ArticleBMC bioinformatics2013
kClust: fast and sensitive clustering of large protein sequence databases.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed.
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- Evolution of Plasmid Mobility: Origin and Fate of Conjugative and Nonconjugative Plasmids.Molecular biology and evolution · 2022Article
- Genomic diversity of bacteriophages infecting Rhodobacter capsulatus and their relatedness to its gene transfer agent RcGTA.PloS one · 2021Article
- DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins.Bioinformatics (Oxford, England) · 2020Article
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- Measuring the impact of gene prediction on gene loss estimates in Eukaryotes by quantifying falsely inferred absences.PLoS computational biology · 2019Article
- 1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses.Nature biotechnology · 2019Article
- A Novel, Highly Related Jumbo Family of Bacteriophages That Were Isolated AgainstFrontiers in microbiology · 2019Article
- Characterization and induction of prophages in human gut-associated Bifidobacterium hosts.Scientific reports · 2018Article
- De novo profile generation based on sequence context specificity with the long short-term memory network.BMC bioinformatics · 2018Article
- Clustering huge protein sequence sets in linear time.Nature communications · 2018Article
- Choosing non-redundant representative subsets of protein sequence data sets using submodular optimization.Proteins · 2018Article
- Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundFueled by rapid progress in high-throughput sequencing, the size of public sequence databases doubles every two years. Searching the ever larger and more redundant databases is getting increasingly inefficient. Clustering can help to organize sequences into homologous and functionally similar groups and can improve the speed, sensitivity, and readability of homology searches. However, because the clustering time is quadratic in the number of sequences, standard sequence search methods are becoming impracticable.
resultsHere we present a method to cluster large protein sequence databases such as UniProt within days down to 20%-30% maximum pairwise sequence identity. kClust owes its speed and sensitivity to an alignment-free prefilter that calculates the cumulative score of all similar 6-mers between pairs of sequences, and to a dynamic programming algorithm that operates on pairs of similar 4-mers. To increase sensitivity further, kClust can run in profile-sequence comparison mode, with profiles computed from the clusters of a previous kClust iteration. kClust is two to three orders of magnitude faster than clustering based on NCBI BLAST, and on multidomain sequences of 20%-30% maximum pairwise sequence identity it achieves comparable sensitivity and a lower false discovery rate. It also compares favorably to CD-HIT and UCLUST in terms of false discovery rate, sensitivity, and speed.
conclusionskClust fills the need for a fast, sensitive, and accurate tool to cluster large protein sequence databases to below 30% sequence identity. kClust is freely available under GPL at http://toolkit.lmb.uni-muenchen.de/pub/kClust/.
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