Chengyong Yang1,*, Erliang Zeng1,*, Kalai Mathee2, Giri Narasimhan1,#
1School of Computer Science, Florida International University
2Department of Biological Sciences, Florida International Unversity
#To whom correspondence should be addressed: School of Computer Science, Florida International University, Miami, FL 33199,
giri@cs.fiu.edu,Phone: (305) 348-3748, Fax: (305) 348-3549
* Contributed equally to this paper
Abstract
There is very little information available with regard to gene regulatory relationships in Plasmodium falciparum. In an attempt to discover transcription factor binding motifs (TFBMs) in P. falciparum, we considered two approaches. In the first approach, gene expression data of all the conditions were fed into the Iterative Signature Algorithm (ISA), which outputs modules composed of sets of genes associated with co-regulating conditions. Potential TFBMs were discovered by applying AlignACE on the resulting gene sets. In the second approach, MotifRegressor was used to generate motifs associated with induced and repressed genes for each time point and then clustered based on the strength of their correlation to the gene expression (i.e., motif coefficients) across different time points. Currently, a total of 637 and 840 motifs have been discovered by the MotifRegressor and ISA-AlignACE programs, respectively. All this information was uploaded into a database, thus making it easy to devise complex queries. Using published information on known motifs, we were able to validate some of our results. In addition, modules consisting of putative transcription factors and related genes were also investigated. This work provides a bioinformatics methodology to analyze transcription regulation and TFBMs across the whole genome.
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