UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches | Zendy

Barış Ethem Süzek | Zendy; Yuqi Wang | Zendy; Hongzhan Huang | Zendy; Peter B. McGarvey | Zendy; Cathy Wu | Zendy

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UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

Author(s) -

Barış Ethem Süzek,

Yuqi Wang,

Hongzhan Huang,

Peter B. McGarvey,

Cathy Wu

Publication year - 2014

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btu739

Subject(s) - annotation , uniprot , computer science , scalability , consistency (knowledge bases) , cluster analysis , similarity (geometry) , data mining , levenshtein distance , gene ontology , precision and recall , information retrieval , artificial intelligence , biology , database , genetics , gene expression , image (mathematics) , gene

UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters.

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