Analysis of computational approaches for motif discovery

by: Nan Li, Martin Tompa

Algorithms for Molecular Biology, Vol. 1, No. 1. (19 May 2006), 8.

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Abstract

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 analyses 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.

@article{citeulike:665531, abstract = {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 analyses 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.}, author = {Li, Nan and Tompa, Martin }, citeulike-article-id = {665531}, doi = {http://dx.doi.org/10.1186/1748-7188-1-8}, issn = {1748-7188}, journal = {Algorithms for Molecular Biology}, keywords = {review, sequence\_comparison, sequence\_motif, transcription\_factor}, month = {May}, number = {1}, pages = {8+}, posted-at = {2007-08-28 02:13:10}, priority = {2}, title = {Analysis of computational approaches for motif discovery}, url = {http://dx.doi.org/10.1186/1748-7188-1-8}, volume = {1}, year = {2006} }

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TY - JOUR ID - citeulike:665531 L3 - citeulike-article-id:665531 TI - Analysis of computational approaches for motif discovery JF - Algorithms for Molecular Biology VL - 1 IS - 1 SP - 8 SN - 1748-7188 N2 - 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 analyses 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. KW - review KW - sequence_comparison KW - sequence_motif KW - transcription_factor AU - Li, Nan AU - Tompa, Martin PY - 2006/05/19/ UR - http://dx.doi.org/10.1186/1748-7188-1-8 ER -

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