Imperial College London
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DrLeonidChindelevitch

Faculty of Medicine, School of Public Health

Lecturer in Infectious Disease Epidemiology
 
 
 
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Contact

 

l.chindelevitch Website

 
 
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Location

 

Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gabbassov:2021:10.1099/mgen.0.000607,
author = {Gabbassov, E and Moreno-Molina, M and Comas, I and Libbrecht, M and Chindelevitch, L},
doi = {10.1099/mgen.0.000607},
journal = {Microbial Genomics},
pages = {1--16},
title = {SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data.},
url = {http://dx.doi.org/10.1099/mgen.0.000607},
volume = {7},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Gabbassov,E
AU  - Moreno-Molina,M
AU  - Comas,I
AU  - Libbrecht,M
AU  - Chindelevitch,L
DO  - 10.1099/mgen.0.000607
EP  - 16
PY  - 2021///
SN  - 2057-5858
SP  - 1
TI  - SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data.
T2  - Microbial Genomics
UR  - http://dx.doi.org/10.1099/mgen.0.000607
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34165419
UR  - https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.000607
UR  - http://hdl.handle.net/10044/1/90204
VL  - 7
ER  -
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