Bayesian genome assembly and assessment by markov chain monte carlo sampling.

ABSTRACT: 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 [Formula: see text]X174. Our sampling strategy achieves both good mixing and convergence on Illumina test data for [Formula: see text]X174, demonstrating the feasibility of our approach. We summarize the po

SUBMITTER: Howison M 

PROVIDER: S-EPMC4072599 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature