FAME: fast and memory efficient multiple sequences alignment tool through compatible chain of roots.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
01 06 2020
Historique:
received: 25 10 2019
revised: 10 02 2020
accepted: 12 03 2020
pubmed: 15 3 2020
medline: 29 12 2020
entrez: 15 3 2020
Statut: ppublish

Résumé

Multiple sequence alignment (MSA) is important and challenging problem of computational biology. Most of the existing methods can only provide a short length multiple alignments in an acceptable time. Nevertheless, when the researchers confront the genome size in the multiple alignments, the process has required a huge processing space/time. Accordingly, using the method that can align genome size rapidly and precisely has a great effect, especially on the analysis of the very long alignments. Herein, we have proposed an efficient method, called FAME, which vertically divides sequences from the places that they have common areas; then they are arranged in consecutive order. Then these common areas are shifted and placed under each other, and the subsequences between them are aligned using any existing MSA tool. The results demonstrate that the combination of FAME and the MSA methods and deploying minimizer are capable to be executed on personal computer and finely align long length sequences with much higher sum-of-pair (SP) score compared to the standalone MSA tools. As we select genomic datasets with longer length, the SP score of the combinatorial methods is gradually improved. The calculated computational complexity of methods supports the results in a way that combining FAME and the MSA tools leads to at least four times faster execution on the datasets. The source code and all datasets and run-parameters are accessible free on http://github.com/naznoosh/msa. Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 32170927
pii: 5805384
doi: 10.1093/bioinformatics/btaa175
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3662-3668

Informations de copyright

© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Etminan Naznooshsadat (E)

Department of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.

Parvinnia Elham (P)

Department of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.

Sharifi-Zarchi Ali (SZ)

Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

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Classifications MeSH