Improving multiple sequence alignment biological accuracy through genetic algorithms

Authors:
Miquel Orobitg;Fernando Cores;Fernando Guirado;Concepció Roig;Cedric Notredame
Affiliations:
Department of Computer Science, Universitat de Lleida, Lleida, Spain;Department of Computer Science, Universitat de Lleida, Lleida, Spain;Department of Computer Science, Universitat de Lleida, Lleida, Spain;Department of Computer Science, Universitat de Lleida, Lleida, Spain;Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG) and Universitat Pompeu Fabra, Barcelona, Spain
Venue:
The Journal of Supercomputing
Year:
2013

Citing 2
Cited 1

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
STRIKE

Bioinformatics

Scalability and accuracy improvements of consistency-based multiple sequence alignment tools

Proceedings of the 20th European MPI Users' Group Meeting

Quantified Score

Hi-index	0.00

Visualization

Abstract

Accuracy on multiple sequence alignments (MSA) is of great significance for such important biological applications as evolution and phylogenetic analysis, homology and domain structure prediction. In such analyses, alignment accuracy is crucial. In this paper, we investigate a combined scoring function capable of obtaining a good approximation to the biological quality of the alignment. The algorithm uses the information obtained by the different quality scores in order to improve the accuracy. The results show that the combined score is able to evaluate alignments better than the isolated scores.