Paper Info
Title
SPEM: improving multiple sequence alignment with sequence profiles and predicted secondary structures.
Abstract
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.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.The SPEM server and its executables are available on http://theory.med.buffalo.edu.
Year
DOI
Venue
2005
10.1093/bioinformatics/bti582
Bioinformatics
Keywords
Field
DocType
accurate alignment,spem server,remote homologs,multiple sequence alignment,alignment accuracy,final multiple alignment,pre-processed sequence profile,secondary structure,pairwise alignment,pairwise alignment score,aligns multiple sequence,multiple alignment
Pairwise comparison,Data mining,Alignment-free sequence analysis,Computer science,Bioinformatics,Multiple sequence alignment
Journal
Volume
Issue
ISSN
21
18
1367-4803
Citations 
PageRank 
References 
27
1.79
15
Authors
2
Name
Order
Citations
PageRank
Hongyi Zhou1271.79
Yaoqi Zhou21098.72