SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data

TL;DR

SplitStrains is a new statistical tool that accurately detects and separates mixed M. tuberculosis infections from whole-genome sequencing data, improving strain identification and proportion estimation.

Contribution

It introduces a novel, statistically grounded method that outperforms existing approaches in identifying and quantifying mixed bacterial strains from WGS data.

Findings

Superior performance in strain proportion estimation on simulated data

Effective identification of underlying strains in real M. tuberculosis samples

Enhances analysis capabilities for bacterial mixed infections

Abstract

The occurrence of multiple strains of a bacterial pathogen such as M. tuberculosis or C. difficile within a single human host, referred to as a mixed infection, has important implications for both healthcare and public health. However, methods for detecting it, and especially determining the proportion and identities of the underlying strains, from WGS (whole-genome sequencing) data, have been limited. In this paper we introduce SplitStrains, a novel method for addressing these challenges. Grounded in a rigorous statistical model, SplitStrains not only demonstrates superior performance in proportion estimation to other existing methods on both simulated as well as real M. tuberculosis data, but also successfully determines the identity of the underlying strains. We conclude that SplitStrains is a powerful addition to the existing toolkit of analytical methods for data coming from bacterial pathogens and holds the promise of enabling previously inaccessible conclusions to be drawn in the realm of public health microbiology