ArticleNucleic acids research2018
MeShClust: an intelligent tool for clustering DNA sequences.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Del immune V and microbiome restructuring in colorectal cancer surgery: a randomized double blind placebo controlled trial.Frontiers in cellular and infection microbiology · 2026Trial
- Phased-genome assemblies of saltgrass reveal structural dynamics and genomic basis for extreme salt tolerance and dioecy.Plant communications · 2026Article
- Clustering methods for categorical time series and sequences : a scoping review.BMC medical research methodology · 2026Article
- Benchmarking the impact of reference genome selection on taxonomic profiling accuracy.BMC genomics · 2026Article
- A New Unsupervised Machine Learning Method for Metabarcoding Analysis Based on Selected Mock Community Data.Computational and structural biotechnology journal · 2026Article
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- CREATE: a novel attention-based framework for efficient classification of transposable elements.Briefings in bioinformatics · 2025Article
- De-Bruijn graph partitioning for scalable and accurate DNA storage processing.Bioinformatics (Oxford, England) · 2025Article
- De novo clustering of large long-read transcriptome datasets with isONclust3.Bioinformatics (Oxford, England) · 2025Article
- Article
- Pantheon-DNA: Versatile encoding-decoding system with integrated adaptive NGS preprocessing algorithms for DNA data storage.Computational and structural biotechnology journal · 2025Article
- CGRclust: Chaos Game Representation for twin contrastive clustering of unlabelled DNA sequences.BMC genomics · 2024Article
- GradHC: highly reliable gradual hash-based clustering for DNA storage systems.Bioinformatics (Oxford, England) · 2024Article
- Accurately clustering biological sequences in linear time by relatedness sorting.Nature communications · 2024Article
- Article
- HycDemux: a hybrid unsupervised approach for accurate barcoded sample demultiplexing in nanopore sequencing.Genome biology · 2023Article
- iDeLUCS: a deep learning interactive tool for alignment-free clustering of DNA sequences.Bioinformatics (Oxford, England) · 2023Article
- Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes.Journal of molecular evolution · 2023Review
- RabbitTClust: enabling fast clustering analysis of millions of bacteria genomes with MinHash sketches.Genome biology · 2023Article
- SCRAPT: an iterative algorithm for clustering large 16S rRNA gene data sets.Nucleic acids research · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Sequence clustering is a fundamental step in analyzing DNA sequences. Widely-used software tools for sequence clustering utilize greedy approaches that are not guaranteed to produce the best results. These tools are sensitive to one parameter that determines the similarity among sequences in a cluster. Often times, a biologist may not know the exact sequence similarity. Therefore, clusters produced by these tools do not likely match the real clusters comprising the data if the provided parameter is inaccurate. To overcome this limitation, we adapted the mean shift algorithm, an unsupervised machine-learning algorithm, which has been used successfully thousands of times in fields such as image processing and computer vision. The theory behind the mean shift algorithm, unlike the greedy approaches, guarantees convergence to the modes, e.g. cluster centers. Here we describe the first application of the mean shift algorithm to clustering DNA sequences. MeShClust is one of few applications of the mean shift algorithm in bioinformatics. Further, we applied supervised machine learning to predict the identity score produced by global alignment using alignment-free methods. We demonstrate MeShClust's ability to cluster DNA sequences with high accuracy even when the sequence similarity parameter provided by the user is not very accurate.
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