Enabling efficient analysis of biobank-scale data with genotype representation graphs

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Drew DeHaas, Ziqing Pan, Xinzhu Wei
{"title":"Enabling efficient analysis of biobank-scale data with genotype representation graphs","authors":"Drew DeHaas, Ziqing Pan, Xinzhu Wei","doi":"10.1038/s43588-024-00739-9","DOIUrl":null,"url":null,"abstract":"Computational analysis of a large number of genomes requires a data structure that can represent the dataset compactly while also enabling efficient operations on variants and samples. However, encoding genetic data in existing tabular data structures and file formats has become costly and unsustainable. Here we introduce the genotype representation graph (GRG), a fully connected hierarchical data structure that losslessly encodes phased whole-genome polymorphisms. Exploiting variant-sharing across samples enables GRG to compress 200,000 UK Biobank phased human genomes to 5–26 gigabytes per chromosome, also enabling graph-traversal algorithms to reuse computed values in random access memory. Constructing and processing GRG files scales to a million whole genomes. Using allele frequencies and association effects as examples, we show that computation on GRG via graph traversal runs the fastest among all tested alternatives. GRG-based algorithms have the potential to increase the scalability and reduce the cost of analyzing large genomic datasets. The genotype representation graph (GRG) is a compact data structure that encodes 200,000 human genomes in just 5–26 gigabytes per chromosome. Computation on GRG via graph traversal greatly accelerates genome-wide analysis.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"112-124"},"PeriodicalIF":12.0000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-024-00739-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Computational analysis of a large number of genomes requires a data structure that can represent the dataset compactly while also enabling efficient operations on variants and samples. However, encoding genetic data in existing tabular data structures and file formats has become costly and unsustainable. Here we introduce the genotype representation graph (GRG), a fully connected hierarchical data structure that losslessly encodes phased whole-genome polymorphisms. Exploiting variant-sharing across samples enables GRG to compress 200,000 UK Biobank phased human genomes to 5–26 gigabytes per chromosome, also enabling graph-traversal algorithms to reuse computed values in random access memory. Constructing and processing GRG files scales to a million whole genomes. Using allele frequencies and association effects as examples, we show that computation on GRG via graph traversal runs the fastest among all tested alternatives. GRG-based algorithms have the potential to increase the scalability and reduce the cost of analyzing large genomic datasets. The genotype representation graph (GRG) is a compact data structure that encodes 200,000 human genomes in just 5–26 gigabytes per chromosome. Computation on GRG via graph traversal greatly accelerates genome-wide analysis.

Abstract Image

通过基因型表示图实现生物库规模数据的有效分析。
大量基因组的计算分析需要一种能够紧凑地表示数据集的数据结构,同时还能够对变体和样本进行有效的操作。然而,在现有的表格数据结构和文件格式中编码遗传数据已经变得昂贵且不可持续。在这里,我们介绍了基因型表示图(GRG),这是一种完全连接的分层数据结构,可以无损地编码分阶段的全基因组多态性。利用样本间的变异共享,GRG可以将20万个英国生物银行分阶段的人类基因组压缩到每条染色体5-26千兆字节,还可以使图遍历算法在随机访问存储器中重用计算值。构建和处理GRG文件可以扩展到一百万个完整基因组。以等位基因频率和关联效应为例,我们表明通过图遍历在GRG上的计算在所有测试的替代方案中运行最快。基于grg的算法具有提高可扩展性和降低分析大型基因组数据集成本的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
11.70
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信