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

Azé J, Sola C, Zhang J, Lafosse-Marin F, Yasmin M, Siddiqui R, Kremer K, van Soolingen D, Refrégier G - PLoS ONE (2015)

Bottom Line: 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.Additional developments using SNPs will help stabilizing it.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.


Related in: MedlinePlus

Concordance of existing classifications with the consensus classification proposed in this study.
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pone.0130912.g002: Concordance of existing classifications with the consensus classification proposed in this study.

Mentions: We measured the concordance of every existing taxonomies with the newly proposed “expert” one on all isolates of our database. This consensual “expert” taxonomy reached almost perfect concordance with that of TB-lineage as retrieved using 24VNTR and spoligotype data (99.7%; discordant points n = 9; Fig 2). Although priority in naming was given to MIRU-VNTRPlus taxonomy during consensus building (see Methods), the “expert” taxonomy reached a very good concordance with the SITVITWEB taxonomy refined using expert knowledge (85% of concordant precise assignations; n = 2,464; Fig 2).


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

Azé J, Sola C, Zhang J, Lafosse-Marin F, Yasmin M, Siddiqui R, Kremer K, van Soolingen D, Refrégier G - PLoS ONE (2015)

Concordance of existing classifications with the consensus classification proposed in this study.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496040&req=5

pone.0130912.g002: Concordance of existing classifications with the consensus classification proposed in this study.
Mentions: We measured the concordance of every existing taxonomies with the newly proposed “expert” one on all isolates of our database. This consensual “expert” taxonomy reached almost perfect concordance with that of TB-lineage as retrieved using 24VNTR and spoligotype data (99.7%; discordant points n = 9; Fig 2). Although priority in naming was given to MIRU-VNTRPlus taxonomy during consensus building (see Methods), the “expert” taxonomy reached a very good concordance with the SITVITWEB taxonomy refined using expert knowledge (85% of concordant precise assignations; n = 2,464; Fig 2).

Bottom Line: 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.Additional developments using SNPs will help stabilizing it.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.


Related in: MedlinePlus