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The InterPro protein families database: the classification resource after 15 years

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of...

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Autores principales: Mitchell, Alex, Chang, Hsin-Yu, Daugherty, Louise, Fraser, Matthew, Hunter, Sarah, Lopez, Rodrigo, McAnulla, Craig, McMenamin, Conor, Nuka, Gift, Pesseat, Sebastien, Sangrador-Vegas, Amaia, Scheremetjew, Maxim, Rato, Claudia, Yong, Siew-Yit, Bateman, Alex, Punta, Marco, Attwood, Teresa K., Sigrist, Christian J.A., Redaschi, Nicole, Rivoire, Catherine, Xenarios, Ioannis, Kahn, Daniel, Guyot, Dominique, Bork, Peer, Letunic, Ivica, Gough, Julian, Oates, Matt, Haft, Daniel, Huang, Hongzhan, Natale, Darren A., Wu, Cathy H., Orengo, Christine, Sillitoe, Ian, Mi, Huaiyu, Thomas, Paul D., Finn, Robert D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383996/
https://www.ncbi.nlm.nih.gov/pubmed/25428371
http://dx.doi.org/10.1093/nar/gku1243
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author Mitchell, Alex
Chang, Hsin-Yu
Daugherty, Louise
Fraser, Matthew
Hunter, Sarah
Lopez, Rodrigo
McAnulla, Craig
McMenamin, Conor
Nuka, Gift
Pesseat, Sebastien
Sangrador-Vegas, Amaia
Scheremetjew, Maxim
Rato, Claudia
Yong, Siew-Yit
Bateman, Alex
Punta, Marco
Attwood, Teresa K.
Sigrist, Christian J.A.
Redaschi, Nicole
Rivoire, Catherine
Xenarios, Ioannis
Kahn, Daniel
Guyot, Dominique
Bork, Peer
Letunic, Ivica
Gough, Julian
Oates, Matt
Haft, Daniel
Huang, Hongzhan
Natale, Darren A.
Wu, Cathy H.
Orengo, Christine
Sillitoe, Ian
Mi, Huaiyu
Thomas, Paul D.
Finn, Robert D.
author_facet Mitchell, Alex
Chang, Hsin-Yu
Daugherty, Louise
Fraser, Matthew
Hunter, Sarah
Lopez, Rodrigo
McAnulla, Craig
McMenamin, Conor
Nuka, Gift
Pesseat, Sebastien
Sangrador-Vegas, Amaia
Scheremetjew, Maxim
Rato, Claudia
Yong, Siew-Yit
Bateman, Alex
Punta, Marco
Attwood, Teresa K.
Sigrist, Christian J.A.
Redaschi, Nicole
Rivoire, Catherine
Xenarios, Ioannis
Kahn, Daniel
Guyot, Dominique
Bork, Peer
Letunic, Ivica
Gough, Julian
Oates, Matt
Haft, Daniel
Huang, Hongzhan
Natale, Darren A.
Wu, Cathy H.
Orengo, Christine
Sillitoe, Ian
Mi, Huaiyu
Thomas, Paul D.
Finn, Robert D.
author_sort Mitchell, Alex
collection PubMed
description The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.
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spelling pubmed-43839962015-04-08 The InterPro protein families database: the classification resource after 15 years Mitchell, Alex Chang, Hsin-Yu Daugherty, Louise Fraser, Matthew Hunter, Sarah Lopez, Rodrigo McAnulla, Craig McMenamin, Conor Nuka, Gift Pesseat, Sebastien Sangrador-Vegas, Amaia Scheremetjew, Maxim Rato, Claudia Yong, Siew-Yit Bateman, Alex Punta, Marco Attwood, Teresa K. Sigrist, Christian J.A. Redaschi, Nicole Rivoire, Catherine Xenarios, Ioannis Kahn, Daniel Guyot, Dominique Bork, Peer Letunic, Ivica Gough, Julian Oates, Matt Haft, Daniel Huang, Hongzhan Natale, Darren A. Wu, Cathy H. Orengo, Christine Sillitoe, Ian Mi, Huaiyu Thomas, Paul D. Finn, Robert D. Nucleic Acids Res Database Issue The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012. Oxford University Press 2014-11-26 2015-01-28 /pmc/articles/PMC4383996/ /pubmed/25428371 http://dx.doi.org/10.1093/nar/gku1243 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Database Issue
Mitchell, Alex
Chang, Hsin-Yu
Daugherty, Louise
Fraser, Matthew
Hunter, Sarah
Lopez, Rodrigo
McAnulla, Craig
McMenamin, Conor
Nuka, Gift
Pesseat, Sebastien
Sangrador-Vegas, Amaia
Scheremetjew, Maxim
Rato, Claudia
Yong, Siew-Yit
Bateman, Alex
Punta, Marco
Attwood, Teresa K.
Sigrist, Christian J.A.
Redaschi, Nicole
Rivoire, Catherine
Xenarios, Ioannis
Kahn, Daniel
Guyot, Dominique
Bork, Peer
Letunic, Ivica
Gough, Julian
Oates, Matt
Haft, Daniel
Huang, Hongzhan
Natale, Darren A.
Wu, Cathy H.
Orengo, Christine
Sillitoe, Ian
Mi, Huaiyu
Thomas, Paul D.
Finn, Robert D.
The InterPro protein families database: the classification resource after 15 years
title The InterPro protein families database: the classification resource after 15 years
title_full The InterPro protein families database: the classification resource after 15 years
title_fullStr The InterPro protein families database: the classification resource after 15 years
title_full_unstemmed The InterPro protein families database: the classification resource after 15 years
title_short The InterPro protein families database: the classification resource after 15 years
title_sort interpro protein families database: the classification resource after 15 years
topic Database Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383996/
https://www.ncbi.nlm.nih.gov/pubmed/25428371
http://dx.doi.org/10.1093/nar/gku1243
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