STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions

Sequence Read Archive submissions to the National Center for Biotechnology Information often lack useful metadata, which limits the utility of these submissions. We describe the Sequence Taxonomic Analysis Tool (STAT), a scalable k-mer-based tool for fast assessment of taxonomic diversity intrinsic...

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Autores principales: Katz, Kenneth S., Shutov, Oleg, Lapoint, Richard, Kimelman, Michael, Brister, J. Rodney, O’Sullivan, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450716/
https://www.ncbi.nlm.nih.gov/pubmed/34544477
http://dx.doi.org/10.1186/s13059-021-02490-0
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author Katz, Kenneth S.
Shutov, Oleg
Lapoint, Richard
Kimelman, Michael
Brister, J. Rodney
O’Sullivan, Christopher
author_facet Katz, Kenneth S.
Shutov, Oleg
Lapoint, Richard
Kimelman, Michael
Brister, J. Rodney
O’Sullivan, Christopher
author_sort Katz, Kenneth S.
collection PubMed
description Sequence Read Archive submissions to the National Center for Biotechnology Information often lack useful metadata, which limits the utility of these submissions. We describe the Sequence Taxonomic Analysis Tool (STAT), a scalable k-mer-based tool for fast assessment of taxonomic diversity intrinsic to submissions, independent of metadata. We show that our MinHash-based k-mer tool is accurate and scalable, offering reliable criteria for efficient selection of data for further analysis by the scientific community, at once validating submissions while also augmenting sample metadata with reliable, searchable, taxonomic terms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02490-0.
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spelling pubmed-84507162021-09-20 STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions Katz, Kenneth S. Shutov, Oleg Lapoint, Richard Kimelman, Michael Brister, J. Rodney O’Sullivan, Christopher Genome Biol Method Sequence Read Archive submissions to the National Center for Biotechnology Information often lack useful metadata, which limits the utility of these submissions. We describe the Sequence Taxonomic Analysis Tool (STAT), a scalable k-mer-based tool for fast assessment of taxonomic diversity intrinsic to submissions, independent of metadata. We show that our MinHash-based k-mer tool is accurate and scalable, offering reliable criteria for efficient selection of data for further analysis by the scientific community, at once validating submissions while also augmenting sample metadata with reliable, searchable, taxonomic terms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02490-0. BioMed Central 2021-09-20 /pmc/articles/PMC8450716/ /pubmed/34544477 http://dx.doi.org/10.1186/s13059-021-02490-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Katz, Kenneth S.
Shutov, Oleg
Lapoint, Richard
Kimelman, Michael
Brister, J. Rodney
O’Sullivan, Christopher
STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions
title STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions
title_full STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions
title_fullStr STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions
title_full_unstemmed STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions
title_short STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions
title_sort stat: a fast, scalable, minhash-based k-mer tool to assess sequence read archive next-generation sequence submissions
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450716/
https://www.ncbi.nlm.nih.gov/pubmed/34544477
http://dx.doi.org/10.1186/s13059-021-02490-0
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