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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| Veröffentlicht in: | Genome Biology Jg. 24; H. 1; S. 76 |
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| Hauptverfasser: | , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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London
BioMed Central
17.04.2023
Springer Nature B.V BMC |
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| ISSN: | 1474-760X, 1474-7596, 1474-760X |
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| Abstract | 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. |
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| AbstractList | 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. Abstract 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. 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. 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.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. |
| ArticleNumber | 76 |
| Author | Peng, Wenjie Li, Mulin Jun Yuan, Yangyang Li, Miaoxin Wang, Qiang Zhang, Liubin Gui, Hongsheng Tang, Bin |
| Author_xml | – sequence: 1 givenname: Liubin surname: Zhang fullname: Zhang, Liubin organization: Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-Sen University, Center for Precision Medicine, Sun Yat-Sen University, Center for Disease Genome Research, Sun Yat-Sen University – sequence: 2 givenname: Yangyang surname: Yuan fullname: Yuan, Yangyang organization: Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-Sen University, Center for Precision Medicine, Sun Yat-Sen University, Center for Disease Genome Research, Sun Yat-Sen University, School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University – sequence: 3 givenname: Wenjie surname: Peng fullname: Peng, Wenjie organization: Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-Sen University, Center for Precision Medicine, Sun Yat-Sen University, Center for Disease Genome Research, Sun Yat-Sen University – sequence: 4 givenname: Bin surname: Tang fullname: Tang, Bin organization: Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-Sen University, Center for Precision Medicine, Sun Yat-Sen University, Center for Disease Genome Research, Sun Yat-Sen University – sequence: 5 givenname: Mulin Jun surname: Li fullname: Li, Mulin Jun organization: The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University – sequence: 6 givenname: Hongsheng surname: Gui fullname: Gui, Hongsheng organization: Behavioral Health Services, Henry Ford Health, Center for Health Policy & Health Services Research, Henry Ford Health – sequence: 7 givenname: Qiang surname: Wang fullname: Wang, Qiang organization: Mental Health Center, West China Hospital, Sichuan University – sequence: 8 givenname: Miaoxin orcidid: 0000-0002-4733-0109 surname: Li fullname: Li, Miaoxin email: limiaoxin@mail.sysu.edu.cn organization: Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-Sen University, Center for Precision Medicine, Sun Yat-Sen University, Center for Disease Genome Research, Sun Yat-Sen University, Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University |
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| Cites_doi | 10.1038/nature15393 10.1111/1755-0998.13438 10.1093/bioinformatics/bty875 10.1086/519795 10.1093/gigascience/giab008 10.1093/bioinformatics/btr330 10.1093/bioinformatics/btaa290 10.1093/bioinformatics/bty023 10.1016/j.cell.2019.09.019 10.5281/zenodo.7737556 10.1038/s41586-018-0579-z 10.1093/bioinformatics/btz508 10.1016/j.xgen.2021.100029 10.1093/bioinformatics/btw437 10.1038/nmeth.3654 10.1126/science.356262 10.1093/bioinformatics/btx145 10.1093/bioinformatics/btt460 10.1101/2020.12.18.423437 10.1093/bioinformatics/btv613 10.1093/bioinformatics/btu014 10.1038/s41586-021-03205-y |
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| Keywords | Parallelization algorithm Cloud computation Genotype compression Highly addressable genotype blocks Genotype management Byte-encoding genotypes Large-scale genotypes |
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| Snippet | Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present... Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present... Abstract Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here,... |
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| SubjectTerms | Algorithms Animal Genetics and Genomics Bioinformatics Biomedical and Life Sciences Byte-encoding genotypes Chromosomes Compression Data Compression - methods Datasets Design Evolutionary Biology genome Genomes Genomics Genomics - methods Genotype Genotype & phenotype Genotype compression Genotype management Genotypes Highly addressable genotype blocks Human Genetics Humans Large-scale genotypes Life Sciences Localization memory Method Microbial Genetics and Genomics Parallelization algorithm Plant Genetics and Genomics Software species Whole genome sequencing |
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| Title | GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species |
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