Therefore, there is a high need for faster sequence analysis algorithms.

This plethora of sequence information increases the computation and time requirements for genome comparisons in computational biology. Recently, large amounts of sequence data produced by next-generation sequencing techniques have become available in private and public databases, which has created new challenges due to the limitations associated with alignment based approaches. Therefore, the alignable homologous segments of the genomes under study have to be identified in the initial steps. Moreover, factors such as the combinatorics of genomic rearrangements and duplications make the alignment of entire genomes impossible. Whole genome alignment of higher eukaryotes can exceed computational resources. However, various limitations are encountered when analyzing large datasets using an alignment based approach. Phylogenetic reconstruction and comparative sequence analysis traditionally depend on multiple or pairwise sequence alignments. Phylogenetic tree analysis and comparative studies of taxa are essential parts of modern molecular biology. The results indicate that the fuzzy integral algorithm is an efficient and feasible alignment-free method for sequence analysis on the genomic scale. Our method was tested on eight benchmark datasets and on in-house generated datasets (18 s rDNA sequences from 11 arbuscular mycorrhizal fungi (AMF) and 16 s rDNA sequences of 40 bacterial isolates from plant interior).

This matrix is used as an input for the neighbor program in the PHYLIP package for phylogenetic tree construction. These estimated Markov chain parameters were used to calculate similarity among all pairwise combinations of DNA sequences based on a fuzzy integral algorithm. The method estimate the parameters of a Markov chain by considering the frequencies of occurrence of all possible nucleotide pairs from each DNA sequence. The novelty of our approach is the inclusion of fuzzy integral with Markov chain for sequence analysis in the alignment-free model. In this study, we developed an alignment-free algorithm for faster sequence analysis. So, there is a high need for faster sequence analysis algorithms. A larger amount of sequence data in private and public databases produced by next-generation sequencing put new challenges due to limitation associated with the alignment-based method for sequence comparison.

Identity matrix in bioedit