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PLoS One. 2015 Jul 8;10(7):e0130912. doi: 10.1371/journal.pone.0130912. eCollection 2015.

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

Author information

1
LIRMM UM CNRS, UMR 5506, 860 rue de St Priest, 34095 Montpellier cedex 5, France.
2
Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Sud, rue Gregor Mendel, Bât 400, 91405 Orsay cedex, France.
3
Pakistan Institute for Engineering and Applied Sciences (PIEAS), Lehtrar Road, Nilore, Islamabad, Pakistan; Health Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), P.O. Box # 577, Jhang Road, Faisalabad, Pakistan.
4
Health Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), P.O. Box # 577, Jhang Road, Faisalabad, Pakistan.
5
National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands.
6
National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands; Department of Pulmonary Diseases and Department of Microbiology, Radbout University Nijmegen Medical Centre, University Lung Centre Dekkerswald, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.

Abstract

Infra-species taxonomy is a prerequisite to compare features such as virulence in different pathogen lineages. Mycobacterium tuberculosis complex taxonomy has rapidly evolved in the last 20 years through intensive clinical isolation, advances in sequencing and in the description of fast-evolving loci (CRISPR and MIRU-VNTR). On-line tools to describe new isolates have been set up based on known diversity either on CRISPRs (also known as spoligotypes) or on MIRU-VNTR profiles. The underlying taxonomies are largely concordant but use different names and offer different depths. The objectives of this study were 1) to explicit the consensus that exists between the alternative taxonomies, and 2) to provide an on-line tool to ease classification of new isolates. Genotyping (24-VNTR, 43-spacers spoligotypes, IS6110-RFLP) was undertaken for 3,454 clinical isolates from the Netherlands (2004-2008). The resulting database was enlarged with African isolates to include most human tuberculosis diversity. Assignations were obtained using TB-Lineage, MIRU-VNTRPlus, SITVITWEB and an algorithm from Borile et al. By identifying the recurrent concordances between the alternative taxonomies, we proposed a consensus including 22 sublineages. Original and consensus assignations of the all isolates from the database were subsequently implemented into an ensemble learning approach based on Machine Learning tool Weka to derive a classification scheme. All assignations were reproduced with very good sensibilities and specificities. When applied to independent datasets, it was able to suggest new sublineages such as pseudo-Beijing. This Lineage Prediction tool, efficient on 15-MIRU, 24-VNTR and spoligotype data is available on the web interface "TBminer." Another section of this website helps summarizing key molecular epidemiological data, easing tuberculosis surveillance. Altogether, we successfully used Machine Learning on a large dataset to set up and make available the first consensual taxonomy for human Mycobacterium tuberculosis complex. Additional developments using SNPs will help stabilizing it.

PMID:
26154264
PMCID:
PMC4496040
DOI:
10.1371/journal.pone.0130912
[Indexed for MEDLINE]
Free PMC Article

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