Grammar-based distance in progressive multiple sequence alignment

David J Russell,Hasan H Otu,Khalid Sayood

doi:10.1186/1471-2105-9-306

David J Russell, Hasan H Otu + Show 1 more

Open Access

https://doi.org/10.1186/1471-2105-9-306

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Abstract

BackgroundWe propose a multiple sequence alignment (MSA) algorithm and compare the alignment-quality and execution-time of the proposed algorithm with that of existing algorithms. The proposed progressive alignment algorithm uses a grammar-based distance metric to determine the order in which biological sequences are to be pairwise aligned. The progressive alignment occurs via pairwise aligning new sequences with an ensemble of the sequences previously aligned.ResultsThe performance of the proposed algorithm is validated via comparison to popular progressive multiple alignment approaches, ClustalW and T-Coffee, and to the more recently developed algorithms MAFFT, MUSCLE, Kalign, and PSAlign using the BAliBASE 3.0 database of amino acid alignment files and a set of longer sequences generated by Rose software. The proposed algorithm has successfully built multiple alignments comparable to other programs with significant improvements in running time. The results are especially striking for large datasets.ConclusionWe introduce a computationally efficient progressive alignment algorithm using a grammar based sequence distance particularly useful in aligning large datasets.

Highlights

We propose a multiple sequence alignment (MSA) algorithm and compare the alignment-quality and execution-time of the proposed algorithm with that of existing algorithms
MSAs are generally used in studying phylogeny of organisms, structure prediction, and identifying segments of interest among many other applications in computational biology [3]
The resulting aligned FASTA files from each algorithm were scored using bali score, a program provided with the BAliBASE distribution that generates a Sum-of-Pairs (SP) score and a Total-Column (TC) score based on predetermined reference alignments

Summary

Introduction

We propose a multiple sequence alignment (MSA) algorithm and compare the alignment-quality and execution-time of the proposed algorithm with that of existing algorithms. The proposed progressive alignment algorithm uses a grammar-based distance metric to determine the order in which biological sequences are to be pairwise aligned. Generation of meaningful multiple sequence alignments (MSAs) of biological sequences is a well-studied NP-complete problem, which has significant implications for a wide spectrum of applications [1,2]. MSAs are generally used in studying phylogeny of organisms, structure prediction, and identifying segments of interest among many other applications in computational biology [3]. Given a scoring scheme to evaluate the fitness of an MSA, calculating the best MSA is an NP-complete problem [1]. Variances in scoring schemes, need for expert-hand analysis in most applications, and many-to-one mapping governing elements-to-functionality (codon mapping and function) make MSA a more challenging problem when considered from a biological context as well [4]

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Conclusion