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SIMAP--the database of all-against-all protein sequence similarities and annotations with new interfaces and increased coverage.

Authors :
Arnold R
Goldenberg F
Mewes HW
Rattei T
Source :
Nucleic acids research [Nucleic Acids Res] 2014 Jan; Vol. 42 (Database issue), pp. D279-84. Date of Electronic Publication: 2013 Oct 27.
Publication Year :
2014

Abstract

The Similarity Matrix of Proteins (SIMAP, http://mips.gsf.de/simap/) database has been designed to massively accelerate computationally expensive protein sequence analysis tasks in bioinformatics. It provides pre-calculated sequence similarities interconnecting the entire known protein sequence universe, complemented by pre-calculated protein features and domains, similarity clusters and functional annotations. SIMAP covers all major public protein databases as well as many consistently re-annotated metagenomes from different repositories. As of September 2013, SIMAP contains >163 million proteins corresponding to ∼70 million non-redundant sequences. SIMAP uses the sensitive FASTA search heuristics, the Smith-Waterman alignment algorithm, the InterPro database of protein domain models and the BLAST2GO functional annotation algorithm. SIMAP assists biologists by facilitating the interactive exploration of the protein sequence universe. Web-Service and DAS interfaces allow connecting SIMAP with any other bioinformatic tool and resource. All-against-all protein sequence similarity matrices of project-specific protein collections are generated on request. Recent improvements allow SIMAP to cover the rapidly growing sequenced protein sequence universe. New Web-Service interfaces enhance the connectivity of SIMAP. Novel tools for interactive extraction of protein similarity networks have been added. Open access to SIMAP is provided through the web portal; the portal also contains instructions and links for software access and flat file downloads.

Details

Language :
English
ISSN :
1362-4962
Volume :
42
Issue :
Database issue
Database :
MEDLINE
Journal :
Nucleic acids research
Publication Type :
Academic Journal
Accession number :
24165881
Full Text :
https://doi.org/10.1093/nar/gkt970