Paper Info
Title
TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference.
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.
Year
DOI
Venue
2013
10.1093/bioinformatics/btt381
BIOINFORMATICS
Field
DocType
Volume
Sequence alignment,Data mining,Gene isoform,RNA Isoforms,Bayesian inference,Computer science,Expectation–maximization algorithm,Alternative splicing,Bioinformatics,Human genome,Bayes' theorem
Journal
29
Issue
ISSN
Citations 
18
1367-4803
10
PageRank 
References 
Authors
0.75
11
4
Name
Order
Citations
PageRank
Naoki Nariai1886.97
Osamu Hirose2698.27
Kaname Kojima315211.12
Masao Nagasaki436826.22