  The translation of comparative genomics into clinical decision support tools
often depends on the quality of sequence alignments. However, currently used
methods of multiple sequence alignments suffer from significant biases and
problems with aligning diverged sequences. The objective of this study was to
develop and test a new multiple sequence alignment (MSA) algorithm suitable for
the high-throughput comparative analysis of different microbial genomes. This
algorithm employs an innovative tensor indexing method for partitioning the
dynamic programming hyper-cube space for parallel processing. We have used the
clinically relevant task of identifying regions that determine resistance to
antibiotics to test the new algorithm and to compare its performance with
existing MSA methods. The new method "mmDst" performed better than existing MSA
algorithms for more divergent sequences because it employs a simultaneous
alignment scoring recurrence, which effectively approximated the score for edge
missing cell scores that fall outside the scoring region.
