Prediction of Sphingosine protein-coding regions with a self adaptive spectral rotation method

DNA numerical encoding schemes for exon prediction: a recent history

Bioinformatics in the present day has been firmly established as a regulator in genomics. In recent times, applications of Signal processing in exon prediction have gained a lot of attention. The exons carry protein information. Proteins are composed of connected constituents known as amino acids that characterize the specific function. Conversion of the nucleotide character string into a numerical sequence is the gateway before analyzing it through signal processing methods. This numeric encoding is the mathematical descriptor of nucleotides and is based on some statistical properties of the structure of nucleic acids. Since the type of encoding extremely affects the exon detection accuracy, this paper is devised for the review of existing encoding (mapping) schemes. The comparative analysis is formulated to emphasize the importance of the genetic code setting of amino acids considered for application related to computational elucidation for exon detection. This work covers much helpful information for future applications.

New Method for Potential Fusions Detection in Protein-Coding Sequences

Gene fusion is known to be one of the mechanisms of a new gene formation. Most bioinformatics methods for studying fused genes are based on the sequence similarity search. However, if the ancestral sequences were lost during evolution or changed too much, it is impossible to detect the fusion. Previously, we have developed a method of searching for triplet periodicity (TP) change points in protein-coding sequences (CDS) and showed the possible relation of this phenomenon with gene formation as a result of fusion. In this study, we improved the TP change point detection method and studied the genes of six eukaryotic genomes. At the level of 2%-3% of the probability of type I error, TP change points were found in 20%-40% of genes. Further analysis showed that about 30% of the TP change points can be explained by amino acid repeats. Another 30% can be potentially fused genes, alignment for which was detected by the BLAST program. We believe that the rest of the results can be fused genes, the ancestral sequences for which have been lost. The method is more sensitive to TP changes and allowed us to find up to two to three times more cases of significant TP change points than our previous method.