  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 to competing methods. However, exact Bayesian inference is
intractable and approximate methods such as Markov chain Monte Carlo (MCMC) 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 Maximisation
(VBEM) 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: The methods were implemented in R and C++, and are available as
part of the BitSeq project at \url{https://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
\url{https://github.com/BitSeq/BitSeqVB_benchmarking}.
