SpeciateIT and vSpeciateDB: novel, fast, and accurate per sequence 16S rRNA gene taxonomic classification of vaginal microbiota

Herein, two resources with new and practical importance are described. The novel classification algorithm, SpeciateIT, is based on 7th order Markov chain models and allows for fast and accurate per-sequence taxonomic assignments (as little as 10 min for 10<sup>7</sup> sequences). vSpeciateDB, a meticulously tailored reference database, stands as a vital and pragmatic contribution. Its significance lies in the superiority of this environment-specific database to provide more species-resolution over its universal counterparts.

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Publication Details

Journal
BMC Bioinformatics
Published
2024-09-27
DOI
https://doi.org/10.1186/s12859-024-05930-3
Citations
14
Primary Topic
Reproductive tract infections research
Type
article
Field-Weighted Citation Impact
4.83

Funders

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article

SpeciateIT and vSpeciateDB: novel, fast, and accurate per sequence 16S rRNA gene taxonomic classification of vaginal microbiota

Pawel Gajer, Johanna B. Holm, Jacques Ravel
14 citations
BMC Bioinformatics
Reproductive tract infections research
4.83
article

SpeciateIT and vSpeciateDB: novel, fast, and accurate per sequence 16S rRNA gene taxonomic classification of vaginal microbiota

Pawel Gajer, Johanna B. Holm, Jacques Ravel
article en
14 citations

Abstract

Herein, two resources with new and practical importance are described. The novel classification algorithm, SpeciateIT, is based on 7th order Markov chain models and allows for fast and accurate per-sequence taxonomic assignments (as little as 10 min for 10<sup>7</sup> sequences). vSpeciateDB, a meticulously tailored reference database, stands as a vital and pragmatic contribution. Its significance lies in the superiority of this environment-specific database to provide more species-resolution over its universal counterparts.

BMC BioinformaticsVol. 25(1)
University of Maryland, Baltimore (US)
National Institute of Allergy and Infectious Diseases
Zero hunger
Openalex Percentile: Top 4%
Reproductive tract infections research
4.83
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