This article is part of the supplement: The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08)

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Word-based characterization of promoters involved in human DNA repair pathways

Jens Lichtenberg1*, Edwin Jacox2, Joshua D Welch1, Kyle Kurz1, Xiaoyu Liang1, Mary Qu Yang2, Frank Drews1, Klaus Ecker1, Stephen S Lee3, Laura Elnitski2 and Lonnie R Welch145

Author Affiliations

1 Bioinformatics Laboratory, School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio, USA

2 Genomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Rockville, Maryland, USA

3 Department of Statistics, University of Idaho, Moscow, Idaho, USA

4 Biomedical Engineering Program, Ohio University, Athens, Ohio, USA

5 Molecular and Cellular Biology Program, Ohio University, Athens, Ohio, USA

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BMC Genomics 2009, 10(Suppl 1):S18  doi:10.1186/1471-2164-10-S1-S18

Published: 7 July 2009



DNA repair genes provide an important contribution towards the surveillance and repair of DNA damage. These genes produce a large network of interacting proteins whose mRNA expression is likely to be regulated by similar regulatory factors. Full characterization of promoters of DNA repair genes and the similarities among them will more fully elucidate the regulatory networks that activate or inhibit their expression. To address this goal, the authors introduce a technique to find regulatory genomic signatures, which represents a specific application of the genomic signature methodology to classify DNA sequences as putative functional elements within a single organism.


The effectiveness of the regulatory genomic signatures is demonstrated via analysis of promoter sequences for genes in DNA repair pathways of humans. The promoters are divided into two classes, the bidirectional promoters and the unidirectional promoters, and distinct genomic signatures are calculated for each class. The genomic signatures include statistically overrepresented words, word clusters, and co-occurring words. The robustness of this method is confirmed by the ability to identify sequences that exist as motifs in TRANSFAC and JASPAR databases, and in overlap with verified binding sites in this set of promoter regions.


The word-based signatures are shown to be effective by finding occurrences of known regulatory sites. Moreover, the signatures of the bidirectional and unidirectional promoters of human DNA repair pathways are clearly distinct, exhibiting virtually no overlap. In addition to providing an effective characterization method for related DNA sequences, the signatures elucidate putative regulatory aspects of DNA repair pathways, which are notably under-characterized.