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MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences

BACKGROUND: Computational approaches for finding DNA regulatory motifs in promoter sequences are useful to biologists in terms of reducing the experimental costs and speeding up the discovery process of de novo binding sites. It is important for rule-based or clustering-based motif searching schemes...

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Autores principales: Wang, Dianhui, Tapan, Sarwar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521183/
https://www.ncbi.nlm.nih.gov/pubmed/23282090
http://dx.doi.org/10.1186/1752-0509-6-S2-S4
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author Wang, Dianhui
Tapan, Sarwar
author_facet Wang, Dianhui
Tapan, Sarwar
author_sort Wang, Dianhui
collection PubMed
description BACKGROUND: Computational approaches for finding DNA regulatory motifs in promoter sequences are useful to biologists in terms of reducing the experimental costs and speeding up the discovery process of de novo binding sites. It is important for rule-based or clustering-based motif searching schemes to effectively and efficiently evaluate the similarity between a k-mer (a k-length subsequence) and a motif model, without assuming the independence of nucleotides in motif models or without employing computationally expensive Markov chain models to estimate the background probabilities of k-mers. Also, it is interesting and beneficial to use a priori knowledge in developing advanced searching tools. RESULTS: This paper presents a new scoring function, termed as MISCORE, for functional motif characterization and evaluation. Our MISCORE is free from: (i) any assumption on model dependency; and (ii) the use of Markov chain model for background modeling. It integrates the compositional complexity of motif instances into the function. Performance evaluations with comparison to the well-known Maximum a Posteriori (MAP) score and Information Content (IC) have shown that MISCORE has promising capabilities to separate and recognize functional DNA motifs and its instances from non-functional ones. CONCLUSIONS: MISCORE is a fast computational tool for candidate motif characterization, evaluation and selection. It enables to embed priori known motif models for computing motif-to-motif similarity, which is more advantageous than IC and MAP score. In addition to these merits mentioned above, MISCORE can automatically filter out some repetitive k-mers from a motif model due to the introduction of the compositional complexity in the function. Consequently, the merits of our proposed MISCORE in terms of both motif signal modeling power and computational efficiency will make it more applicable in the development of computational motif discovery tools.
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spelling pubmed-35211832012-12-14 MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences Wang, Dianhui Tapan, Sarwar BMC Syst Biol Proceedings BACKGROUND: Computational approaches for finding DNA regulatory motifs in promoter sequences are useful to biologists in terms of reducing the experimental costs and speeding up the discovery process of de novo binding sites. It is important for rule-based or clustering-based motif searching schemes to effectively and efficiently evaluate the similarity between a k-mer (a k-length subsequence) and a motif model, without assuming the independence of nucleotides in motif models or without employing computationally expensive Markov chain models to estimate the background probabilities of k-mers. Also, it is interesting and beneficial to use a priori knowledge in developing advanced searching tools. RESULTS: This paper presents a new scoring function, termed as MISCORE, for functional motif characterization and evaluation. Our MISCORE is free from: (i) any assumption on model dependency; and (ii) the use of Markov chain model for background modeling. It integrates the compositional complexity of motif instances into the function. Performance evaluations with comparison to the well-known Maximum a Posteriori (MAP) score and Information Content (IC) have shown that MISCORE has promising capabilities to separate and recognize functional DNA motifs and its instances from non-functional ones. CONCLUSIONS: MISCORE is a fast computational tool for candidate motif characterization, evaluation and selection. It enables to embed priori known motif models for computing motif-to-motif similarity, which is more advantageous than IC and MAP score. In addition to these merits mentioned above, MISCORE can automatically filter out some repetitive k-mers from a motif model due to the introduction of the compositional complexity in the function. Consequently, the merits of our proposed MISCORE in terms of both motif signal modeling power and computational efficiency will make it more applicable in the development of computational motif discovery tools. BioMed Central 2012-12-12 /pmc/articles/PMC3521183/ /pubmed/23282090 http://dx.doi.org/10.1186/1752-0509-6-S2-S4 Text en Copyright ©2012 Wang and Tapan; 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 Proceedings
Wang, Dianhui
Tapan, Sarwar
MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences
title MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences
title_full MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences
title_fullStr MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences
title_full_unstemmed MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences
title_short MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences
title_sort miscore: a new scoring function for characterizing dna regulatory motifs in promoter sequences
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521183/
https://www.ncbi.nlm.nih.gov/pubmed/23282090
http://dx.doi.org/10.1186/1752-0509-6-S2-S4
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