SPEM: improving multiple sequence alignment with sequence profiles and predicted secondary structures.
BACKGROUND
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 PRALINE(PSI) in easy (homologs) and hard (remote homologs) benchmarks. Results indicate that the average sum of pairwise alignment scores given by SPEM are 7-15% 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.


BACKGROUND
The SPEM server and its executables are available on http://theory.med.buffalo.edu.
Zhou
Hongyi
H
Department of Physiology and Biophysics, Howard Hughes Medical Institute Center for Single Molecule Biophysics, State University of New York, Buffalo, NY 14214, USA.
Zhou
Yaoqi
Y
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D003205:Computing Methodologies; D030562:Databases, Protein; D019143:Evolution, Molecular; D020407:Internet; D017510:Protein Folding; D017433:Protein Structure, Secondary; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software