Communications Theory-Inspired Algorithms for Detecting Protein-Coding Regions in Prokaryotic Genomes: A Comparative Study: Using Communications Theory for Gene Detection in Prokaryotic Genomes: A Comparative Analysis of Correlation-Based Algorithms and Bayesian Classifiers

The identification of protein-coding regions in genomic DNA sequences remains a significant challenge in computational genomics, and numerous computational algorithms have been developed to address this problem. Recent advances in this field have facilitated the creation of innovative engineering methods that enable the analysis and modeling of various molecular biology processes. In this work, we propose a novel algorithm for detecting prokaryotic genes that leverages established concepts from communications theory, including correlation, the maximal ratio combining (MRC) algorithm, and filtering techniques, to generate a signal that distinguishes coding and noncoding regions based on characteristic features. The proposed algorithm is applied to multiple prokaryotic genome sequences, and Bayesian classifiers are employed to assess its performance. To further validate the proposed method, we compare its performance to that of established gene detection methods in prokaryotes, such as GLIMMER and GeneMark. This comparison underscores the value of using communications theory concepts for genomic sequence analysis and establishes the efficacy of the proposed algorithm.