ArticlePloS one
DeLUCS: Deep learning for unsupervised clustering of DNA sequences.
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24 citing papers in PubMed, 0 citations in OpenAlex.
- Machine Learning Classification of Prostate Cancer Genomic Sequences Using K-Mer and Sequence-Derived Features.Computational molecular bioscience · 2026Article
- Clustering methods for categorical time series and sequences : a scoping review.BMC medical research methodology · 2026Article
- Harnessing Deep Learning in Searching Wild Relatives of Domestic Animals.Molecular ecology resources · 2026Article
- Life at the extremes: maximally divergent microbes with similar genomic signatures linked to extreme environments.NAR genomics and bioinformatics · 2025Article
- Review
- Introducing a foundational sequence transformer for range adaptive nucleotide decoding (STRAND).Briefings in bioinformatics · 2025Article
- A composite universal DNA signature for the tree of life.Nature ecology & evolution · 2025Article
- A curated benchmark dataset for molecular identification based on genome skimming.Scientific data · 2025Article
- PCVR: a pre-trained contextualized visual representation for DNA sequence classification.BMC bioinformatics · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- Artificial Intelligence-Enhanced Analysis of Genomic DNA Visualized with Nanoparticle-Tagged Peptides under Electron Microscopy.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- CGRclust: Chaos Game Representation for twin contrastive clustering of unlabelled DNA sequences.BMC genomics · 2024Article
- Genome analysis through image processing with deep learning models.Journal of human genetics · 2024Review
- PlasmidEC and gplas2: an optimized short-read approach to predict and reconstruct antibiotic resistance plasmids inMicrobial genomics · 2024Article
- A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study.Briefings in bioinformatics · 2023Article
- Environment and taxonomy shape the genomic signature of prokaryotic extremophiles.Scientific reports · 2023Article
- iDeLUCS: a deep learning interactive tool for alignment-free clustering of DNA sequences.Bioinformatics (Oxford, England) · 2023Article
- Deep Transfer Learning with Enhanced Feature Fusion for Detection of Abnormalities in X-ray Images.Cancers · 2023Article
- Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes.Journal of molecular evolution · 2023Review
- Genomic Signature in Evolutionary Biology: A Review.Biology · 2023Review
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of primary DNA sequences, and generates "mimic" sequence FCGRs to self-learn data patterns (genomic signatures) through the optimization of multiple neural networks. A majority voting scheme is then used to determine the final cluster assignment for each sequence. The clusters learned by DeLUCS match true taxonomic groups for large and diverse datasets, with accuracies ranging from 77% to 100%: 2,500 complete vertebrate mitochondrial genomes, at taxonomic levels from sub-phylum to genera; 3,200 randomly selected 400 kbp-long bacterial genome segments, into clusters corresponding to bacterial families; three viral genome and gene datasets, averaging 1,300 sequences each, into clusters corresponding to virus subtypes. DeLUCS significantly outperforms two classic clustering methods (K-means++ and Gaussian Mixture Models) for unlabelled data, by as much as 47%. DeLUCS is highly effective, it is able to cluster datasets of unlabelled primary DNA sequences totalling over 1 billion bp of data, and it bypasses common limitations to classification resulting from the lack of sequence homology, variation in sequence length, and the absence or instability of sequence annotations and taxonomic identifiers. Thus, DeLUCS offers fast and accurate DNA sequence clustering for previously intractable datasets.
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