A novel sequence and context based method for promoter recognition



P Umesh1*, Jitendra Kumar Dubey2, Karthika RV1, Betsy Sheena Cherian3, Gopakumar Gopalakrishnan2 & Achuthsankar Sukumaran Nair1



1Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram 695581, Kerala, India; 2Department of Computer Science and Engineering, National Institute of Technology, Calicut - 673601, Kerala, India; 3Faculty of Science, Kuwait University, P. O. Box: 5969, SAFAT: 13060, Kuwait


Email; *Corresponding author


Article Type




Received March 12, 2014; Revised March 17, 2014; Accepted March 18, 2014; Published April 23, 2014



Identification of promoters in DNA sequence using computational techniques is a significant research area because of its direct association in transcription regulation. A wide range of algorithms are available for promoter prediction. Most of them are polymerase dependent and cannot handle eukaryotes and prokaryotes alike. This study proposes a polymerase independent algorithm, which can predict whether a given DNA fragment is a promoter or not, based on the sequence features and statistical elements. This algorithm considers all possible pentamers formed from the nucleotides A, C, G, and T along with CpG islands, TATA box, initiator elements, and downstream promoter elements. The highlight of the algorithm is that it is not polymerase specific and can predict for both eukaryotes and prokaryotes in the same computational manner even though the underlying biological mechanisms of promoter recognition differ greatly. The proposed Method, Promoter Prediction System - PPS-CBM achieved a sensitivity, specificity, and accuracy percentages of 75.08, 83.58 and 79.33 on E. coli data set and 86.67, 88.41 and 87.58 on human data set. We have developed a tool based on PPS-CBM, the proposed algorithm, with which multiple sequences of varying lengths can be tested simultaneously and the result is reported in a comprehensive tabular format. The tool also reports the strength of the prediction.  



The tool and source code of PPS-CBM is available at



Umesh et al.   Bioinformation 10(4): 175-179 (2014)

Edited by

P Kangueane






Biomedical Informatics



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