Research article
Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data
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* Corresponding author: Habtom W Ressom hwr@georgetown.edu
1 Lombardi Comprehensive Cancer Center, Georgetown University, 4000 Reservoir Rd, Washington, DC, USA
2 Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 4300 Wilson Blvd., Arlington, VA, USA
3 School of Biology and Ecology, University of Maine, Orono, ME 04469, USA
BMC Bioinformatics 2008, 9:203 doi:10.1186/1471-2105-9-203
Published: 21 April 2008Additional files
Additional file 1:
Gene clusters obtained by using the FCM clustering algorithm. The data provided present the cluster membership for each of the 800 genes considered in this study.
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Additional file 2:
Potential TFs among yeast cell cycle related genes. The data provided present TFs we identified from 800 cell cycle related genes based on their GO annotations. The genes annotated with terms related to transcription activities and DNA binding are considered as potential TFs.
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Additional file 3:
Predicted motifs for gene clusters. The data provided present the top three enriched motifs for each gene cluster identified in this study. The motifs are predicted through promoter sequence analysis of the gene clusters using WebMOTIFS http://fraenkel.mit.edu/webmotifs/ webcite. This information helps in the validation of the NM prediction results. For example, the predicted downstream gene clusters of FKH1 all have a motif called Fork_head, GTAAACAA, in their promoter regions. This suggests that our NM inference strategy has the capability to identify the downstream target genes for TFs based on their NM assignment. The motif is annotated in Pfam database http://pfam.sanger.ac.uk/ webcite.
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