Splice Trap: A method to quantify alternative splicing under single cellular conditions

Jie Wu, Martin Akerman, Shuying Sun, W. Richard McCombie, Adrian R. Krainer, Michael Q. Zhang

Research output: Contribution to journalArticle

Abstract

Motivation: Alternative splicing (AS) is a pre-mRNA maturation process leading to the expression of multiple mRNA variants from the same primary transcript. More than 90% of human genes are expressed via AS. Therefore, quantifying the inclusion level of every exon is crucial for generating accurate transcriptomic maps and studying the regulation of AS. Results: Here we introduce SpliceTrap, a method to quantify exon inclusion levels using paired-end RNA-seq data. Unlike other tools, which focus on full-length transcript isoforms, SpliceTrap approaches the expression-level estimation of each exon as an independent Bayesian inference problem. In addition, SpliceTrap can identify major classes of alternative splicing events under a single cellular condition, without requiring a background set of reads to estimate relative splicing changes. We tested SpliceTrap both by simulation and real data analysis, and compared it to state-of-the-art tools for transcript quantification. SpliceTrap demonstrated improved accuracy, robustness and reliability in quantifying exon-inclusion ratios. Conclusions: SpliceTrap is a useful tool to study alternative splicing regulation, especially for accurate quantification of local exoninclusion ratios from RNA-seq data.

Original languageEnglish (US)
Article numberbtr508
Pages (from-to)3010-3016
Number of pages7
JournalBioinformatics
Volume27
Issue number21
DOIs
StatePublished - Nov 1 2011

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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    Wu, J., Akerman, M., Sun, S., McCombie, W. R., Krainer, A. R., & Zhang, M. Q. (2011). Splice Trap: A method to quantify alternative splicing under single cellular conditions. Bioinformatics, 27(21), 3010-3016. [btr508]. https://doi.org/10.1093/bioinformatics/btr508