Skip to Main Content
With the enormous amount of data that is available in the public domain, signal processing concepts are playing an important role in genomic and proteomic data processing. The binary sequence method is well known in the literature for genomic signal processing. As an alternative to the binary sequence method, this paper presents a new view to the genomic sequence processing with the electron-ion interaction potential (EIIP) values for nucleotides. The efficacy of the EIIP values-based method is demonstrated through implementation results for the prediction of the gene F56F11.4 with five exons and for cystic-fibrosis gene identification. The biological function of a protein is determined by the amino-acid sequence within the protein. Identification of the amino acids (hot spots) that contribute to the characteristic frequency characterizing a particular biological function is an important task in the protein sequence analysis. Hence, using EIIP values for amino acids, this paper presents a continuous-wavelet transform approach using the modified Morlet wavelet for the identification of active sites (hot spots) in a protein sequence. The efficacy of the approach is illustrated through implementation results on hemoglobin human alpha, human immunodeficiency virus, and oncogene protein sequences.