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DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets
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DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets
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DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets
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Journal Article
DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets
2022
Overview
Proteins that are known only at a sequence level outnumber those with an experimental characterization by orders of magnitude. Classifying protein regions (domains) into homologous families can generate testable functional hypotheses for yet unannotated sequences. Existing domain family resources typically use at least some degree of manual curation: they grow slowly over time and leave a large fraction of the protein sequence space unclassified. We here describe automatic clustering by Density Peak Clustering of UniRef50 v. 2017_07, a protein sequence database including approximately 23M sequences. We performed a radical re-implementation of a pipeline we previously developed in order to allow handling millions of sequences and data volumes of the order of 3 TeraBytes. The modified pipeline, which we call DPCfam, finds ∼ 45,000 protein clusters in UniRef50. Our automatic classification is in close correspondence to the ones of the Pfam and ECOD resources: in particular, about 81% of medium-large Pfam families and 72% of ECOD families can be mapped to clusters generated by DPCfam. In addition, our protocol finds more than 14,000 clusters constituted of protein regions with no Pfam annotation, which are therefore candidates for representing novel protein families. These results are made available to the scientific community through a dedicated repository.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
