A parallel evolutionary algorithm for RNA secondary structure prediction using stacking-energies (INN and INN-HB)

Author

Hendriks, Andrew ; Deschenes, A. ; Wiese, Kay C.

Author_Institution

Simon Fraser Univ., Burnaby, BC, Canada

fYear

2004

fDate

7-8 Oct. 2004

Firstpage

223

Lastpage

230

Abstract

This work presents a coarse-grained distributed genetic algorithm (GA) for RNA secondary structure prediction. This research builds on previous work and contains two new thermodynamic models, INN and INN-HB, which add stacking-energies using base pair adjacencies. Comparison tests were performed against the original serial GA on known structures that are 122, 543, and 784 nucleotides in length on a wide variety of parameter settings. The effects of the new models are investigated, the predicted structures are compared to known structures and the GA is compared against a serial GA with identical models. Both algorithms perform well and are able to predict structures with high accuracy for short sequences.

Keywords

biochemistry; biology computing; genetic algorithms; hydrogen bonds; macromolecules; molecular biophysics; organic compounds; stacking; INN-HB; RNA secondary structure prediction; coarse-grained distributed genetic algorithm; nucleotides; parallel evolutionary algorithm; parameter setting; stacking-energy; thermodynamic model; Amino acids; Convergence; Evolutionary computation; Molecular biophysics; Nearest neighbor searches; Predictive models; Proteins; RNA; Sequences; Thermodynamics;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Bioinformatics and Computational Biology, 2004. CIBCB '04. Proceedings of the 2004 IEEE Symposium on

Print_ISBN

0-7803-8728-7

Type

conf

DOI

10.1109/CIBCB.2004.1393957

Filename

1393957