用于在 PBWT 中寻找 SMEM 的数据结构。

Paola Bonizzoni, Christina Boucher, Davide Cozzi, Travis Gagie, Dominik Köppl, Massimiliano Rossi
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引用次数: 0

摘要

位置 Burrows-Wheeler 变换(PBWT)是一种通过计算发散阵列在单倍型数据中寻找集最大精确匹配(SMEM)的方法。虽然之前已经考虑过对 PBWT 进行运行长度编码,但以压缩方式将发散阵列与 PBWT 一起存储的问题还没有得到严格研究。我们定义了两种可用于组合计算 SMEM 的查询方式,从而可以定义支持其中一种或两种查询方式的较小数据结构。我们将这些数据结构结合在一起,使 PBWT 和发散数组的存储方式允许查找 SMEM。我们估算并比较了这些数据结构的内存使用情况,最终得出了一种内存效率最高的数据结构。最后,我们实现了这种数据结构,并使用从 1000 基因组计划数据中提取的各种数据集将其性能与之前的方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Structures for SMEM-Finding in the PBWT.

The positional Burrows-Wheeler Transform (PBWT) was presented as a means to find set-maximal exact matches (SMEMs) in haplotype data via the computation of the divergence array. Although run-length encoding the PBWT has been previously considered, storing the divergence array along with the PBWT in a compressed manner has not been as rigorously studied. We define two queries that can be used in combination to compute SMEMs, allowing us to define smaller data structures that support one or both of these queries. We combine these data structures, enabling the PBWT and the divergence array to be stored in a manner that allows for finding SMEMs. We estimate and compare the memory usage of these data structures, leading to one data structure that is most memory efficient. Lastly, we implement this data structure and compare its performance to prior methods using various datasets taken from the 1000 Genomes Project data.

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