Batch-Learning Self-Organizing Map Identifies Horizontal Gene Transfer Candidates and Their Origins in Entire Genomes.
Antarctic environment
Batch-Learning Self-Organizing Map
Sphingomonas genome
amino acid frequency
convergent evolution
horizontal gene transfer
low-temperature adaptation
oligonucleotide
Journal
Frontiers in microbiology
ISSN: 1664-302X
Titre abrégé: Front Microbiol
Pays: Switzerland
ID NLM: 101548977
Informations de publication
Date de publication:
2020
2020
Historique:
received:
05
02
2020
accepted:
08
06
2020
entrez:
29
7
2020
pubmed:
29
7
2020
medline:
29
7
2020
Statut:
epublish
Résumé
Horizontal gene transfer (HGT) has been widely suggested to play a critical role in the environmental adaptation of microbes; however, the number and origin of the genes in microbial genomes obtained through HGT remain unknown as the frequency of detected HGT events is generally underestimated, particularly in the absence of information on donor sequences. As an alternative to phylogeny-based methods that rely on sequence alignments, we have developed an alignment-free clustering method on the basis of an unsupervised neural network "Batch-Learning Self-Organizing Map (BLSOM)" in which sequence fragments are clustered based solely on oligonucleotide similarity without taxonomical information, to detect HGT candidates and their origin in entire genomes. By mapping the microbial genomic sequences on large-scale BLSOMs constructed with nearly all prokaryotic genomes, HGT candidates can be identified, and their origin assigned comprehensively, even for microbial genomes that exhibit high novelty. By focusing on two types of
Identifiants
pubmed: 32719664
doi: 10.3389/fmicb.2020.01486
pmc: PMC7350273
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1486Informations de copyright
Copyright © 2020 Abe, Akazawa, Toyoda, Niki and Baba.
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