Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics

The alignment operation between many protein sequences or DNA sequences related to the scientific bioinformatics application is very complex. There is a trade-off in the objectives in the existing techniques of Multiple Sequence Alignment (MSA). The techniques that concern with speed ignore accuracy, whereas techniques that concern with accuracy ignore speed. The term alignment means to get the similarity in different sequences with high accuracy. The more growing number of sequences leads to a very complex and complicated problem. Because of the emergence; rapid development; and dependence on gene sequencing, sequence alignment has become important in every biological relationship analysis process. Calculating the number of similar amino acids is the primary method for proving that there is a relationship between two sequences. The time is a main issue in any alignment technique. In this paper, a more effective MSA method for handling the massive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method depends on Artificial Fish Swarm (AFS) algorithm that can break down the most challenges of MSA problems. The AFS is exploited to obtain high accuracy in adequate time. ASF has been increasing popularly in various applications such as artificial intelligence, computer vision, machine learning, and data-intensive application. It basically mimics the behavior of fish trying to get the food in nature. The proposed mechanisms of AFS that is like preying, swarming, following, moving, and leaping help in increasing the accuracy and concerning the speed by decreasing execution time. The sense organs that aid the artificial fishes to collect information and vision from the environment help in concerning the accuracy. These features of the proposed AFS make the alignment operation more efficient and are suitable especially for large-scale data. The implementation and experimental results put the proposed AFS as a first choice in the queue of alignment compared to the well-known algorithms in multiple sequence alignment.