Effect of mutations on the detection of translational signals based on a communications theory approach

Gene and regulatory sequence identification is the first step in the functional annotation of any genome. Identification and annotation of such elements in the genome is a fundamental challenge in genomics and computational biology. Since regulatory elements are often short and variable, their identification and discovery using computational algorithms is difficult. However, significant advances have been made in the computational methods for modeling and detection of DNA regulatory elements. This paper proposes a novel use of techniques and principles from communications engineering and coding theory for modeling, identification and analysis of genomic regulatory elements and biological sequences. The last 13 bases sequence in the 16S rRNA molecule was used as a test sequence and was detected using the proposed models. Results show that the proposed models are not only able to identify this regulatory element (RE) in the mRNA sequence, but also can help identify coding from noncoding regions. The models described in this work where used to study the effect of mutations in the last 13 bases sequence of the 16S rRNA molecule. The obtained results showed total agreement with published investigations on mutations which further certify the biological relevance of the proposed models.