A parallel strategy for biological sequence alignment in restricted memory space

Recently, many organisms had their DNA entirely sequenced, and this reality presents the need for aligning long DNA sequences, which is a challenging task due to its high demands for computational power and memory. The algorithm proposed by Smith–Waterman (SW) is an exact method that obtains optimal local alignments in quadratic space and time. For long sequences, quadratic complexity makes the use of this algorithm impractical. In this scenario, parallel computing is a very attractive alternative. In this paper, we propose and evaluate z-align, a parallel exact strategy based on the divergence concept to locally align long biological sequences using an affine gap function. Z-align runs in limited memory space, where the amount of memory used can be defined by the user. The results collected in a cluster with 16 processors presented very good speedups for long real DNA sequences. With z-align, we were able to compare up to 3 MBP (mega base-pairs) DNA sequences. As far as we know, this is the first time 3 MBP sequences are compared with an affine gap exact variation of the SW algorithm. Also, by comparing the results obtained with z-align and the popular BLAST tool, it is clear that z-align is able to produce longer and more significant alignments.