Bioinformatics Advance Access originally published online on July 14, 2005
Bioinformatics 2005 21(18):3615-3621; doi:10.1093/bioinformatics/bti582
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SPEM: improving multiple sequence alignment with sequence profiles and predicted secondary structures
1Department of Physiology and Biophysics, Howard Hughes Medical Institute Center for Single Molecule Biophysics, State University of New York at Buffalo 124 Sherman Hall, Buffalo, NY 14214, USA
2The Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University Shanghai, China
*To whom correspondence should be addressed.
Motivation: Multiple sequence alignment is an essential part of bioinformatics tools for a genome-scale study of genes and their evolution relations. However, making an accurate alignment between remote homologs 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 with 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 715% 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
Contact: yqzhou{at}buffalo.edu
Received on May 3, 2005; revised on July 5, 2005; accepted on July 12, 2005
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