Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis

Here, we develop k -mer substring space decomposition (Kssd), a sketching technique which is significantly faster and more accurate than current sketching methods. We show that it is the only method that can be used for large-scale dataset comparisons at population resolution on simulated and real d...

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Autores principales: Yi, Huiguang, Lin, Yanling, Lin, Chengqi, Jin, Wenfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962209/
https://www.ncbi.nlm.nih.gov/pubmed/33726811
http://dx.doi.org/10.1186/s13059-021-02303-4
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author Yi, Huiguang
Lin, Yanling
Lin, Chengqi
Jin, Wenfei
author_facet Yi, Huiguang
Lin, Yanling
Lin, Chengqi
Jin, Wenfei
author_sort Yi, Huiguang
collection PubMed
description Here, we develop k -mer substring space decomposition (Kssd), a sketching technique which is significantly faster and more accurate than current sketching methods. We show that it is the only method that can be used for large-scale dataset comparisons at population resolution on simulated and real data. Using Kssd, we prioritize references for all 1,019,179 bacteria whole genome sequencing (WGS) runs from NCBI Sequence Read Archive and find misidentification or contamination in 6164 of these. Additionally, we analyze WGS and exome runs of samples from the 1000 Genomes Project. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02303-4.
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spelling pubmed-79622092021-03-16 Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis Yi, Huiguang Lin, Yanling Lin, Chengqi Jin, Wenfei Genome Biol Software Here, we develop k -mer substring space decomposition (Kssd), a sketching technique which is significantly faster and more accurate than current sketching methods. We show that it is the only method that can be used for large-scale dataset comparisons at population resolution on simulated and real data. Using Kssd, we prioritize references for all 1,019,179 bacteria whole genome sequencing (WGS) runs from NCBI Sequence Read Archive and find misidentification or contamination in 6164 of these. Additionally, we analyze WGS and exome runs of samples from the 1000 Genomes Project. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02303-4. BioMed Central 2021-03-16 /pmc/articles/PMC7962209/ /pubmed/33726811 http://dx.doi.org/10.1186/s13059-021-02303-4 Text en © The Author(s) 2021 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/. The Creative Commons Public Domain Dedication waiver (http://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 Software
Yi, Huiguang
Lin, Yanling
Lin, Chengqi
Jin, Wenfei
Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis
title Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis
title_full Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis
title_fullStr Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis
title_full_unstemmed Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis
title_short Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis
title_sort kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962209/
https://www.ncbi.nlm.nih.gov/pubmed/33726811
http://dx.doi.org/10.1186/s13059-021-02303-4
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