Visualizing Codon Usage Within and Across Genomes: Concepts and Tools

Abstract

The sheer complexity and diversity of life forms on Earth hinge on a genetic code. The latter is set of rules that define how nucleotide sequence (information) is converted into proteins (workhorses of cellularmetabolism) in course of ribosomal protein synthesis. InmRNA, four nucleotide bases can form 64 possible trinucleotide sequences or codons. Each codon corresponds to a specific amino acid (sense codons) or stop signal in the process of protein synthesis. There are 61 sense codons, while natural proteins are built of only 20 amino acids. Hence, the extant genetic code, theoretically, may encode three times as many different amino acids. Elucidation of the origin of the genetic code and driving forces behind the evolution of protein-coding sequences are of great fundamental and applied interest. Much has been achieved in these areas since the discovery of mRNA triplet structure over 50 years ago, leading to a refined set of algorithms and tools for the analysis of codon evolution [1]. Wide adoption of next-generation sequencing technologies since 2005 [2–4] has led to tremendous growth of nucleotide sequence databases, allowing application of these tools to virtually any biological problem. Larger datasets might indeed provide insights into function and evolution of coding sequences.