|Title||A Genetic Optimization Approach for Isolating Translational Efficiency Bias|
|Publication Type||Journal Article|
|Year of Publication||2009|
|Authors||Douglas Raiford, Dan Krane, Travis Doom, Michael Raymer|
|Journal||IEEE/ACM Transactions on Computational Biology and Bioinformatics|
|Publisher||IEEE Computer Society Press|
|Keywords||Artificial Intelligence, codon usage bias, computing methodologies, Evolutionary computing and genetic algorithms, GC-content, miscellaneous, strand bias, translational efficiency|
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 ten microbial genomes, five of which have proven particularly difficult for existing approaches due to the presence of strong confounding biases.