Operon Prediction Using Chaos Embedded Particle Swarm Optimization
| Autor: | Cheng-Huei Yang, Jui-Hung Tsai, Li-Yeh Chuang, Cheng-Hong Yang |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Genetics
Fitness function Operon Applied Mathematics Chromosome Mapping Particle swarm optimization Genomics Sequence Analysis DNA Bacterial genome size Computational biology Bacillus subtilis Biology biology.organism_classification Genome Nonlinear Dynamics bacteria Gene Algorithms Genome Bacterial Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 10:1299-1309 |
| ISSN: | 1545-5963 |
| DOI: | 10.1109/tcbb.2013.63 |
| Popis: | Operons contain valuable information for drug design and determining protein functions. Genes within an operon are co-transcribed to a single-strand mRNA and must be coregulated. The identification of operons is, thus, critical for a detailed understanding of the gene regulations. However, currently used experimental methods for operon detection are generally difficult to implement and time consuming. In this paper, we propose a chaotic binary particle swarm optimization (CBPSO) to predict operons in bacterial genomes. The intergenic distance, participation in the same metabolic pathway and the cluster of orthologous groups (COG) properties of the Escherichia coli genome are used to design a fitness function. Furthermore, the Bacillus subtilis, Pseudomonas aeruginosa PA01, Staphylococcus aureus and Mycobacterium tuberculosis genomes are tested and evaluated for accuracy, sensitivity, and specificity. The computational results indicate that the proposed method works effectively in terms of enhancing the performance of the operon prediction. The proposed method also achieved a good balance between sensitivity and specificity when compared to methods from the literature. |
| Databáze: | OpenAIRE |
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