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Finding All Maximal Perfect Haplotype Blocks in Linear Time
Authors
Jarno Alanko 
,
Hideo Bannai ,
Bastien Cazaux ,
Pierre Peterlongo ,
Jens Stoye
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19th International Workshop on Algorithms in Bioinformatics (WABI 2019)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Workshop on Algorithms in Bioinformatics (WABI) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2019-09-03
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Acknowledgements
We thank the organizers of DSB 2019 (dsb2019.gitlab.io) for giving us the opportunity to present earlier work in this area and start a discussion from which the present results originated. We would also like to thank Michel T. Henrichs for providing a script to convert VCF files to haplotype matrices and for assisting with the production of Figure 3.
Cite As Get BibTex
Jarno Alanko, Hideo Bannai, Bastien Cazaux, Pierre Peterlongo, and Jens Stoye. Finding All Maximal Perfect Haplotype Blocks in Linear Time. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 8:1-8:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.WABI.2019.8
Abstract
Recent large-scale community sequencing efforts allow at an unprecedented level of detail the identification of genomic regions that show signatures of natural selection. Traditional methods for identifying such regions from individuals' haplotype data, however, require excessive computing times and therefore are not applicable to current datasets. In 2019, Cunha et al. (Proceedings of BSB 2019) suggested the maximal perfect haplotype block as a very simple combinatorial pattern, forming the basis of a new method to perform rapid genome-wide selection scans. The algorithm they presented for identifying these blocks, however, had a worst-case running time quadratic in the genome length. It was posed as an open problem whether an optimal, linear-time algorithm exists. In this paper we give two algorithms that achieve this time bound, one conceptually very simple one using suffix trees and a second one using the positional Burrows-Wheeler Transform, that is very efficient also in practice.
Subject Classification
ACM Subject Classification
- Theory of computation → Pattern matching
- Applied computing → Bioinformatics
- Mathematics of computing → Combinatorial algorithms
- Applied computing → Computational genomics
Keywords
- Population genomics
- selection coefficient
- haplotype block
- positional Burrows-Wheeler Transform
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References
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