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SIMAP: the similarity matrix of proteins
Similarity Matrix of Proteins (SIMAP) () provides a database based on a pre-computed similarity matrix covering the similarity space formed by >4 million amino acid sequences from public databases and completely sequenced genomes. The database is capable of handling very large datasets and is upd...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2006
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1347468/ https://www.ncbi.nlm.nih.gov/pubmed/16381858 http://dx.doi.org/10.1093/nar/gkj106 |
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author | Rattei, Thomas Arnold, Roland Tischler, Patrick Lindner, Dominik Stümpflen, Volker Mewes, H. Werner |
author_facet | Rattei, Thomas Arnold, Roland Tischler, Patrick Lindner, Dominik Stümpflen, Volker Mewes, H. Werner |
author_sort | Rattei, Thomas |
collection | PubMed |
description | Similarity Matrix of Proteins (SIMAP) () provides a database based on a pre-computed similarity matrix covering the similarity space formed by >4 million amino acid sequences from public databases and completely sequenced genomes. The database is capable of handling very large datasets and is updated incrementally. For sequence similarity searches and pairwise alignments, we implemented a grid-enabled software system, which is based on FASTA heuristics and the Smith–Waterman algorithm. Our ProtInfo system allows querying by protein sequences covered by the SIMAP dataset as well as by fragments of these sequences, highly similar sequences and title words. Each sequence in the database is supplemented with pre-calculated features generated by detailed sequence analyses. By providing WWW interfaces as well as web-services, we offer the SIMAP resource as an efficient and comprehensive tool for sequence similarity searches. |
format | Text |
id | pubmed-1347468 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-13474682006-01-25 SIMAP: the similarity matrix of proteins Rattei, Thomas Arnold, Roland Tischler, Patrick Lindner, Dominik Stümpflen, Volker Mewes, H. Werner Nucleic Acids Res Article Similarity Matrix of Proteins (SIMAP) () provides a database based on a pre-computed similarity matrix covering the similarity space formed by >4 million amino acid sequences from public databases and completely sequenced genomes. The database is capable of handling very large datasets and is updated incrementally. For sequence similarity searches and pairwise alignments, we implemented a grid-enabled software system, which is based on FASTA heuristics and the Smith–Waterman algorithm. Our ProtInfo system allows querying by protein sequences covered by the SIMAP dataset as well as by fragments of these sequences, highly similar sequences and title words. Each sequence in the database is supplemented with pre-calculated features generated by detailed sequence analyses. By providing WWW interfaces as well as web-services, we offer the SIMAP resource as an efficient and comprehensive tool for sequence similarity searches. Oxford University Press 2006-01-01 2005-12-28 /pmc/articles/PMC1347468/ /pubmed/16381858 http://dx.doi.org/10.1093/nar/gkj106 Text en © The Author 2006. Published by Oxford University Press. All rights reserved |
spellingShingle | Article Rattei, Thomas Arnold, Roland Tischler, Patrick Lindner, Dominik Stümpflen, Volker Mewes, H. Werner SIMAP: the similarity matrix of proteins |
title | SIMAP: the similarity matrix of proteins |
title_full | SIMAP: the similarity matrix of proteins |
title_fullStr | SIMAP: the similarity matrix of proteins |
title_full_unstemmed | SIMAP: the similarity matrix of proteins |
title_short | SIMAP: the similarity matrix of proteins |
title_sort | simap: the similarity matrix of proteins |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1347468/ https://www.ncbi.nlm.nih.gov/pubmed/16381858 http://dx.doi.org/10.1093/nar/gkj106 |
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