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
Hauptverfasser: Zhang, Liubin, Yuan, Yangyang, Peng, Wenjie, Tang, Bin, Li, Mulin Jun, Gui, Hongsheng, Wang, Qiang, Li, Miaoxin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 17.04.2023
Springer Nature B.V
BMC
Schlagworte:
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
Parallelization algorithm
Cloud computation
Genotype compression
Highly addressable genotype blocks
Genotype management
Byte-encoding genotypes
Large-scale genotypes
ISSN:1474-760X, 1474-7596, 1474-760X
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Beschreibung
Zusammenfassung: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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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-023-02906-z