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Updated: Apr 20, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
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UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

Baris E Suzek1, Yuqi Wang2, Hongzhan Huang2

  • 1Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla 48000, Turkey, Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla 48000, Turkey, Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland.

Bioinformatics (Oxford, England)
|November 16, 2014
PubMed
Summary

UniRef clusters, enhanced for non-redundancy, improve protein similarity searches. These clusters offer faster, more sensitive similarity detection and reliable functional annotation, outperforming traditional sequence databases.

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