Bayesian identification of bacterial strains from sequencing data

TL;DR

This paper presents a Bayesian statistical pipeline for rapid, accurate identification of bacterial strains from sequencing data, especially closely related strains, outperforming existing methods with modest computational resources.

Contribution

It introduces a novel Bayesian approach for bacterial strain identification that improves accuracy and speed over current pipelines, suitable for clinical and environmental applications.

Findings

Outperforms existing bacterial identification pipelines

Enables fast and accurate strain detection from sequencing data

Uses modest computational resources for practical deployment

Abstract

Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an evironmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, resistance and spread. Using advanced Bayesian statistical modelling and computation techniques we introduce a novel pipeline for bacterial identification that is shown to outperform the currently leading pipeline for this purpose. Our approach enables fast and accurate sequence-based identification of bacterial strains while using only modest computational resources. Hence it provides a useful tool for a wide spectrum of applications, including rapid clinical diagnostics to distinguish among closely related strains causing nosocomial infections. The software implementation is available at https://github.com/PROBIC/BIB