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IEM: an algorithm for iterative enhancement of motifs using comparative genomics data
Journal article

IEM: an algorithm for iterative enhancement of motifs using comparative genomics data

Erliang Zeng, Kalai Mathee and Giri Narasimhan
Computational systems bioinformatics. Computational Systems Bioinformatics Conference, Vol.6, pp.227-235
2007
DOI: 10.1142/9781860948732_0025
PMID: 17951827

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Abstract

Understanding gene regulation is a key step to investigating gene functions and their relationships. Many algorithms have been developed to discover transcription factor binding sites (TFBS); they are predominantly located in upstream regions of genes and contribute to transcription regulation if they are bound by a specific transcription factor. However, traditional methods focusing on finding motifs have shortcomings, which can be overcome by using comparative genomics data that is now increasingly available. Traditional methods to score motifs also have their limitations. In this paper, we propose a new algorithm called IEM to refine motifs using comparative genomics data. We show the effectiveness of our techniques with several data sets. Two sets of experiments were performed with comparative genomics data on five strains of P. aeruginosa. One set of experiments were performed with similar data on four species of yeast. The weighted conservation score proposed in this paper is an improvement over existing motif scores.
 Algorithms Software Sequence Alignment - methods Base Sequence Chromosome Mapping - methods Molecular Sequence Data Protein Binding Transcription Factors - genetics Binding Sites Sequence Analysis, DNA - methods Sequence Homology, Nucleic Acid

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