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Fast practical online exact single and multiple pattern matching algorithms in highly similar sequences

Authors :
Thierry Lecroq
Nadia Ben Nsira
Élise Prieur-Gaston
Equipe Traitement de l'information en Biologie Santé (TIBS - LITIS)
Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS)
Université Le Havre Normandie (ULH)
Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN)
Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie)
Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH)
Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)
Source :
International Journal of Data Mining and Bioinformatics, International Journal of Data Mining and Bioinformatics, Inderscience, 2019, 22 (1), pp.1. ⟨10.1504/IJDMB.2019.099285⟩
Publication Year :
2019
Publisher :
Inderscience Publishers, 2019.

Abstract

International audience; With the advent of high-throughput sequencing technologies there are more and more genomic sequences of individuals of the same species available. These sequences only differ by a very small amount of variations. There is thus a strong need for efficient algorithms for performing fast pattern matching in such specific sets of sequences. In this paper, we propose efficient practical algorithms that solve on-line exact pattern matching problem in a set of highly similar DNA sequences. We first present a method for exact single pattern matching when k variations are allowed in a window which size is equal to the pattern length. We then propose an algorithm for exact multiple pattern matching when only one variation is allowed in a window which size is equal to the length of the longest pattern. Experimental results show that our algorithms, though not optimal in the worst case, have good performances in practice.

Details

ISSN :
17485681 and 17485673
Volume :
22
Database :
OpenAIRE
Journal :
International Journal of Data Mining and Bioinformatics
Accession number :
edsair.doi.dedup.....1b430b199f0b7de053e12b654cef8fa0
Full Text :
https://doi.org/10.1504/ijdmb.2019.10020726