Letter Change Bias and Local Uniqueness in Optimal Sequence Alignments

TL;DR

This paper studies how changing a single letter affects optimal sequence alignments and shows that in low-gap alignments, the optimal alignment is mostly locally unique, which aids in homology detection.

Contribution

It introduces a probabilistic analysis linking letter changes to local uniqueness in optimal alignments, informing new alignment method designs.

Findings

Optimal alignments with few gaps are mostly locally unique.

Changing a random letter has a predictable impact on alignment scores.

Local uniqueness can serve as an indicator of homology.

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.