μ- PBWT: a lightweight r-indexing of the PBWT for storing and querying UK Biobank data.

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Davide Cozzi, Massimiliano Rossi, Simone Rubinacci, Travis Gagie, Dominik Köppl, Christina Boucher, Paola Bonizzoni
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引用次数: 0

Abstract

Motivation: The Positional Burrows-Wheeler Transform (PBWT) is a data structure that indexes haplotype sequences in a manner that enables finding maximal haplotype matches in h sequences containing w variation sites in O(hw) time. This represents a significant improvement over classical quadratic-time approaches. However, the original PBWT data structure does not allow for queries over Biobank panels that consist of several millions of haplotypes, if an index of the haplotypes must be kept entirely in memory.

Results: In this article, we leverage the notion of r-index proposed for the BWT to present a memory-efficient method for constructing and storing the run-length encoded PBWT, and computing set maximal matches (SMEMs) queries in haplotype sequences. We implement our method, which we refer to as μ-PBWT, and evaluate it on datasets of 1000 Genome Project and UK Biobank data. Our experiments demonstrate that the μ-PBWT reduces the memory usage up to a factor of 20% compared to the best current PBWT-based indexing. In particular, μ-PBWT produces an index that stores high-coverage whole genome sequencing data of chromosome 20 in about a third of the space of its BCF file. μ-PBWT is an adaptation of techniques for the run-length compressed BWT for the PBWT (RLPBWT) and it is based on keeping in memory only a succinct representation of the RLPBWT that still allows the efficient computation of set maximal matches (SMEMs) over the original panel.

Availability and implementation: Our implementation is open source and available at https://github.com/dlcgold/muPBWT. The binary is available at https://bioconda.github.io/recipes/mupbwt/README.html.

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μ- PBWT:用于存储和查询UK Biobank数据的轻量级r索引PBWT。
动机:位置Burrows-Wheeler变换(PBWT)是一种数据结构,它以一种方式对单倍型序列进行索引,这种方式能够在O(hw)时间内找到包含w个变异位点的h个序列中的最大单倍型匹配。这代表了对经典二次时间方法的重大改进。然而,如果单体型的索引必须完全保存在内存中,那么原始的PBWT数据结构不允许对包含数百万单体型的Biobank面板进行查询。结果:在本文中,我们利用为BWT提出的r-index概念,提出了一种内存高效的方法来构建和存储运行长度编码的PBWT,并计算单倍型序列中的最大匹配集(SMEMs)查询。我们实现了我们的方法,我们称之为μ-PBWT,并在1000 Genome Project和UK Biobank数据集上进行了评估。我们的实验表明,与目前最好的基于pbwt的索引相比,μ-PBWT将内存使用减少了20%。特别是,μ-PBWT产生了一个索引,该索引将20号染色体的高覆盖率全基因组测序数据存储在其BCF文件约三分之一的空间中。μ-PBWT是对运行长度压缩的BWT (RLPBWT)技术的改进,它基于在内存中只保留RLPBWT的简洁表示,仍然允许在原始面板上有效地计算集最大匹配(SMEMs)。可用性和实现:我们的实现是开源的,可以在https://github.com/dlcgold/muPBWT上获得。二进制文件可从https://bioconda.github.io/recipes/mupbwt/README.html获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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