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Infect Genet Evol. 2018 Jun 28. pii: S1567-1348(18)30441-6. doi: 10.1016/j.meegid.2018.06.029. [Epub ahead of print]

Towards better prediction of Mycobacterium tuberculosis lineages from MIRU-VNTR data.

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School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.
New York City Department of Health and Mental Hygiene, Queens, NY, USA.
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
Public Health Research Institute TB Center, Rutgers University, Newark, NJ, USA.
Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
School of Computing Science, Simon Fraser University, Burnaby, BC, Canada. Electronic address:


The determination of lineages from strain-based molecular genotyping information is an important problem in tuberculosis. Mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) typing is a commonly used molecular genotyping approach that uses counts of the number of times pre-specified loci repeat in a strain. There are three main approaches for determining lineage based on MIRU-VNTR data - one based on a direct comparison to the strains in a curated database, and two others, on machine learning algorithms trained on a large collection of labeled data. All existing methods have limitations. The direct approach imposes an arbitrary threshold on how much a database strain can differ from a given one to be informative. On the other hand, the machine learning-based approaches require a substantial amount of labeled data. Notably, all three methods exhibit suboptimal classification accuracy without additional data. We explore several computational approaches to address these limitations. First, we show that eliminating the arbitrary threshold improves the performance of the direct approach. Second, we introduce RuleTB, an alternative direct method that proposes a concise set of rules for determining lineages. Lastly, we propose StackTB, a machine learning approach that requires only a fraction of the training data to outperform the accuracy of both existing machine learning methods. Our approaches demonstrate superior performance on a training dataset collected in New York City over 10 years, and the improvement in performance translates to a held-out testing set. We conclude that our methods provide opportunities for improving the determination of pathogenic lineages based on MIRU-VNTR data.


Interpretability; Lineage; MIRU-VNTR; Machine learning; Mycobacterium tuberculosis

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