A novel approach to multiple sequence alignment using hadoop data grids

Multiple alignment of protein sequences is an essential tool in molecular biology. It aids to determine evolutionary linkage and to predict molecular structures. The factors to be considered while aligning multiple sequences are speed and accuracy of alignment. Dynamic programming algorithms like Needleman-Wunsch and Smith-Waterman produce accurate alignments. But these algorithms are computation intensive and are limited to a small number of short sequences. In this paper we propose a time efficient approach to sequence alignment that produces quality alignment. The dynamic nature of the algorithm coupled with data and computational parallelism of hadoop data grids improves the accuracy and speed of sequence alignment. Further due to the scalability of hadoop framework, the proposed multiple sequence alignment is also highly suited for large scale alignment problems.