Optimal Detection of Sequence Similarity by Local Alignment

TL;DR

This paper provides a theoretical analysis of local alignment algorithms with gaps for DNA sequences, revealing scaling laws and criteria for optimal parameter selection to improve sequence similarity detection.

Contribution

It introduces a novel approach focusing on the score landscape and offers quantitative criteria for choosing optimal scoring parameters near the phase transition.

Findings

Alignment statistics differ near the phase transition with gaps versus gapless.

Optimal scores follow robust scaling laws for uncorrelated sequences.

Deviation from scaling laws indicates sequence homology.

Abstract

The statistical properties of local alignment algorithms with gaps are analyzed theoretically for uncorrelated and correlated DNA sequences. In the vicinity of the log-linear phase transition, the statistics of alignment with gaps is shown to be characteristically different from that of gapless alignment. The optimal scores obtained for uncorrelated sequences obey certain robust scaling laws. Deviation from these scaling laws signals sequence homology, and can be used to guide the empirical selection of scoring parameters for the optimal detection of sequence similarities. This can be accomplished in a computationally efficient way by using a novel approach focusing on the score landscape. Furthermore, by assuming a few gross features characterizing the statistics of underlying sequence-sequence correlations, quantitative criteria are obtained for the choice of optimal scoring parameters: Optimal similarity detection is most likely to occur in a region close to the log side of the log-linear phase transition.