and many existing methods can only perform pairwise comparisons. Results: In this contribution, we present ASpli, a computational suite implemented in R statistical language, that allows the identification of changes in both, annotated and novel alternative-splicing events and can deal with simple, multi-factor or paired experimental designs. Our integrative computational workflow, that considers the same GLM model applied to different sets of reads and junctions, allows computation of complementary splicing signals. Analyzing simulated and real data, we found that the consolidation of these signals resulted in a robust proxy of the occurrence of splicing alterations. While the analysis of junctions allowed us to uncover annotated as well as non-annotated events, read coverage signals notably increased recall capabilities at a very competitive performance when compared against other state-of-the-art splicing analysis algorithms. © 2021 The Author(s). Published by Oxford University Press. All rights reserved. 
700 1 |a Chernomoretz, A.  |e author 
700 1 |a Iserte, J.  |e author 
700 1 |a Mancini, E.  |e author 
700 1 |a Rabinovich, A.  |e author 
700 1 |a Yanovsky, M.  |e author 
773 |t Bioinformatics