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Bioinformatics. 2015 Dec 15;31(24):3881-9. doi: 10.1093/bioinformatics/btv483. Epub 2015 Aug 26.

Fast and accurate approximate inference of transcript expression from RNA-seq data.

Author information

1
Sheffield Institute for Translational Neuroscience (SITraN), Sheffield, UK.
2
Faculty of Life Sciences.
3
School of Computer Science, The University of Manchester, Manchester, UK and.
4
Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland.

Abstract

MOTIVATION:

Assigning RNA-seq reads to their transcript of origin is a fundamental task in transcript expression estimation. Where ambiguities in assignments exist due to transcripts sharing sequence, e.g. alternative isoforms or alleles, the problem can be solved through probabilistic inference. Bayesian methods have been shown to provide accurate transcript abundance estimates compared with competing methods. However, exact Bayesian inference is intractable and approximate methods such as Markov chain Monte Carlo and Variational Bayes (VB) are typically used. While providing a high degree of accuracy and modelling flexibility, standard implementations can be prohibitively slow for large datasets and complex transcriptome annotations.

RESULTS:

We propose a novel approximate inference scheme based on VB and apply it to an existing model of transcript expression inference from RNA-seq data. Recent advances in VB algorithmics are used to improve the convergence of the algorithm beyond the standard Variational Bayes Expectation Maximization algorithm. We apply our algorithm to simulated and biological datasets, demonstrating a significant increase in speed with only very small loss in accuracy of expression level estimation. We carry out a comparative study against seven popular alternative methods and demonstrate that our new algorithm provides excellent accuracy and inter-replicate consistency while remaining competitive in computation time.

AVAILABILITY AND IMPLEMENTATION:

The methods were implemented in R and C++, and are available as part of the BitSeq project at github.com/BitSeq. The method is also available through the BitSeq Bioconductor package. The source code to reproduce all simulation results can be accessed via github.com/BitSeq/BitSeqVB_benchmarking.

PMID:
26315907
PMCID:
PMC4673974
DOI:
10.1093/bioinformatics/btv483
[Indexed for MEDLINE]
Free PMC Article

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