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

• Published in: Genome Biology Vol. 24; no. 1; p. 76
• Main Authors: Zhang, Liubin, Yuan, Yangyang, Peng, Wenjie, Tang, Bin, Li, Mulin Jun, Gui, Hongsheng, Wang, Qiang, Li, Miaoxin
• Format: Journal Article
• Language: English
• Published: London BioMed Central 17.04.2023; Springer Nature B.V; BMC
• Subjects: 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
• DOI: 10.1186/s13059-023-02906-z

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.