ArticleBMC bioinformatics2006
EVEREST: automatic identification and classification of protein domains in all protein sequences.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets.PLoS computational biology · 2022Article
- Density Peak clustering of protein sequences associated to a Pfam clan reveals clear similarities and interesting differences with respect to manual family annotation.BMC bioinformatics · 2021Article
- DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Alignment-free clustering of large data sets of unannotated protein conserved regions using minhashing.BMC bioinformatics · 2018Article
- ThreaDomEx: a unified platform for predicting continuous and discontinuous protein domains by multiple-threading and segment assembly.Nucleic acids research · 2017Article
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- A pluralistic account of homology: adapting the models to the data.Molecular biology and evolution · 2014Review
- ThreaDom: extracting protein domain boundary information from multiple threading alignments.Bioinformatics (Oxford, England) · 2013Article
- Functional inference by ProtoNet family tree: the uncharacterized proteome of Daphnia pulex.BMC bioinformatics · 2013Article
- Expansion of tandem repeats in sea anemone Nematostella vectensis proteome: A source for gene novelty?BMC genomics · 2009Article
- ProCKSI: a decision support system for Protein (structure) Comparison, Knowledge, Similarity and Information.BMC bioinformatics · 2007Article
- Growth of novel protein structural data.Proceedings of the National Academy of Sciences of the United States of America · 2007Article
- EVEREST: a collection of evolutionary conserved protein domains.Nucleic acids research · 2007Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundProteins are comprised of one or several building blocks, known as domains. Such domains can be classified into families according to their evolutionary origin. Whereas sequencing technologies have advanced immensely in recent years, there are no matching computational methodologies for large-scale determination of protein domains and their boundaries. We provide and rigorously evaluate a novel set of domain families that is automatically generated from sequence data. Our domain family identification process, called EVEREST (EVolutionary Ensembles of REcurrent SegmenTs), begins by constructing a library of protein segments that emerge in an all vs. all pairwise sequence comparison. It then proceeds to cluster these segments into putative domain families. The selection of the best putative families is done using machine learning techniques. A statistical model is then created for each of the chosen families. This procedure is then iterated: the aforementioned statistical models are used to scan all protein sequences, to recreate a library of segments and to cluster them again.
resultsProcessing the Swiss-Prot section of the UniProt Knoledgebase, release 7.2, EVEREST defines 20,230 domains, covering 85% of the amino acids of the Swiss-Prot database. EVEREST annotates 11,852 proteins (6% of the database) that are not annotated by Pfam A. In addition, in 43,086 proteins (20% of the database), EVEREST annotates a part of the protein that is not annotated by Pfam A. Performance tests show that EVEREST recovers 56% of Pfam A families and 63% of SCOP families with high accuracy, and suggests previously unknown domain families with at least 51% fidelity. EVEREST domains are often a combination of domains as defined by Pfam or SCOP and are frequently sub-domains of such domains.
conclusionThe EVEREST process and its output domain families provide an exhaustive and validated view of the protein domain world that is automatically generated from sequence data. The EVEREST library of domain families, accessible for browsing and download at 1, provides a complementary view to that provided by other existing libraries. Furthermore, since it is automatic, the EVEREST process is scalable and we will run it in the future on larger databases as well. The EVEREST source files are available for download from the EVEREST web site.
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