Limits...
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

TBminer Lineage Prediction tool: the output file.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4496040&req=5

pone.0130912.g003: TBminer Lineage Prediction tool: the output file.

Mentions: A Lineage prediction tool on the website TBminer was set up to make the Vote-10 classifier available to all users. Underlying rules and trees were derived using the complete training dataset. The web interface allows to upload any genotypic data including CRISPR spacer data (1 to 43 standard spacer set), and 24 standard MIRU-VNTR numbers of repeats. When including at least 43-spacers spoligotype data or 15-MIRU, TBminer Lineage Prediction webtool provides an output file with 7 new columns, one column for each of the 7 predictions mentioned in Table 3 (Pred1 to Pred5bis), as well as 2 consensus columns clarifying whether the predictions using spoligotype data and MIRU-VNTR data are concordant (Pred6 and Pred6bis; Fig 3).


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)

TBminer Lineage Prediction tool: the output file.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130912.g003: TBminer Lineage Prediction tool: the output file.
Mentions: A Lineage prediction tool on the website TBminer was set up to make the Vote-10 classifier available to all users. Underlying rules and trees were derived using the complete training dataset. The web interface allows to upload any genotypic data including CRISPR spacer data (1 to 43 standard spacer set), and 24 standard MIRU-VNTR numbers of repeats. When including at least 43-spacers spoligotype data or 15-MIRU, TBminer Lineage Prediction webtool provides an output file with 7 new columns, one column for each of the 7 predictions mentioned in Table 3 (Pred1 to Pred5bis), as well as 2 consensus columns clarifying whether the predictions using spoligotype data and MIRU-VNTR data are concordant (Pred6 and Pred6bis; Fig 3).

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