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.Contact: james.hensman@sheffield.ac.uk or panagiotis.papastamoulis@manchester.ac.uk or Magnus.Rattray@manchester.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
We used human data (SRR307907) from the ENCODE project in order to capture the dynamics of realistic RNA-seq datasets
We describe the generative process of the RPK values used in the simulation section of the manuscript
Denote by RPKjm the RPK value for transcript m = 1, . . . , M at replicate j = 1, . . . , J, where J and M denote the number of replicates and the total number of transcripts, respectively
Summary
We used human data (SRR307907) from the ENCODE project in order to capture the dynamics of realistic RNA-seq datasets. BitSeq (MCMC) was used to estimate the relative expression levels of M = 48009 transcripts. The resulting estimates was used as input to generate the baseline mean. Reads were simulated according to the following generative process: RPKjm ∼ N B(μm, 50), m = 1, . M denoting the corresponding estimates of RPK values according to BitSeq MCMC. This resulted in 56 million paired-end reads of 76 base-pairs per replicate, almost 280 million reads in total. Note that BitSeqMCMC, BitSeqVB and Casper are the only methods which avoid extreme outliers on the boundary of the inter-replicate consistency graphs
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