ArticleNature communications2018
Clustering huge protein sequence sets in linear time.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 484 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
484 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A human gut metagenome-assembled genome catalogue spanning 41 countries supports genome-scale metabolic models.Nature microbiology · 2026Pooled it
- Article
- Identification and characterization of a lipopolysaccharide binding, anti-inflammatory protein fromGut microbes · 2026Article
- Mechanistic interpretability of fine-tuned protein language models for nanobody thermostability prediction.Bioinformatics (Oxford, England) · 2026Article
- Genomic characterization of LaoThe Journal of general virology · 2026Article
- From neuropeptides to toxins: illuminating the origins of venom complexity in cone snails.Molecular biology and evolution · 2026Article
- PMAVP: A Mamba-Inspired Deep Learning Framework for Antiviral Peptide Identification and Functional Activity Prediction.International journal of molecular sciences · 2026Article
- ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences.Nature communications · 2026Article
- Article
- Multi-objective antibody design and optimization using machine learning.Nature reviews bioengineering · 2026Article
- A machine learning framework for predicting and modulating condition-dependent protein phase separation.Nature communications · 2026Article
- RNARL: reinforcement learning-driven unified generative framework for multi-objective RNA codon design.Genome biology · 2026Article
- Article
- Chronic stress unleashes an intratumor phage-fibroblast-B cell circuit to promote tumor growth.Cancer cell · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- PhyloNaP: a user-friendly database of phylogeny for natural product-producing enzymes.Bioinformatics (Oxford, England) · 2026Article
- Panorama: A robust pangenome-based method for predicting and comparing biological systems across species.PLoS computational biology · 2026Article
- Optimizing protein tokenization: reduced amino acid alphabets for efficient and accurate protein language models.Bioinformatics (Oxford, England) · 2026Article
- Analysis of phylogenetic signal in protein language model embeddings.Scientific reports · 2026Article
424 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metagenomic datasets contain billions of protein sequences that could greatly enhance large-scale functional annotation and structure prediction. Utilizing this enormous resource would require reducing its redundancy by similarity clustering. However, clustering hundreds of millions of sequences is impractical using current algorithms because their runtimes scale as the input set size N times the number of clusters K, which is typically of similar order as N, resulting in runtimes that increase almost quadratically with N. We developed Linclust, the first clustering algorithm whose runtime scales as N, independent of K. It can also cluster datasets several times larger than the available main memory. We cluster 1.6 billion metagenomic sequence fragments in 10 h on a single server to 50% sequence identity, >1000 times faster than has been possible before. Linclust will help to unlock the great wealth contained in metagenomic and genomic sequence databases.
Indexed as
Identifiers
What OpenQuestion holds
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.