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GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species

Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present GBC, a toolkit for rapidly compressing large-scale genotypes into highly addressable byte-encoding blocks under an optimized parallel framew...

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Autores principales: Zhang, Liubin, Yuan, Yangyang, Peng, Wenjie, Tang, Bin, Li, Mulin Jun, Gui, Hongsheng, Wang, Qiang, Li, Miaoxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10108510/
https://www.ncbi.nlm.nih.gov/pubmed/37069653
http://dx.doi.org/10.1186/s13059-023-02906-z
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author Zhang, Liubin
Yuan, Yangyang
Peng, Wenjie
Tang, Bin
Li, Mulin Jun
Gui, Hongsheng
Wang, Qiang
Li, Miaoxin
author_facet Zhang, Liubin
Yuan, Yangyang
Peng, Wenjie
Tang, Bin
Li, Mulin Jun
Gui, Hongsheng
Wang, Qiang
Li, Miaoxin
author_sort Zhang, Liubin
collection PubMed
description Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present GBC, a toolkit for rapidly compressing large-scale genotypes into highly addressable byte-encoding blocks under an optimized parallel framework. We demonstrate that GBC is up to 1000 times faster than state-of-the-art methods to access and manage compressed large-scale genotypes while maintaining a competitive compression ratio. We also showed that conventional analysis would be substantially sped up if built on GBC to access genotypes of a large population. GBC’s data structure and algorithms are valuable for accelerating large-scale genomic research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02906-z.
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spelling pubmed-101085102023-04-18 GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species Zhang, Liubin Yuan, Yangyang Peng, Wenjie Tang, Bin Li, Mulin Jun Gui, Hongsheng Wang, Qiang Li, Miaoxin Genome Biol Method Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present GBC, a toolkit for rapidly compressing large-scale genotypes into highly addressable byte-encoding blocks under an optimized parallel framework. We demonstrate that GBC is up to 1000 times faster than state-of-the-art methods to access and manage compressed large-scale genotypes while maintaining a competitive compression ratio. We also showed that conventional analysis would be substantially sped up if built on GBC to access genotypes of a large population. GBC’s data structure and algorithms are valuable for accelerating large-scale genomic research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02906-z. BioMed Central 2023-04-17 /pmc/articles/PMC10108510/ /pubmed/37069653 http://dx.doi.org/10.1186/s13059-023-02906-z Text en © The Author(s) 2023 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
Zhang, Liubin
Yuan, Yangyang
Peng, Wenjie
Tang, Bin
Li, Mulin Jun
Gui, Hongsheng
Wang, Qiang
Li, Miaoxin
GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species
title GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species
title_full GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species
title_fullStr GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species
title_full_unstemmed GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species
title_short GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species
title_sort gbc: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10108510/
https://www.ncbi.nlm.nih.gov/pubmed/37069653
http://dx.doi.org/10.1186/s13059-023-02906-z
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