Identification of Coding Regions in Prokaryotic DNA Sequences Using Bayesian Classification

The identification of protein-coding regions in genomic DNA sequences is a well-known problem in computational genomics. Various computational algorithms can be employed to achieve the identification process. The rapid advances in this field have motivated the development of innovative engineering methods that allow for further analysis and modeling of many processes in molecular biology. The proposed algorithm utilizes well-known concepts in communications theory, such as correlation, the maximal ratio combining (MRC) algorithm, and filtering techniques to create a signal whose maxima and minima indicate coding and noncoding regions, respectively. The proposed algorithm investigates several prokaryotic genome sequences. Two Bayesian classifiers are designed to test and evaluate the performance of the proposed algorithm. The obtained simulation results prove that the algorithm can efficiently and accurately detect protein-coding regions, which is being demonstrated by the obtained sensitivity and specificity values that are comparable to well-known gene detection methods in prokaryotes. The obtained results further verify the correctness and the biological relevance of using communications theory concepts for genomic sequence analysis.