ArticleFrontiers in microbiology2020
Batch-Learning Self-Organizing Map Identifies Horizontal Gene Transfer Candidates and Their Origins in Entire Genomes.
Article in Frontiers in microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 14 citations in OpenAlex.
- Self-organizing maps for allele specific expression data reconstruction and identification of anomalous genomic regions.Frontiers in bioinformatics · 2026Article
- The functional small RNA interactome reveals targets for the vancomycin-responsive sRNA RsaOI in vancomycin-tolerantmSystems · 2024Article
- Comprehensive insights on environmental adaptation strategies in Antarctic bacteria and biotechnological applications of cold adapted molecules.Frontiers in microbiology · 2023Review
- Unsupervised explainable AI for molecular evolutionary study of forty thousand SARS-CoV-2 genomes.BMC microbiology · 2022Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Registered trials
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