Novel approaches to alignment-free comparison in DNA sequence analysis

The fact that traditional biological and statistical methods are insufficient in the processing of large-scale biological data, which has emerged as a result of the rapid development of technology, has made the subject one of the focal points of the field of computer science. Similarity is one of the key processes of DNA sequence analysis in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction, and sequence retrieving, it is necessary to perform similarity calculations. As an alternative to alignment-based sequence comparison methods that have high computational costs, alignment-free methods have emerged that by digitizing the sequence in a different space, calculate the similarity with the new mathematical identifiers utilized. In this thesis, novel approaches are proposed in alignment-free DNA sequence similarity analysis. With the proposed methods, feature extraction processes are designed to be used to determine similarities between DNA sequences. Three different datasets used in the literature are discussed to test the effect of methods analysing independently of length on different sequences of DNA. The relations of the feature vectors obtained with similarity metrics are defined and compared with phylogenetic trees used as reference. As a result of the tests, it is seen that the results obtained by the proposed methods are successful and acceptable.