Fast and robust metagenomic sequence comparison through sparse chaining with skani

Sequence comparison tools for metagenome-assembled genomes (MAGs) struggle with high-volume or low-quality data. We present skani (https://github.com/bluenote-1577/skani), a method for determining average nucleotide identity (ANI) via sparse approximate alignments. skani outperforms FastANI in accur...

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Detalles Bibliográficos
Autores principales: Shaw, Jim, Yu, Yun William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2023
Materias:
Brief Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630134/
https://www.ncbi.nlm.nih.gov/pubmed/37735570
http://dx.doi.org/10.1038/s41592-023-02018-3
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author Shaw, Jim
Yu, Yun William
author_facet Shaw, Jim
Yu, Yun William
author_sort Shaw, Jim
collection PubMed
description Sequence comparison tools for metagenome-assembled genomes (MAGs) struggle with high-volume or low-quality data. We present skani (https://github.com/bluenote-1577/skani), a method for determining average nucleotide identity (ANI) via sparse approximate alignments. skani outperforms FastANI in accuracy and speed (>20× faster) for fragmented, incomplete MAGs. skani can query genomes against >65,000 prokaryotic genomes in seconds and 6 GB memory. skani unlocks higher-resolution insights for extensive, noisy metagenomic datasets.
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spelling pubmed-106301342023-11-09 Fast and robust metagenomic sequence comparison through sparse chaining with skani Shaw, Jim Yu, Yun William Nat Methods Brief Communication Sequence comparison tools for metagenome-assembled genomes (MAGs) struggle with high-volume or low-quality data. We present skani (https://github.com/bluenote-1577/skani), a method for determining average nucleotide identity (ANI) via sparse approximate alignments. skani outperforms FastANI in accuracy and speed (>20× faster) for fragmented, incomplete MAGs. skani can query genomes against >65,000 prokaryotic genomes in seconds and 6 GB memory. skani unlocks higher-resolution insights for extensive, noisy metagenomic datasets. Nature Publishing Group US 2023-09-21 2023 /pmc/articles/PMC10630134/ /pubmed/37735570 http://dx.doi.org/10.1038/s41592-023-02018-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Brief Communication
Shaw, Jim
Yu, Yun William
Fast and robust metagenomic sequence comparison through sparse chaining with skani
title Fast and robust metagenomic sequence comparison through sparse chaining with skani
title_full Fast and robust metagenomic sequence comparison through sparse chaining with skani
title_fullStr Fast and robust metagenomic sequence comparison through sparse chaining with skani
title_full_unstemmed Fast and robust metagenomic sequence comparison through sparse chaining with skani
title_short Fast and robust metagenomic sequence comparison through sparse chaining with skani
title_sort fast and robust metagenomic sequence comparison through sparse chaining with skani
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630134/
https://www.ncbi.nlm.nih.gov/pubmed/37735570
http://dx.doi.org/10.1038/s41592-023-02018-3
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