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Phenoclustering: online mining of cross-species phenotypes

Summary: Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety...

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Detalles Bibliográficos
Autores principales: Groth, Philip, Kalev, Ivan, Kirov, Ivaylo, Traikov, Borislav, Leser, Ulf, Weiss, Bertram
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905556/
https://www.ncbi.nlm.nih.gov/pubmed/20562418
http://dx.doi.org/10.1093/bioinformatics/btq311
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author Groth, Philip
Kalev, Ivan
Kirov, Ivaylo
Traikov, Borislav
Leser, Ulf
Weiss, Bertram
author_facet Groth, Philip
Kalev, Ivan
Kirov, Ivaylo
Traikov, Borislav
Leser, Ulf
Weiss, Bertram
author_sort Groth, Philip
collection PubMed
description Summary: Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety of sources and screening methods can be analyzed together. Clusters of similar phenotypes are visualized as networks of highly similar phenotypes, inducing gene groups useful for functional analysis. This system is part of PhenomicDB, providing the world's largest cross-species phenotype data collection with a tool to mine its wealth of information. Availability: Freely available at http://www.phenomicdb.de Contact: bertram.weiss@bayerhealthcare.com Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-29055562010-07-19 Phenoclustering: online mining of cross-species phenotypes Groth, Philip Kalev, Ivan Kirov, Ivaylo Traikov, Borislav Leser, Ulf Weiss, Bertram Bioinformatics Applications Note Summary: Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety of sources and screening methods can be analyzed together. Clusters of similar phenotypes are visualized as networks of highly similar phenotypes, inducing gene groups useful for functional analysis. This system is part of PhenomicDB, providing the world's largest cross-species phenotype data collection with a tool to mine its wealth of information. Availability: Freely available at http://www.phenomicdb.de Contact: bertram.weiss@bayerhealthcare.com Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2010-08-01 2010-06-18 /pmc/articles/PMC2905556/ /pubmed/20562418 http://dx.doi.org/10.1093/bioinformatics/btq311 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/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), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Groth, Philip
Kalev, Ivan
Kirov, Ivaylo
Traikov, Borislav
Leser, Ulf
Weiss, Bertram
Phenoclustering: online mining of cross-species phenotypes
title Phenoclustering: online mining of cross-species phenotypes
title_full Phenoclustering: online mining of cross-species phenotypes
title_fullStr Phenoclustering: online mining of cross-species phenotypes
title_full_unstemmed Phenoclustering: online mining of cross-species phenotypes
title_short Phenoclustering: online mining of cross-species phenotypes
title_sort phenoclustering: online mining of cross-species phenotypes
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905556/
https://www.ncbi.nlm.nih.gov/pubmed/20562418
http://dx.doi.org/10.1093/bioinformatics/btq311
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