ConoDictor: a tool for prediction of conopeptide superfamilies

ConoDictor is a tool that enables fast and accurate classification of conopeptides into superfamilies based on their amino acid sequence. ConoDictor combines predictions from two complementary approaches—profile hidden Markov models and generalized profiles. Results appear in a browser as tables tha...

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Detalles Bibliográficos
Autores principales: Koua, Dominique, Brauer, Age, Laht, Silja, Kaplinski, Lauris, Favreau, Philippe, Remm, Maido, Lisacek, Frédérique, Stöcklin, Reto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3394318/
https://www.ncbi.nlm.nih.gov/pubmed/22661581
http://dx.doi.org/10.1093/nar/gks337
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author Koua, Dominique
Brauer, Age
Laht, Silja
Kaplinski, Lauris
Favreau, Philippe
Remm, Maido
Lisacek, Frédérique
Stöcklin, Reto
author_facet Koua, Dominique
Brauer, Age
Laht, Silja
Kaplinski, Lauris
Favreau, Philippe
Remm, Maido
Lisacek, Frédérique
Stöcklin, Reto
author_sort Koua, Dominique
collection PubMed
description ConoDictor is a tool that enables fast and accurate classification of conopeptides into superfamilies based on their amino acid sequence. ConoDictor combines predictions from two complementary approaches—profile hidden Markov models and generalized profiles. Results appear in a browser as tables that can be downloaded in various formats. This application is particularly valuable in view of the exponentially increasing number of conopeptides that are being identified. ConoDictor was written in Perl using the common gateway interface module with a php submission page. Sequence matching is performed with hmmsearch from HMMER 3 and ps_scan.pl from the pftools 2.3 package. ConoDictor is freely accessible at http://conco.ebc.ee.
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spelling pubmed-33943182012-07-30 ConoDictor: a tool for prediction of conopeptide superfamilies Koua, Dominique Brauer, Age Laht, Silja Kaplinski, Lauris Favreau, Philippe Remm, Maido Lisacek, Frédérique Stöcklin, Reto Nucleic Acids Res Articles ConoDictor is a tool that enables fast and accurate classification of conopeptides into superfamilies based on their amino acid sequence. ConoDictor combines predictions from two complementary approaches—profile hidden Markov models and generalized profiles. Results appear in a browser as tables that can be downloaded in various formats. This application is particularly valuable in view of the exponentially increasing number of conopeptides that are being identified. ConoDictor was written in Perl using the common gateway interface module with a php submission page. Sequence matching is performed with hmmsearch from HMMER 3 and ps_scan.pl from the pftools 2.3 package. ConoDictor is freely accessible at http://conco.ebc.ee. Oxford University Press 2012-07 2012-05-31 /pmc/articles/PMC3394318/ /pubmed/22661581 http://dx.doi.org/10.1093/nar/gks337 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Koua, Dominique
Brauer, Age
Laht, Silja
Kaplinski, Lauris
Favreau, Philippe
Remm, Maido
Lisacek, Frédérique
Stöcklin, Reto
ConoDictor: a tool for prediction of conopeptide superfamilies
title ConoDictor: a tool for prediction of conopeptide superfamilies
title_full ConoDictor: a tool for prediction of conopeptide superfamilies
title_fullStr ConoDictor: a tool for prediction of conopeptide superfamilies
title_full_unstemmed ConoDictor: a tool for prediction of conopeptide superfamilies
title_short ConoDictor: a tool for prediction of conopeptide superfamilies
title_sort conodictor: a tool for prediction of conopeptide superfamilies
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3394318/
https://www.ncbi.nlm.nih.gov/pubmed/22661581
http://dx.doi.org/10.1093/nar/gks337
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