Scaling statistical multiple sequence alignment to large datasets

Michael Nute,Tandy Warnow

doi:10.1186/s12864-016-3101-8

Abstract

BackgroundMultiple sequence alignment is an important task in bioinformatics, and alignments of large datasets containing hundreds or thousands of sequences are increasingly of interest. While many alignment methods exist, the most accurate alignments are likely to be based on stochastic models where sequences evolve down a tree with substitutions, insertions, and deletions. While some methods have been developed to estimate alignments under these stochastic models, only the Bayesian method BAli-Phy has been able to run on even moderately large datasets, containing 100 or so sequences. A technique to extend BAli-Phy to enable alignments of thousands of sequences could potentially improve alignment and phylogenetic tree accuracy on large-scale data beyond the best-known methods today.ResultsWe use simulated data with up to 10,000 sequences representing a variety of model conditions, including some that are significantly divergent from the statistical models used in BAli-Phy and elsewhere. We give a method for incorporating BAli-Phy into PASTA and UPP, two strategies for enabling alignment methods to scale to large datasets, and give alignment and tree accuracy results measured against the ground truth from simulations. Comparable results are also given for other methods capable of aligning this many sequences.ConclusionsExtensions of BAli-Phy using PASTA and UPP produce significantly more accurate alignments and phylogenetic trees than the current leading methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3101-8) contains supplementary material, which is available to authorized users.

Highlights

Multiple sequence alignment is an important task in bioinformatics, and alignments of large datasets containing hundreds or thousands of sequences are increasingly of interest
We explore phylogenetic accuracy of maximum likelihood (ML) trees computed on these alignments using the Robinson-Foulds (RF) error rate, where the RF error is the percentage of the non-trivial bipartitions in the true tree that are missing from the estimated tree
Bold numbers indicate best performing method produces less accurate alignments than the PASTA variants we study, but is much more accurate than MAFFT run in default mode

Summary

Introduction

Multiple sequence alignment is an important task in bioinformatics, and alignments of large datasets containing hundreds or thousands of sequences are increasingly of interest. A technique to extend BAli-Phy to enable alignments of thousands of sequences could potentially improve alignment and phylogenetic tree accuracy on large-scale data beyond the best-known methods today. One of the most accurate approaches to multiple sequence alignment is statistical estimation under stochastic models of sequence evolution where sequences evolve down trees with insertions and deletions (jointly referred to as indels) and substitutions. While many methods have this approach [7,8,9,10], BAli-Phy is the best-known, and the main such method that is used to estimate an alignment and phylogeny from unaligned sequences; [11] is the initial paper on this method, but subsequent publications extended and improved the statistical models on which the method is based

Methods

Results

Conclusion