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SIMAP—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters
The prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the simi...
Autores principales: | , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2808863/ https://www.ncbi.nlm.nih.gov/pubmed/19906725 http://dx.doi.org/10.1093/nar/gkp949 |
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author | Rattei, Thomas Tischler, Patrick Götz, Stefan Jehl, Marc-André Hoser, Jonathan Arnold, Roland Conesa, Ana Mewes, Hans-Werner |
author_facet | Rattei, Thomas Tischler, Patrick Götz, Stefan Jehl, Marc-André Hoser, Jonathan Arnold, Roland Conesa, Ana Mewes, Hans-Werner |
author_sort | Rattei, Thomas |
collection | PubMed |
description | The prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the similarity matrix, the computation of the Similarity Matrix of Proteins (SIMAP) becomes a computational intensive task. The SIMAP database provides a comprehensive and up-to-date pre-calculation of the protein sequence similarity matrix, sequence-based features and sequence clusters. As of September 2009, SIMAP covers 48 million proteins and more than 23 million non-redundant sequences. Novel features of SIMAP include the expansion of the sequence space by including databases such as ENSEMBL as well as the integration of metagenomes based on their consistent processing and annotation. Furthermore, protein function predictions by Blast2GO are pre-calculated for all sequences in SIMAP and the data access and query functions have been improved. SIMAP assists biologists to query the up-to-date sequence space systematically and facilitates large-scale downstream projects in computational biology. Access to SIMAP is freely provided through the web portal for individuals (http://mips.gsf.de/simap/) and for programmatic access through DAS (http://webclu.bio.wzw.tum.de/das/) and Web-Service (http://mips.gsf.de/webservices/services/SimapService2.0?wsdl). |
format | Text |
id | pubmed-2808863 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-28088632010-01-20 SIMAP—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters Rattei, Thomas Tischler, Patrick Götz, Stefan Jehl, Marc-André Hoser, Jonathan Arnold, Roland Conesa, Ana Mewes, Hans-Werner Nucleic Acids Res Articles The prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the similarity matrix, the computation of the Similarity Matrix of Proteins (SIMAP) becomes a computational intensive task. The SIMAP database provides a comprehensive and up-to-date pre-calculation of the protein sequence similarity matrix, sequence-based features and sequence clusters. As of September 2009, SIMAP covers 48 million proteins and more than 23 million non-redundant sequences. Novel features of SIMAP include the expansion of the sequence space by including databases such as ENSEMBL as well as the integration of metagenomes based on their consistent processing and annotation. Furthermore, protein function predictions by Blast2GO are pre-calculated for all sequences in SIMAP and the data access and query functions have been improved. SIMAP assists biologists to query the up-to-date sequence space systematically and facilitates large-scale downstream projects in computational biology. Access to SIMAP is freely provided through the web portal for individuals (http://mips.gsf.de/simap/) and for programmatic access through DAS (http://webclu.bio.wzw.tum.de/das/) and Web-Service (http://mips.gsf.de/webservices/services/SimapService2.0?wsdl). Oxford University Press 2010-01 2009-11-11 /pmc/articles/PMC2808863/ /pubmed/19906725 http://dx.doi.org/10.1093/nar/gkp949 Text en © The Author(s) 2009. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Rattei, Thomas Tischler, Patrick Götz, Stefan Jehl, Marc-André Hoser, Jonathan Arnold, Roland Conesa, Ana Mewes, Hans-Werner SIMAP—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters |
title | SIMAP—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters |
title_full | SIMAP—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters |
title_fullStr | SIMAP—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters |
title_full_unstemmed | SIMAP—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters |
title_short | SIMAP—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters |
title_sort | simap—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2808863/ https://www.ncbi.nlm.nih.gov/pubmed/19906725 http://dx.doi.org/10.1093/nar/gkp949 |
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