PathOGiST: A Novel Method for Clustering Pathogen Isolates by Combining Multiple Genotyping Signals

Abstract

In this work, we study the problem of clustering bacterial isolates into epidemiologically related groups from next-generation sequencing data. Existing methods for this problem mainly use a single genotyping signal, and either use a distance-based method with a pre-specified number of clusters, or a phylogenetic tree-based method with a pre-specified threshold.We propose PathOGiST, an open-source algorithmic framework for clustering bacterial isolates by leveraging multiple genotypic signals and calibrated thresholds. PathOGiST uses different genotypic signals, clusters the isolates based on these individual signals with correlation clustering, and combines the clusterings based on the individual signals with consensus clustering.We implemented and tested PathOGiST on three different bacterial pathogens -Escherichia coli, Yersinia pseudotuberculosis, and Mycobacterium tuberculosis -and found that it outperforms most existing methods. We conclude by discussing how our framework can be extended and some of the challenges that remain to be addressed.

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

“…In future work, we plan to extend SplitStrains to work with other bacterial pathogens as well as to improve its resolution, at least in datasets with a high depth of coverage. Lastly, we plan to use SplitStrains as a pre-processing step in two pipelines - one for identifying related isolates in an outbreak [35], where mixed infections can mask such relatedness, and another one for predicting drug resistance [36], where mixed infections can impede a correct prediction when only the minor strain is drug-resistant.…”

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

“…In future work, we plan to extend SplitStrains to work with other bacterial pathogens as well as to improve its resolution, at least in datasets with a high depth of coverage. Lastly, we plan to use SplitStrains as a preprocessing step in two pipelinesone for identifying related isolates in an outbreak [23], where mixed infections can mask such relatedness, and another one for predicting drug resistance [24], where mixed infections can impede a correct prediction when only the minor strain is drug-resistant.…”