Extensive Review on Computational Predictions of Genomic Regulatory Sequences - Sasikala, S; Jeyalakshmi, Ratha T (International Journal of Computer Sciences and Engineering, 2025)
This paper focuses an extensive study of the existing computational work related to prediction of gene regulatory sequences and the relevant factors based on different properties. A hybrid approach is studied which combines position correlation score function and increment of diversity to elucidate signal features and composition features of sequences to improve the accuracy of promoter classifiers. It is found that Markov Model of order K is used to extract features from k-mer frequency of the sequence. Also a Support Vector Machine (SVM) is applied with the transcription signals such as Gc box, TATA box, CAAT box, NIT box and CpG islands and modified Mahalanob discriminant to predict Eukaryotic and Prokaryotic promoters. It is studied that a new approach is implemented using Artificial Neural Network (ANN) with the properties namely curvature, stacking energy and Stress Induced duplex Destabilization (SIDD). To analyze sequence characteristics of prokaryotic and eukaryotic promoters, Convolutional Neural Networks (CNN) is found to be contributing a significant role. This paper analyses the use of a tool bTSSfinder for promoter predicting models. It is identified that an algorithm exist for promoter prediction based on evolutionarily conserved sequences by concentrating AT-rich elements and G-quadruplex sequences using various statistical measures such as recall, precision, specificity accuracy and F1- scores . Algorithms using machine learning based approach are studied to discover promoters in nucleotide sequence using entropy based feature. Some of the remarkable DNA structural features such as DNA bending stiffness, duplex free energy, duplex disrupt energy, stacking energy, DNA denaturation, protein deformation, nucleosome position, propeller twist are studied. Multifarious promoter