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.AvailabilityThe tool and source code of PPS-CBM is available at http://keralabs.org