TY - GEN
T1 - TFBS detection algorithm using distance metrics based on center of mass and polyphase mapping
AU - Bataineh, Mohammad Al
AU - Huang, Lun
AU - Atkin, Guillermo
PY - 2012
Y1 - 2012
N2 - Regulatory sequence detection is a fundamental challenge in computational biology. The transcription process in protein synthesis starts with the binding of the transcription factor to its binding site. Different sites can bind to the same factor. This variability in binding sequences increases the difficulty of their detection using computational algorithms. This paper proposes a novel algorithm for transcription factor binding site (TFBS) detection. The algorithm applies a polyphase mapping scheme to represent the four nucleobases in both the DNA sequence and the set of binding sites associated with a given transcription factor (TF). The center of mass (CoM) of each set of binding sites, which can be thought of as a consensus sequence, is then calculated. The algorithm then calculates distances between the CoM and each binding site belonging to a given TF. Same scenario is then applied to the genome sequence under study. The obtained distances are then utilized to detect new potential TFBSs based on their similitude of the set of binding sites that we already know. Analysis is applied to E. coli bacterial genomes. Simulation results verify the correctness and the biological relevance of the proposed algorithm.
AB - Regulatory sequence detection is a fundamental challenge in computational biology. The transcription process in protein synthesis starts with the binding of the transcription factor to its binding site. Different sites can bind to the same factor. This variability in binding sequences increases the difficulty of their detection using computational algorithms. This paper proposes a novel algorithm for transcription factor binding site (TFBS) detection. The algorithm applies a polyphase mapping scheme to represent the four nucleobases in both the DNA sequence and the set of binding sites associated with a given transcription factor (TF). The center of mass (CoM) of each set of binding sites, which can be thought of as a consensus sequence, is then calculated. The algorithm then calculates distances between the CoM and each binding site belonging to a given TF. Same scenario is then applied to the genome sequence under study. The obtained distances are then utilized to detect new potential TFBSs based on their similitude of the set of binding sites that we already know. Analysis is applied to E. coli bacterial genomes. Simulation results verify the correctness and the biological relevance of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=84862730529&partnerID=8YFLogxK
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U2 - 10.1109/HIBIT.2012.6209039
DO - 10.1109/HIBIT.2012.6209039
M3 - Conference contribution
AN - SCOPUS:84862730529
SN - 9781467308786
T3 - 2012 7th International Symposium on Health Informatics and Bioinformatics, HIBIT 2012
SP - 37
EP - 40
BT - 2012 7th International Symposium on Health Informatics and Bioinformatics, HIBIT 2012
T2 - 2012 7th International Symposium on Health Informatics and Bioinformatics, HIBIT 2012
Y2 - 19 April 2012 through 22 April 2012
ER -