Pair HMM based gap statistics for re-evaluation of indels in alignments with affine gap penalties: Extended Version

TL;DR

This paper introduces a novel statistical method based on Pair Hidden Markov Models to evaluate the significance of gaps in sequence alignments, improving the reliability of indel detection especially in challenging alignment scenarios.

Contribution

It develops efficient algorithms for gap significance testing using HMMs, enhancing the evaluation of indels in affine gap penalty alignments compared to existing methods.

Findings

Indel reliability increases with gap significance.

Method performs well on structural alignments from SABmark.

Improves indel assessment in twilight zone alignments.

Abstract

Although computationally aligning sequence is a crucial step in the vast majority of comparative genomics studies our understanding of alignment biases still needs to be improved. To infer true structural or homologous regions computational alignments need further evaluation. It has been shown that the accuracy of aligned positions can drop substantially in particular around gaps. Here we focus on re-evaluation of score-based alignments with affine gap penalty costs. We exploit their relationships with pair hidden Markov models and develop efficient algorithms by which to identify gaps which are significant in terms of length and multiplicity. We evaluate our statistics with respect to the well-established structural alignments from SABmark and find that indel reliability substantially increases with their significance in particular in worst-case twilight zone alignments. This points out that our statistics can reliably complement other methods which mostly focus on the reliability of match positions.