Abstract

Abstract Motivation: The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping viruses based on the similarity of their proteins at proteome scale can normalize against potential taxonomic nomenclature anomalies. Results: We present Viral Reference Proteomes (Viral RPs), which are computed from complete virus proteomes within UniProtKB. Viral RPs based on 95, 75, 55, 35 and 15% co-membership in proteome similarity based clusters are provided. Comparison of our computational Viral RPs with UniProt’s curator-selected Reference Proteomes indicates that the two sets are consistent and complementary. Furthermore, each Viral RP represents a cluster of virus proteomes that was consistent with virus or host taxonomy. We provide BLASTP search and FTP download of Viral RP protein sequences, and a browser to facilitate the visualization of Viral RPs. Availability and implementation: http://proteininformationresource.org/rps/viruses/ Contact: chenc@udel.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Keywords

UniProtProteomeComputational biologyBiologyGenomeCluster analysisBioinformaticsComputer scienceGeneticsArtificial intelligence

MeSH Terms

Amino Acid SequenceCluster AnalysisComputational BiologyDatabasesProteinKnowledge BasesProteomeViral Proteins

Affiliated Institutions

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Publication Info

Year
2016
Type
article
Volume
32
Issue
13
Pages
2041-2043
Citations
4
Access
Closed

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Cite This

Chuming Chen, Hongzhan Huang, Raja Mazumder et al. (2016). Computational clustering for viral reference proteomes. Bioinformatics , 32 (13) , 2041-2043. https://doi.org/10.1093/bioinformatics/btw110

Identifiers

DOI
10.1093/bioinformatics/btw110
PMID
27153712
PMCID
PMC4920120

Data Quality

Data completeness: 90%