Fast Statistical Alignment

Robert K Bradley,Ian Holmes,Colin Dewey,Adam Roberts,Michael Smoot,Lior Pachter,Sudeep Juvekar,Jaeyoung Do,Adam Siepel

doi:10.1371/journal.pcbi.1000392

Abstract

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment—previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches—yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/.

Highlights

The field of biological sequence alignment is very active, with numerous new alignment programs developed every year in response to increasing demand driven by rapidly-dropping sequencing costs
Each type of benchmark is vulnerable to manipulation and may not represent the problem setups which are most relevant to biologists
The result is that biologists are confronted with many programs and publications, but it is frequently unclear which approach will give the best results for the everyday problems which they seek to address

Summary

Introduction

The field of biological sequence alignment is very active, with numerous new alignment programs developed every year in response to increasing demand driven by rapidly-dropping sequencing costs. The ClustalW program [1,2], published in 1994, remains the most widely-used multiple sequence alignment program. In a recent review of multiple sequence alignment [3], the authors remark that ‘‘to the best of our knowledge, no significant improvements have been made to the [ClustalW] algorithm since 1994 and several modern methods achieve better performance in accuracy, speed, or both.’’ it is natural to ask, ‘‘Why do alignment programs continue to be developed, and why are new tools not more widely adopted by biologists?’’. The result is that biologists are confronted with many programs and publications, but it is frequently unclear which approach will give the best results for the everyday problems which they seek to address

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