Probabilistic Approaches to Alignment with Tandem Repeats

TL;DR

This paper introduces a pair hidden Markov model that effectively incorporates short tandem repeats into sequence alignment, improving accuracy over traditional models through novel decoding algorithms.

Contribution

It presents a new tractable pair HMM framework for aligning sequences with tandem repeats and develops specialized decoding algorithms for enhanced accuracy.

Findings

Our model outperforms classical three-state pair HMM in simulations.

Decoding algorithms based on gain functions improve alignment accuracy.

Block-based decoding algorithms are effective for tandem repeat regions.

Abstract

We propose a simple tractable pair hidden Markov model for pairwise sequence alignment that accounts for the presence of short tandem repeats. Using the framework of gain functions, we design several optimization criteria for decoding this model and describe the resulting decoding algorithms, ranging from the traditional Viterbi and posterior decoding to block-based decoding algorithms specialized for our model. We compare the accuracy of individual decoding algorithms on simulated data and find our approach superior to the classical three-state pair HMM in simulations.