Letter Change Bias and Local Uniqueness in Optimal Sequence Alignments

Abstract: Considering two optimally aligned random sequences, we investigate the effect on the alignment score caused by changing a random letter in one of the two sequences. Using this idea in conjunction with large deviations theory, we show that in alignments with a low proportion of gaps the optimal alignment is locally unique in most places with high probability. This has implications in the design of recently pioneered alignment methods that use the local uniqueness as a homology indicator.