Finding all maximal perfect haplotype blocks in linear time

J. Alanko, H. Bannai, B. Cazaux, Peter Peterlongo, J. Stoye

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKapitelVetenskaplig

Sammanfattning

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. © Jarno N. Alanko, Hideo Bannai, Bastien Cazaux, Pierre Peterlongo, and Jens Stoye; licensed under Creative Commons License CC-BY
Originalspråkengelska
Titel på gästpublikation19th International Workshop on Algorithms in Bioinformatics (WABI 2019)
RedaktörerKatharina T. Huber, Dan Gusfield
Antal sidor9
UtgivningsortDagstuhl
FörlagSchloss Dagstuhl - Leibniz-Zentrum für Informatik
Utgivningsdatum2019
ISBN (elektroniskt)978-3-95977-123-8
DOI
StatusPublicerad - 2019
MoE-publikationstypEj behörig
EvenemangInternational Workshop on Algorithms in Bioinformatics - Niagara Falls, Förenta Staterna (USA)
Varaktighet: 8 sep 201910 sep 2019
Konferensnummer: 19
http://acm-bcb.org/WABI/2019/

Publikationsserier

NamnLeibniz International Proceedings in Informatics (LIPIcs)
Volym143
ISSN (elektroniskt)1868-8969

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap
  • 1182 Biokemi, cell- och molekylärbiologi

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