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Genomics and Machine Learning for Taxonomy Consensus: The Mycobacterium tuberculosis Complex Paradigm.

Jérôme Azé1, Christophe Sola2, Jian Zhang2

  • 1LIRMM UM CNRS, UMR 5506, 860 rue de St Priest, 34095 Montpellier cedex 5, France.

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|July 9, 2015
PubMed
Summary

This study establishes the first consensus taxonomy for Mycobacterium tuberculosis complex lineages using machine learning. A new online tool, TBminer, aids in classifying new isolates and enhances tuberculosis surveillance.

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Area of Science:

  • Microbiology
  • Genetics
  • Computational Biology

Background:

  • Mycobacterium tuberculosis complex (MTBC) infra-species taxonomy is crucial for understanding pathogen virulence and evolution.
  • Rapid advancements in sequencing and genotyping, including CRISPR (spoligotypes) and MIRU-VNTR, have led to evolving MTBC taxonomies.
  • Existing online tools for isolate classification based on spoligotypes or MIRU-VNTR profiles use different nomenclature and classification depths.

Purpose of the Study:

  • To establish a consensus between alternative MTBC taxonomies.
  • To develop an online tool for simplified classification of new MTBC isolates.

Main Methods:

  • Genotyping of 3,454 Dutch MTBC clinical isolates (24-VNTR, 43-spacer spoligotypes, IS6110-RFLP) and inclusion of African isolates.
  • Utilized existing assignation tools (TB-Lineage, MIRU-VNTRPlus, SITVITWEB) and an algorithm by Borile et al. to identify concordances.
  • Applied machine learning (Weka) on an ensemble learning approach for classification scheme development.

Main Results:

  • A consensus taxonomy comprising 22 sublineages was proposed based on recurrent concordances between alternative taxonomies.
  • The developed classification scheme demonstrated high sensibilities and specificities in assigning isolates.
  • The tool successfully identified potential new sublineages, such as pseudo-Beijing, when applied to independent datasets.

Conclusions:

  • Machine learning was successfully applied to create the first consensual taxonomy for the human MTBC.
  • The online tool "TBminer" facilitates MTBC isolate classification using MIRU, VNTR, and spoligotype data, aiding tuberculosis surveillance.
  • Future developments incorporating Single Nucleotide Polymorphisms (SNPs) are expected to further stabilize the MTBC taxonomy.