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 very similar to each other.
Researchers at Tohoku University, Japan have developed a statistical method to estimate transcript isoform abundances from RNA-Seq data. This 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 data sets, this 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 (EM) algorithm. The researchers also applied their method to RNA-Seq data of human cell line samples, and showed that the prediction result was more consistent among technical replicates than those of other methods.
AVAILABILITY: An implementation of our method is available at http://github.com/nariai/tigar
- Nariai N, Hirose O, Kojima K, Nagasaki M. (2013) TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference. Bioinformatics [Epub ahead of print]. [abstract]