Sequence alignment and mutual information

TL;DR

This paper introduces a robust method to estimate mutual information from global sequence alignments, potentially providing an objective measure for sequence similarity and alignment quality in computational bioscience.

Contribution

The authors present a simple, flexible approach to estimate mutual information from global alignments, aligning well with estimates from unrelated methods and enabling objective evaluation of alignment quality.

Findings

Mutual information estimates from alignments closely match those from sequence concatenation and zipping.

The approach provides a consistent, model-independent measure of sequence similarity.

Potential applications include assessing alignment quality and significance objectively.

Abstract

Background: Alignment of biological sequences such as DNA, RNA or proteins is one of the most widely used tools in computational bioscience. All existing alignment algorithms rely on heuristic scoring schemes based on biological expertise. Therefore, these algorithms do not provide model independent and objective measures for how similar two (or more) sequences actually are. Although information theory provides such a similarity measure -- the mutual information (MI) -- previous attempts to connect sequence alignment and information theory have not produced realistic estimates for the MI from a given alignment. Results: Here we describe a simple and flexible approach to get robust estimates of MI from {\it global} alignments. For mammalian mitochondrial DNA, our approach gives pairwise MI estimates for commonly used global alignment algorithms that are strikingly close to estimates obtained by an entirely unrelated approach -- concatenating and zipping the sequences. Conclusions: This remarkable consistency may help establish MI as a reliable tool for evaluating the quality of global alignments, judging the relative merits of different alignment algorithms, and estimating the significance of specific alignments. We expect that our approach can be extended to establish further connections between information theory and sequence alignment, including applications to local and multiple alignment procedures.