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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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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2006
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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. |
format | Text |
id | pubmed-1540429 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT linan analysisofcomputationalapproachesformotifdiscovery AT tompamartin analysisofcomputationalapproachesformotifdiscovery |