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

作者：Liubin Zhang, Yangyang Yuan, Wenjie Peng, Bin Tang, Mulin Jun Li, Mulin Jun Li, Hongsheng Gui, Qiang Wang, Miaoxin Li, Miaoxin Li · 发表于：Genome biology · 年份：2023 · DOI：10.1186/s13059-023-02906-z · 被引用次数：5 · 研究领域：Genomics and Phylogenetic Studies、Algorithms and Data Compression、Evolutionary Algorithms and Applications

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.