Issue No. 02 - March/April (2011 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.24
Douglas W. Raiford , Southern Methodist University, Dallas
Dan E. Krane , Wright State University, Dayton
Travis E.W. Doom , Wright State University, Dayton
Michael L. Raymer , Wright State University, Dayton
The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution, phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most well-studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both genomic sequence information and prior knowledge of a set of highly expressed genes. This novel approach provides more accurate results from sequence information alone by resisting the confounding effects of other biases. We validate this increase in accuracy in isolating translational efficiency bias on 10 microbial genomes, five of which have proven particularly difficult for existing approaches due to the presence of strong confounding biases.
Codon usage bias, evolutionary computing and genetic algorithms, miscellaneous, artificial intelligence, computing methodologies, GC-content, strand bias, translational efficiency.
D. E. Krane, T. E. Doom, M. L. Raymer and D. W. Raiford, "A Genetic Optimization Approach for Isolating Translational Efficiency Bias," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 342-352, 2009.