Wavelet Transform-Based Phylogenetic Analysis of Protein Sequences

With the acceleration of gene sequencing studies, many biological data emerges. By analyzing these data, it contributes greatly to the studies on understanding the metabolic disorders in the organism and increasing the efficiency of the drugs. For this purpose, it is critical to classify the data in a way that is accurate, fast and low-cost according to its characteristics and relationships. Besides experimental methods, machine learning and bioinformatics methods are used. Artificial neural networks, support vector machines, flexible calculation methods are frequently used methods. However, the effectiveness of these methods on biosecence data depends on the method of using the method with the most appropriate parameters and converting protein sequences into numerical sequences. When the sequences are transformed with amino acid frequencies, the properties of amino acids are ignored. For this purpose, handling the physicochemical (hydrophobicity, hydrophilicity ...) properties of amino acids increases the performance of classification techniques. The phylogenetic tree is the best method to visualize the classification among species. In the project, the wavelet transform used in the analysis of digital signals has been adapted to protein sequences defined by hydrophobicity values. Each protein sequence was defined to correspond to a signal, the wavelet transform was divided into approach and detail components, and the similarities between them were calculated, and the phylogenetic tree of the species was created. As an application, phylogenetic trees of ND5 protein sequences of 22 species were created in the MatlabR2017 program of NeighborJoining (NJ) and Unweighed Pair Group Method of Aritmetic Averages (UPGMA) methods