Computer Science

COMPUTATIONAL METHODS FOR IDENTIFYING TRANSCRIPTION FACTOR BINIDNG SITES
IN *PLASMODIUM FALCIPARUM*, [Sean Westfall]{.underline}^1^, Kim
Williamson^2^, Catherine Putonti\*^1,2^

1 Loyola University Chicago, Department of Computer Science, Chicago, IL
60626; 2 Loyola University Chicago, Department of Biology, Chicago, IL
60626; <cputonti@luc.edu>

Relatively few transcription factor binding sites (TFBS) are known for
*Plasmodium falciparum*, the causative agent of malaria. Though,
knowledge of these sites would prove invaluable in understanding the
highly unique transcriptional regulation employed by this organism. With
the entire genome of *P. falciparum* sequenced and assembled through the
Malaria Genome Project, it is now possible to use computational methods
to find TFBS. Numerous different algorithms have been developed to
identify TFBS, particularly those within model organisms such as yeast
and *E. coli*. These methods are predominantly based upon finding
statistically overrepresented motifs, or short segments of DNA, in the
upstream regions of clustered genes known to be co-regulated. Much of
the difficulty faced in extending these methods to the examination of
*P. falciparum* is due to the organism's high AT richness (\~80%) and a
lack of understanding of the organism's transcriptional regulatory
mechanisms. Thus far, algorithms designed to recognize TFBS in *P.
falciparum* have utilized microarray expression data to first cluster
genes that appear to be co-regulated and then apply existing algorithms
of motif detection. As a result, biases may be introduced from the
underlying expression data or the particular clustering algorithm
employed.

Herein, we present a multi-tier analysis for TFBS discovery in higher
order organisms. Firstly, we compared the occurrences of 7-mer motifs in
the 2000 basepair (bp) upstream regions for coding regions of interest
to the upstream regions of the entire genome sequence. In doing so, we
identified putative motifs that appear within the genes of interest and
do not appear (or do not appear often) elsewhere in the genome; such
motifs could be functionally important sites. Next, we looked for
correlations between the genes for which the putative motifs occurred
and the expression profile of these genes from microarray data.
Furthermore, we used a sliding window approach to gain insight into the
relationship between the GC content of these putative motifs against the
background AT content within the upstream regions of these genes. In
order to assess the strength of our method, we analyzed putative motifs
in well studied gene families, specifically the *var* gene family and a
family of heat shock proteins, for which the actual TFBS has been
experimentally verified. Because clustering is applied only once
putative motifs have been identified, expression data bias' is removed.

Our results demonstrate that there is a relationship between the GC
content of specific motifs and the background AT content within the
upstream region of co-regulated gene families. Further, our results show
that though more experimentation is necessary, our method can provide
much insight into *P. falciparum*'s transcriptional regulatory control
system.