Bayesian genome assembly and assessment by Markov Chain Monte Carlo sampling
Mark Howison, Felipe Zapata, Erika J. Edwards, Casey W. Dunn
(Submitted on 6 Aug 2013)

Most genome assemblers provide a point estimates of the true genome sequences, chosen 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 a distribution of assembly hypotheses with quantified probabilities. This statistically explicit Bayesian approach to assembly allows the investigator to evaluate alternative assembly hypotheses in a unified framework and propagate uncertainty about genomes assembly to downstream analyses. We implement this approach in a prototype assembler and illustrate its application to the genome of the bacteriophage $\Phi$X174.