  Motivation: The mapping of RNA-seq reads to their transcripts of origin is a
fundamental task in transcript expression estimation and differential
expression scoring. Where ambiguities in mapping exist due to transcripts
sharing sequence, e.g. alternative isoforms or alleles, the problem becomes an
instance of non-trivial probabilistic inference. Bayesian inference in such a
problem is intractable and approximate methods must be used such as Markov
chain Monte Carlo (MCMC) and Variational Bayes. Standard implementations of
these methods can be prohibitively slow for large datasets and complex gene
models.
  Results: We propose an approximate inference scheme based on Variational
Bayes applied to an existing model of transcript expression inference from
RNA-seq data. We apply recent advances in Variational Bayes algorithmics to
improve the convergence of the algorithm beyond the standard variational
expectation-maximisation approach. We apply our algorithm to simulated and
biological datasets, demonstrating that the increase in speed requires only a
small trade-off in accuracy of expression level estimation.
  Availability: The methods were implemented in R and C++, and are available as
part of the BitSeq project at https://code.google.com/p/bitseq/. The methods
will be made available through the BitSeq Bioconductor package at the next
stable release.
