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

Abstract

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.

Enhancing the differentiation of specific genotypes in Mycobacterium tuberculosis population

“…The MIRU-VNTR results were analyzed in MIRU-VNTRplus (http://www.miru vntrplus.org/MIRU/index.faces) 26 , TBminer databases (http://infodemo.lirmm.fr/tbminer/about.php) 27 and StackTB (https://stacktb.cs.sfu.ca/) 28 . Based on each VNTR loci set, a Minimum Spanning Tree (MST) was constructed using MIRU-VNTRplus database to determine genetic variations and classify the isolates.…”

Novel methods included in SpolLineages tool for fast and precise prediction ofMycobacterium tuberculosiscomplex spoligotype families

“…Several software tools allow to predict MTBC genotyping families from spoligotyping and/or MIRU-VNTR patterns; examples include the following online resources: MIRU-VNTRplus ( 17 ), StackTB ( 18 ), TB-Lineage and other TB-Insight’s tools ( 19 ) and TBminer ( 20 ); however, most of the existing tools are only available as online tools limiting the amount of data that can be analysed. Some available software tools are using more or less complex algorithmic approaches that are not easily understandable for neophytes (or persons with few or no computing skills).…”

“…RuleTB is one of the approaches used in StackTB online tool, allowing to determine MTBC families from 24-loci MIRU-VNTR basing on a direct method that proposes a concise set of rules ( 18 ). StackTB also uses a machine learning approach that requires only a fraction of training data to determine lineages.…”

Precision, Equity, and Public Health and Epidemiology Informatics – A Scoping Review

“…In this context, which some have called 'precision epidemiology' [22], the genomics of the infectious organism plays a central role [22][23][24]. Through the identification of transmission networks [25] and optimal antimicrobial therapy [26] rapid sequencing and analysis of the genomes of infectious organisms can be used to identify the best strategies for preventing transmission and treating disease. However, some have noted that genomic data on organisms is rarely sufficient to understand the mechanisms of disease transmission, and that data for 'deep phenotyping', or describing precisely the characteristics of disease, are also needed [22].…”