Journal
BIOINFORMATICS
Volume 21, Issue 18, Pages 3615-3621Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bti582
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Funding
- NIGMS NIH HHS [R01 GM 068530, R01 GM 966049] Funding Source: Medline
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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 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.
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