Massively Parallel Algorithm for Multiple Sequence Alignment Based on Artificial Bee Colony

In silico biological sequence processing is a key task in molecular biology. This scientific area requires powerful computing
resources for exploring large sets of biological data. Parallel in silico simulations based on methods and algorithms for analysis of
biological data using high-performance distributed computing is essential for accelerating the research and reducing the investment.
Multiple sequence alignment is a widely used method for biological sequence processing. The goal of this method is DNA and protein sequences alignment. This paper presents an innovative parallel algorithm MSA_BG for multiple alignment of biological sequences that is highly scalable and locality aware. The MSA_BG algorithm we describe is iterative and is based on the concept of Artificial Bee Colony metaheuristics and the concept of algorithmic and architectural spaces correlation. The metaphor of the ABC
metaheuristics has been constructed and the functionalities of the agents have been defined. The conceptual parallel model of
computation has been designed and the algorithmic framework of the designed parallel algorithm constructed. Experimental
simulations on the basis of parallel implementation of MSA_BG algorithm for multiple sequences alignment on heterogeneouc
compact computer cluster and supercomputer BlueGene/P have been carried out for the case study of the influenza virus variability
investigation. The performance estimation and profiling analyses have shown that the parallel system is well balanced both in
respect to the workload and machine size.