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HoxPred: automated classification of Hox proteins using combinations of generalised profiles
BACKGROUND: Correct identification of individual Hox proteins is an essential basis for their study in diverse research fields. Common methods to classify Hox proteins focus on the homeodomain that characterise homeobox transcription factors. Classification is hampered by the high conservation of th...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1965487/ https://www.ncbi.nlm.nih.gov/pubmed/17626621 http://dx.doi.org/10.1186/1471-2105-8-247 |
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author | Thomas-Chollier, Morgane Leyns, Luc Ledent, Valérie |
author_facet | Thomas-Chollier, Morgane Leyns, Luc Ledent, Valérie |
author_sort | Thomas-Chollier, Morgane |
collection | PubMed |
description | BACKGROUND: Correct identification of individual Hox proteins is an essential basis for their study in diverse research fields. Common methods to classify Hox proteins focus on the homeodomain that characterise homeobox transcription factors. Classification is hampered by the high conservation of this short domain. Phylogenetic tree reconstruction is a widely used but time-consuming classification method. RESULTS: We have developed an automated procedure, HoxPred, that classifies Hox proteins in their groups of homology. The method relies on a discriminant analysis that classifies Hox proteins according to their scores for a combination of protein generalised profiles. 54 generalised profiles dedicated to each Hox homology group were produced de novo from a curated dataset of vertebrate Hox proteins. Several classification methods were investigated to select the most accurate discriminant functions. These functions were then incorporated into the HoxPred program. CONCLUSION: HoxPred shows a mean accuracy of 97%. Predictions on the recently-sequenced stickleback fish proteome identified 44 Hox proteins, including HoxC1a only found so far in zebrafish. Using the Uniprot databank, we demonstrate that HoxPred can efficiently contribute to large-scale automatic annotation of Hox proteins into their paralogous groups. As orthologous group predictions show a higher risk of misclassification, they should be corroborated by additional supporting evidence. HoxPred is accessible via SOAP and Web interface . Complete datasets, results and source code are available at the same site. |
format | Text |
id | pubmed-1965487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-19654872007-09-06 HoxPred: automated classification of Hox proteins using combinations of generalised profiles Thomas-Chollier, Morgane Leyns, Luc Ledent, Valérie BMC Bioinformatics Research Article BACKGROUND: Correct identification of individual Hox proteins is an essential basis for their study in diverse research fields. Common methods to classify Hox proteins focus on the homeodomain that characterise homeobox transcription factors. Classification is hampered by the high conservation of this short domain. Phylogenetic tree reconstruction is a widely used but time-consuming classification method. RESULTS: We have developed an automated procedure, HoxPred, that classifies Hox proteins in their groups of homology. The method relies on a discriminant analysis that classifies Hox proteins according to their scores for a combination of protein generalised profiles. 54 generalised profiles dedicated to each Hox homology group were produced de novo from a curated dataset of vertebrate Hox proteins. Several classification methods were investigated to select the most accurate discriminant functions. These functions were then incorporated into the HoxPred program. CONCLUSION: HoxPred shows a mean accuracy of 97%. Predictions on the recently-sequenced stickleback fish proteome identified 44 Hox proteins, including HoxC1a only found so far in zebrafish. Using the Uniprot databank, we demonstrate that HoxPred can efficiently contribute to large-scale automatic annotation of Hox proteins into their paralogous groups. As orthologous group predictions show a higher risk of misclassification, they should be corroborated by additional supporting evidence. HoxPred is accessible via SOAP and Web interface . Complete datasets, results and source code are available at the same site. BioMed Central 2007-07-12 /pmc/articles/PMC1965487/ /pubmed/17626621 http://dx.doi.org/10.1186/1471-2105-8-247 Text en Copyright © 2007 Thomas-Chollier et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Thomas-Chollier, Morgane Leyns, Luc Ledent, Valérie HoxPred: automated classification of Hox proteins using combinations of generalised profiles |
title | HoxPred: automated classification of Hox proteins using combinations of generalised profiles |
title_full | HoxPred: automated classification of Hox proteins using combinations of generalised profiles |
title_fullStr | HoxPred: automated classification of Hox proteins using combinations of generalised profiles |
title_full_unstemmed | HoxPred: automated classification of Hox proteins using combinations of generalised profiles |
title_short | HoxPred: automated classification of Hox proteins using combinations of generalised profiles |
title_sort | hoxpred: automated classification of hox proteins using combinations of generalised profiles |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1965487/ https://www.ncbi.nlm.nih.gov/pubmed/17626621 http://dx.doi.org/10.1186/1471-2105-8-247 |
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