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Genome Biol. 2020 Mar 16;21(1):69. doi: 10.1186/s13059-020-01967-8.

BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty.

Author information

1
Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland. simone.tiberi@uzh.ch.
2
Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.

Abstract

Alternative splicing is a biological process during gene expression that allows a single gene to code for multiple proteins. However, splicing patterns can be altered in some conditions or diseases. Here, we present BANDITS, a R/Bioconductor package to perform differential splicing, at both gene and transcript level, based on RNA-seq data. BANDITS uses a Bayesian hierarchical structure to explicitly model the variability between samples and treats the transcript allocation of reads as latent variables. We perform an extensive benchmark across both simulated and experimental RNA-seq datasets, where BANDITS has extremely favourable performance with respect to the competitors considered.

KEYWORDS:

Alternative splicing; Bayesian hierarchical modelling; Differential splicing; Differential transcript usage; Markov chain Monte Carlo; RNA-seq; Transcriptomics

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