ArticleGenome research2003
Informatics for unveiling hidden genome signatures.
Article in Genome research, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.
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Who cites it
91 citing papers in PubMed.
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- Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction.Briefings in bioinformatics · 2022Article
- Binning long reads in metagenomics datasets using composition and coverage information.Algorithms for molecular biology : AMB · 2022Article
- Comparative genomic analysis of the human genome and six bat genomes using unsupervised machine learning: Mb-level CpG and TFBS islands.BMC genomics · 2022Article
- Unsupervised explainable AI for molecular evolutionary study of forty thousand SARS-CoV-2 genomes.BMC microbiology · 2022Article
- AI-based search for convergently expanding, advantageous mutations in SARS-CoV-2 by focusing on oligonucleotide frequencies.PloS one · 2022Article
- Skotomorphogenesis exploits threonine to promote hypocotyl elongation.Quantitative plant biology · 2022Article
- Comparative genomics ofLife science alliance · 2021Article
- Human cell-dependent, directional, time-dependent changes in the mono- and oligonucleotide compositions of SARS-CoV-2 genomes.BMC microbiology · 2021Article
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- MetaBCC-LR: metagenomics binning by coverage and composition for long reads.Bioinformatics (Oxford, England) · 2020Article
- A completeness-independent method for pre-selection of closely related genomes for species delineation in prokaryotes.BMC genomics · 2020Article
- Batch-Learning Self-Organizing Map Identifies Horizontal Gene Transfer Candidates and Their Origins in Entire Genomes.Frontiers in microbiology · 2020Article
- A high-resolution genomic composition-based method with the ability to distinguish similar bacterial organisms.BMC genomics · 2019Article
- Viral population analysis of the taiga tick, Ixodes persulcatus, by using Batch Learning Self-Organizing Maps and BLAST search.The Journal of veterinary medical science · 2019Article
- Genomic Signatures Among Acanthamoeba polyphaga Entoorganisms Unveil Evidence of Coevolution.Journal of molecular evolution · 2019Article
- PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures.Nucleic acids research · 2018Article
31 more citing papers are in PubMed but not listed here.
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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the increasing amount of available genome sequences, novel tools are needed for comprehensive analysis of species-specific sequence characteristics for a wide variety of genomes. We used an unsupervised neural network algorithm, a self-organizing map (SOM), to analyze di-, tri-, and tetranucleotide frequencies in a wide variety of prokaryotic and eukaryotic genomes. The SOM, which can cluster complex data efficiently, was shown to be an excellent tool for analyzing global characteristics of genome sequences and for revealing key combinations of oligonucleotides representing individual genomes. From analysis of 1- and 10-kb genomic sequences derived from 65 bacteria (a total of 170 Mb) and from 6 eukaryotes (460 Mb), clear species-specific separations of major portions of the sequences were obtained with the di-, tri-, and tetranucleotide SOMs. The unsupervised algorithm could recognize, in most 10-kb sequences, the species-specific characteristics (key combinations of oligonucleotide frequencies) that are signature features of each genome. We were able to classify DNA sequences within one and between many species into subgroups that corresponded generally to biological categories. Because the classification power is very high, the SOM is an efficient and fundamental bioinformatic strategy for extracting a wide range of genomic information from a vast amount of sequences.
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