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

Simone Tiberi1, Mark D Robinson2

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

Genome Biology
|March 18, 2020
PubMed
Summary

Alternative splicing, a key gene expression process, can change in disease. We developed BANDITS, a R/Bioconductor package for differential splicing analysis using RNA-seq data, showing superior performance in benchmarks.

Keywords:
Alternative splicingBayesian hierarchical modellingDifferential splicingDifferential transcript usageMarkov chain Monte CarloRNA-seqTranscriptomics

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Alternative splicing allows a single gene to produce multiple proteins.
  • Altered splicing patterns are implicated in various diseases.
  • Accurate differential splicing analysis is crucial for understanding gene expression.

Purpose of the Study:

  • To introduce BANDITS, a novel R/Bioconductor package for differential splicing analysis.
  • To provide a robust method for analyzing differential splicing at both gene and transcript levels using RNA-seq data.
  • To evaluate the performance of BANDITS against existing methods.

Main Methods:

  • BANDITS employs a Bayesian hierarchical model.
  • It explicitly models inter-sample variability.
  • Transcript read allocation is treated as a latent variable.

Main Results:

  • BANDITS demonstrated favorable performance in benchmark tests.
  • The package was evaluated on both simulated and experimental RNA-seq datasets.
  • It showed superior results compared to other considered methods.

Conclusions:

  • BANDITS offers a powerful new tool for differential splicing analysis.
  • The package provides accurate and reliable results for RNA-seq data.
  • It is a valuable addition to the bioinformatics toolkit for gene expression studies.