ArticleNature communications2024
Accurately clustering biological sequences in linear time by relatedness sorting.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The metabolic and anatomical complexity of root microhabitats modulate their interaction with the microbiota.Nature communications · 2026Article
- Phylogenize2: robust phylogenetic methods link genes to phenotypes across host-associated and environmental microbiomes.bioRxiv : the preprint server for biology · 2026Article
- Leveraging microbial phylogeny for computational efficiency.Nature reviews. Microbiology · 2026Article
- From spacecraft ranging to massive DNA data storage: Composite ranging codes as indices and error correction references.Science advances · 2026Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Control of gene output by intron RNA structure.bioRxiv : the preprint server for biology · 2025Article
- Enzymatic carbon-fluorine bond cleavage by human gut microbes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Tandem Repeats Provide Evidence for Convergent Evolution to Similar Protein Structures.Genome biology and evolution · 2025Article
- Essential nucleic acid omics: a theoretical foundation for early-stage users.Frontiers in bioinformatics · 2025Review
- Many purported pseudogenes in bacterial genomes are bona fide genes.BMC genomics · 2024Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Clustering biological sequences into similar groups is an increasingly important task as the number of available sequences continues to grow exponentially. Search-based approaches to clustering scale super-linearly with the number of input sequences, making it impractical to cluster very large sets of sequences. Approaches to clustering sequences in linear time currently lack the accuracy of super-linear approaches. Here, I set out to develop and characterize a strategy for clustering with linear time complexity that retains the accuracy of less scalable approaches. The resulting algorithm, named Clusterize, sorts sequences by relatedness to linearize the clustering problem. Clusterize produces clusters with accuracy rivaling popular programs (CD-HIT, MMseqs2, and UCLUST) but exhibits linear asymptotic scalability. Clusterize generates higher accuracy and oftentimes much larger clusters than Linclust, a fast linear time clustering algorithm. I demonstrate the utility of Clusterize by accurately solving different clustering problems involving millions of nucleotide or protein sequences.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.