Alignment Metric Accuracy

TL;DR

This paper introduces a new alignment accuracy metric and an algorithm called AMAP that improves the assessment and quality of multiple sequence alignments, especially in identifying unalignable regions and balancing sensitivity and specificity.

Contribution

The paper presents a novel accuracy metric for sequence alignments, an algorithm for maximizing expected accuracy, and demonstrates superior performance of AMAP on benchmark datasets.

Findings

The new metric improves accuracy assessment when a reference alignment is available.

The method effectively identifies and discards low-accuracy, unalignable regions.

AMAP outperforms existing multiple alignment programs on benchmarks.

Abstract

We propose a metric for the space of multiple sequence alignments that can be used to compare two alignments to each other. In the case where one of the alignments is a reference alignment, the resulting accuracy measure improves upon previous approaches, and provides a balanced assessment of the fidelity of both matches and gaps. Furthermore, in the case where a reference alignment is not available, we provide empirical evidence that the distance from an alignment produced by one program to predicted alignments from other programs can be used as a control for multiple alignment experiments. In particular, we show that low accuracy alignments can be effectively identified and discarded. We also show that in the case of pairwise sequence alignment, it is possible to find an alignment that maximizes the expected value of our accuracy measure. Unlike previous approaches based on expected accuracy alignment that tend to maximize sensitivity at the expense of specificity, our method is able to identify unalignable sequence, thereby increasing overall accuracy. In addition, the algorithm allows for control of the sensitivity/specificity tradeoff via the adjustment of a single parameter. These results are confirmed with simulation studies that show that unalignable regions can be distinguished from homologous, conserved sequences. Finally, we propose an extension of the pairwise alignment method to multiple alignment. Our method, which we call AMAP, outperforms existing protein sequence multiple alignment programs on benchmark datasets. A webserver and software downloads are available at http://bio.math.berkeley.edu/amap/ .