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Analysis of computational approaches for motif discovery

Recently, we performed an assessment of 13 popular computational tools for discovery of transcription factor binding sites (M. Tompa, N. Li, et al., "Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites", Nature Biotechnology, Jan. 2005). This paper contain...

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Detalles Bibliográficos
Autores principales: Li, Nan, Tompa, Martin
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1540429/
https://www.ncbi.nlm.nih.gov/pubmed/16722558
http://dx.doi.org/10.1186/1748-7188-1-8
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author Li, Nan
Tompa, Martin
author_facet Li, Nan
Tompa, Martin
author_sort Li, Nan
collection PubMed
description Recently, we performed an assessment of 13 popular computational tools for discovery of transcription factor binding sites (M. Tompa, N. Li, et al., "Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites", Nature Biotechnology, Jan. 2005). This paper contains follow-up analysis of the assessment results, and raises and discusses some important issues concerning the state of the art in motif discovery methods: 1. We categorize the objective functions used by existing tools, and design experiments to evaluate whether any of these objective functions is the right one to optimize. 2. We examine various features of the data sets that were used in the assessment, such as sequence length and motif degeneracy, and identify which features make data sets hard for current motif discovery tools. 3. We identify an important feature that has not yet been used by existing tools and propose a new objective function that incorporates this feature.
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spelling pubmed-15404292006-08-12 Analysis of computational approaches for motif discovery Li, Nan Tompa, Martin Algorithms Mol Biol Research Recently, we performed an assessment of 13 popular computational tools for discovery of transcription factor binding sites (M. Tompa, N. Li, et al., "Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites", Nature Biotechnology, Jan. 2005). This paper contains follow-up analysis of the assessment results, and raises and discusses some important issues concerning the state of the art in motif discovery methods: 1. We categorize the objective functions used by existing tools, and design experiments to evaluate whether any of these objective functions is the right one to optimize. 2. We examine various features of the data sets that were used in the assessment, such as sequence length and motif degeneracy, and identify which features make data sets hard for current motif discovery tools. 3. We identify an important feature that has not yet been used by existing tools and propose a new objective function that incorporates this feature. BioMed Central 2006-05-19 /pmc/articles/PMC1540429/ /pubmed/16722558 http://dx.doi.org/10.1186/1748-7188-1-8 Text en Copyright © 2006 Li and Tompa; 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
Li, Nan
Tompa, Martin
Analysis of computational approaches for motif discovery
title Analysis of computational approaches for motif discovery
title_full Analysis of computational approaches for motif discovery
title_fullStr Analysis of computational approaches for motif discovery
title_full_unstemmed Analysis of computational approaches for motif discovery
title_short Analysis of computational approaches for motif discovery
title_sort analysis of computational approaches for motif discovery
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1540429/
https://www.ncbi.nlm.nih.gov/pubmed/16722558
http://dx.doi.org/10.1186/1748-7188-1-8
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