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

Bioinformatics
James HensmanMagnus Rattray

Abstract

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. 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...Continue Reading

References

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Citations

May 24, 2016·BioData Mining·Katherine IcaySampsa Hautaniemi
Jan 23, 2018·Journal of the Royal Statistical Society. Series C, Applied Statistics·Panagiotis Papastamoulis, Magnus Rattray
Nov 2, 2017·Statistical Applications in Genetics and Molecular Biology·Panagiotis Papastamoulis, Magnus Rattray
Mar 30, 2017·Microbial Genomics·Aravind SankarAntti Honkela

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Datasets Mentioned

BETA
SRR307898
SRR307902
SRR307912
SRR307916
SRR307908
SRR307907

Methods Mentioned

BETA
RNA-seq
BitSeq

Software Mentioned

Tophat
Spanki
Tigar
Bowtie2
Bowtie1
RSEM
BitSeqVB
BitSeqMCMC
WGE
Sailfish

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