TY - JOUR T1 - SplitStrains, a tool to identify and separate mixed <em>Mycobacterium tuberculosis</em> infections from WGS data JF - medRxiv DO - 10.1101/2021.02.07.21250981 SP - 2021.02.07.21250981 AU - Gabbasov, Einar AU - Moreno-Molina, Miguel AU - Comas, Iñaki AU - Libbrecht, Maxwell AU - Chindelevitch, Leonid Y1 - 2021/01/01 UR - http://medrxiv.org/content/early/2021/02/08/2021.02.07.21250981.abstract N2 - 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.Author summary When multiple strains of a pathogenic organism are present in a patient, it may be necessary to not only detect this, but also to identify the individual strains. However, this problem has not yet been solved for bacterial pathogens processed via whole-genome sequencing. In this paper, we propose the SplitStrains algorithm for detecting multiple strains in a sample, identifying their proportions, and inferring their sequences, in the case of Mycobacterium tuberculosis. We test it on both simulated and real data, with encouraging results. We believe that our work opens new horizons in public health microbiology by allowing a more precise detection, identification and quantification of multiple infecting strains within a sample.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work has been funded in part by a CANSSI Collaborative Research Team grant, 'Statistical methods for challenging problems in public health microbiology' and a Genome Canada grant, 'Machine Learning Methods to Predict Drug Resistance in Pathogenic Bacteria'. LC acknowledges funding from a Sloan Foundation fellowship (FG-2016-6392) and the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth &amp; Development Office (FCDO), under the MRC/FCDO Concordat agreement, and is part of the EDCTP2 programme supported by the European Union. ML acknowledges funding from a NSERC Discovery grant.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Not applicableAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data related to this manuscript is freely available. https://github.com/WGS-TB/SplitStrains ER -