Ant Target Algorithm: A Novel DNA Target Assembling Technique Using Optimized Graph and Ant Colony Optimization

Target genome reassembling technique allows us to analyze specific regions of DNA, which facilitates clinical correction or disease detection. Here we have developed a methodology that focuses on the target region or region of interest. We have introduced a new methodology, called Ant-Target Algorithm (ATA), for target genome assembling using Ant Colony Optimization (ACO) and optimized graph, where we have used only the reads of the original sequence which has a length up to 109 bp. A section of reads are aligned to the reference target location and the remaining reads are rejected. Later aligned reads are stitched and reassembled. We have considered data sequence from NCBI and Ensembl BioMart, which have been obtained from organisms, like human, mouse, cat, zebrafish, dog, chimpanzee, lion, tiger, monkey, tortoise, camel, chicken, goat, duck, crow, Budgerigar bird, Atlantic salmon fish, blue whale, catfish and COVID virus. ATA has reported a maximum average error of 0.1% per sample, whereas existing technologies have reported an error of >0.1%.