Faster Algorithms for Optimal Multiple Sequence Alignment Based on Pairwise Comparisons
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning Scoring Schemes for Sequence Alignment from Partial Examples
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning Models for Aligning Protein Sequences with Predicted Secondary Structure
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Multiple sequence alignment based on profile alignment of intermediate sequences
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
CONTRAlign: discriminative training for protein sequence alignment
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
A new greedy randomised adaptive search procedure for multiple sequence alignment
International Journal of Bioinformatics Research and Applications
Hi-index | 3.84 |
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 7--15% higher than those of the methods compared in aligning remote 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@buffalo.edu