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Bioinformatics Advance Access published online on July 14, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti582
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received May 3, 2005
Revised July 5, 2005
Accepted July 12, 2005

Article

SPEM: improving multiple-sequence alignment with sequence profiles and predicted secondary structures

Hongyi Zhou 1 and Yaoqi Zhou 2*

1 Howard Hughes Medical Institute Center for Single Molecule Biophysics, Department of Physiology & Biophysics, State University of New York at Buffalo, 124 Sherman Hall, Buffalo, NY 14214, USA
2 Howard Hughes Medical Institute Center for Single Molecule Biophysics, Department of Physiology & Biophysics, State University of New York at Buffalo, 124 Sherman Hall, Buffalo, NY 14214, USA; The Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai, China

* To whom correspondence should be addressed.
Yaoqi Zhou, E-mail: yqzhou{at}buffalo.edu


  Abstract

Motivation: Multiple-sequence alignment is an essential part of bioinformatics tools for a genome-scale study of genes and their evolution relations. Making an accurate alignment between remote homologs, however, is challenging. Here, we develop a method, called SPEM, that aligns multiple sequences using pre-processed sequence profiles and predicted secondary structures for pairwise alignment, consistency-based scoring for refinement of the pairwise alignment, and a progressive algorithm for final multiple alignment.

Results: The alignment accuracy of SPEM is compared to those of established methods such as ClustalW, T-Coffee, MUSCLE, ProbCons, and PRALINEPSI in easy (homologs) and hard (remote homologs) benchmarks. Results indicate that the average sum of pairwise alignment scores given by SPEM are 7 to 15% higher than those of the methods compared in aligning remote homologs (sequence identity <30%). Its accuracy for aligning homologs (sequence identity >30%) is statistically indistinguishable from those of the state-of-the-art techniques such as ProbCons or MUSCLE 6.0.

Availability: The SPEM server and its executables are available on http://theory.med.buffalo.edu.


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