Journal Article

CGRclust: Chaos Game Representation for twin contrastive clustering of unlabelled DNA sequences

Alipour, Fatemeh, Kari, Lila, Hill, Kathleen A.

2024

Overview

Background Traditional supervised learning methods applied to DNA sequence taxonomic classification rely on the labor-intensive and time-consuming step of labelling the primary DNA sequences. Additionally, standard DNA classification/clustering methods involve time-intensive multiple sequence alignments, which impacts their applicability to large genomic datasets or distantly related organisms. These limitations indicate a need for robust, efficient, and scalable unsupervised DNA sequence clustering methods that do not depend on sequence labels or alignment. Results This study proposes CGRclust, a novel combination of unsupervised twin contrastive clustering of Chaos Game Representations (CGR) of DNA sequences, with convolutional neural networks (CNNs). To the best of our knowledge, CGRclust is the first method to use unsupervised learning for image classification (herein applied to two-dimensional CGR images) for clustering datasets of DNA sequences. CGRclust overcomes the limitations of traditional sequence classification methods by leveraging unsupervised twin contrastive learning to detect distinctive sequence patterns, without requiring DNA sequence alignment or biological/taxonomic labels. CGRclust accurately clustered twenty-five diverse datasets, with sequence lengths ranging from 664 bp to 100 kbp, including mitochondrial genomes of fish, fungi, and protists, as well as viral whole genome assemblies and synthetic DNA sequences. Compared with three recent clustering methods for DNA sequences (DeLUCS, i DeLUCS, and MeShClust v3.0.), CGRclust is the only method that surpasses 81.70% accuracy across all four taxonomic levels tested for mitochondrial DNA genomes of fish. Moreover, CGRclust also consistently demonstrates superior performance across all the viral genomic datasets. The high clustering accuracy of CGRclust on these twenty-five datasets, which vary significantly in terms of sequence length, number of genomes, number of clusters, and level of taxonomy, demonstrates its robustness, scalability, and versatility. Conclusion CGRclust is a novel, scalable, alignment-free DNA sequence clustering method that uses CGR images of DNA sequences and CNNs for twin contrastive clustering of unlabelled primary DNA sequences, achieving superior or comparable accuracy and performance over current approaches. CGRclust demonstrated enhanced reliability, by consistently achieving over 80% accuracy in more than 90% of the datasets analyzed. In particular, CGRclust performed especially well in clustering viral DNA datasets, where it consistently outperformed all competing methods.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Alignment / Alignment-free DNA sequence comparison / Animal Genetics and Genomics / Animals / Artificial neural networks / Biomedical and Life Sciences / Chaos Game Representation (CGR) / Classification / Cluster Analysis / Clustering / Computational biology / Computational Biology - methods / Datasets / Deoxyribonucleic acid / DNA / DNA sequence clustering / Fish / Gene sequencing / Genetic research / Genome, Mitochondrial / Genome, Viral / Genomes / Genomics / Identification and classification / Image classification / Information management / Labels / Learning / Life Sciences / Machine learning / Methods / Microarrays / Microbial Genetics and Genomics / Mitochondrial DNA / Neural networks / Neural Networks, Computer / Nucleotide sequence / Plant Genetics and Genomics / Proteomics / Representations / Sequence Analysis, DNA - methods / Supervised learning / Taxonomic classification / Taxonomy / Twin contrastive learning / Unsupervised learning / Unsupervised Machine Learning