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Arnold, R.* ; Goldenberg, F.* ; Mewes, H.-W. ; Rattei, T.*

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

Nucleic Acids Res. 42, D279-D284 (2014)
Verlagsversion Volltext DOI
Open Access Gold
Creative Commons Lizenzvertrag
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 similar 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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Substitution Matrices; Visualization; Networks; Construction; Generation; Clusters; Genomes; Family; Tool
Sprache englisch
Veröffentlichungsjahr 2014
HGF-Berichtsjahr 2014
ISSN (print) / ISBN 0305-1048
e-ISSN 1362-4962
Quellenangaben Band: 42, Heft: D1, Seiten: D279-D284 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Begutachtungsstatus Peer reviewed
POF Topic(s) 30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503700-001
Erfassungsdatum 2014-03-24