While genomic sequencing projects have proved an abundant source of information for biological studies ranging from the molecular to the ecological in scale, much of the information present may yet be hidden from casual analysis. One such information source, trends in codon usage, can provide a wealth of information about an organism's genes and their expression. Degeneracy in the genetic code causes more than one codon to code for the same amino acid, and usage of these codons is often biased such that one or more of these synonymous codons is preferred. Detection of this bias is an important tool in the analysis of genomic data, particularly as a predictor of gene expressivity. There are many algorithmic methods for identifying bias in genomic data, all of which are susceptible to being obscured by the presence of factors simultaneously influencing codon selection. Presented here is a new technique for removing the effects of confounding factors and of visualizing the bias data through the use of a bias landscape. This technique is shown to successfully isolate expressivity-related codon usage trends where other techniques fail due to the presence of confounding influences.
Available at: http://works.bepress.com/michael_raymer/21/