Detection, reduction and filtration of cancer cells through a new DNA polymerization sequence approach

In Bioinformatics, a sequence alignment is a way of arranging the sequence of DNA, RNA or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. In our process, we report binding of cancer cells, normal cells and KRAS genes, and detections of high and low density of cancer cells and sequence mutations of cancer cells that can control the cancer cells in feasible tolerant matrices. The goal of this process is to explore the computational approaches to sequence alignment and mutations in a faster and optimal way by using PYMOL software with the help of filtering method which filtrate mutated DNA. This approach helps in detecting any abnormal changes and the mutation percentages of those abnormal changes and successful in reading multiple lengths of DNA sequences, detecting high density of cancer cell atoms and generating optimal alignment efficiently. In this process, we have used the idea of both the alignment techniques (Needleman Wunsch algorithm and Smith-Waterman algorithm for global alignment) which helps in generating proper alignment and comparison with our process.