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

Hensman, James and Papastamoulis, Panagiotis and Glaus, Peter and Honkela, Antti and Rattray, Magnus (2015) Fast and accurate approximate inference of transcript expression from RNA-seq data. Bioinformatics, 31 (24). pp. 3881-3889. ISSN 1367-4803

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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.

Item Type: Journal Article
Journal or Publication Title: Bioinformatics
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
Departments: Faculty of Health and Medicine > Medicine
ID Code: 84436
Deposited By: ep_importer_pure
Deposited On: 27 Jan 2017 16:50
Refereed?: Yes
Published?: Published
Last Modified: 15 Jan 2020 03:04
URI: https://eprints.lancs.ac.uk/id/eprint/84436

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