Bayesian genome assembly and assessment by Markov chain Monte Carlo sampling

Citations of this article
Mendeley users who have this article in their library.


Most genome assemblers construct point estimates, choosing only a single genome sequence from among many alternative hypotheses that are supported by the data. We present a Markov chain Monte Carlo approach to sequence assembly that instead generates distributions of assembly hypotheses with posterior probabilities, providing an explicit statistical framework for evaluating alternative hypotheses and assessing assembly uncertainty. We implement this approach in a prototype assembler, called Genome Assembly by Bayesian Inference (GABI), and illustrate its application to the bacteriophage ΦX174. Our sampling strategy achieves both good mixing and convergence on Illumina test data for ΦX174, demonstrating the feasibility of our approach. We summarize the posterior distribution of assembly hypotheses generated by GABI as a majority-rule consensus assembly. Then we compare the posterior distribution to external assemblies of the same test data, and annotate those assemblies by assigning posterior probabilities to features that are in common with GABI's assembly graph. GABI is freely available under a GPL license from © 2014 Howison et al.




Howison, M., Zapata, F., Edwards, E. J., & Dunn, C. W. (2014). Bayesian genome assembly and assessment by Markov chain Monte Carlo sampling. PLoS ONE, 9(6).

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free