Bayesian genome assembly and assessment by Markov Chain Monte Carlo sampling

TL;DR

This paper introduces a Bayesian genome assembly method using Markov Chain Monte Carlo sampling to generate probability distributions over possible assemblies, enabling better uncertainty assessment.

Contribution

It presents a novel Bayesian approach with MCMC sampling for genome assembly, moving beyond point estimates to probabilistic hypothesis evaluation.

Findings

Provides posterior probability distributions for assemblies

Demonstrates application on bacteriophage PhiX174

Enables explicit assessment of assembly uncertainty

Abstract

Most genome assemblers construct point estimates, choosing a 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 and illustrate its application to the bacteriophage PhiX174.