TIGAR: Transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference

Naoki Nariai, Osamu Hirose, Kaname Kojima, Masao Nagasaki

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Motivation: Many human genes express multiple transcript isoforms through alternative splicing, which greatly increases diversity of protein function. Although RNA sequencing (RNA-Seq) technologies have been widely used in measuring amounts of transcribed mRNA, accurate estimation of transcript isoform abundances from RNA-Seq data is challenging because reads often map to more than one transcript isoforms or paralogs whose sequences are similar to each other.Results: We propose a statistical method to estimate transcript isoform abundances from RNA-Seq data. Our method can handle gapped alignments of reads against reference sequences so that it allows insertion or deletion errors within reads. The proposed method optimizes the number of transcript isoforms by variational Bayesian inference through an iterative procedure, and its convergence is guaranteed under a stopping criterion. On simulated datasets, our method outperformed the comparable quantification methods in inferring transcript isoform abundances, and at the same time its rate of convergence was faster than that of the expectation maximization algorithm. We also applied our method to RNA-Seq data of human cell line samples, and showed that our prediction result was more consistent among technical replicates than those of other methods.

Original languageEnglish
Pages (from-to)2292-2299
Number of pages8
JournalBioinformatics
Volume29
Issue number18
DOIs
Publication statusPublished - 2013 Sep 15

ASJC Scopus subject areas

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

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