MOTIVATION: The standard genetic code translates 61 codons into 20 amino acids using fewer than 61 transfer RNAs (tRNAs). This is possible because of the tRNA's ability to 'wobble' at the third base to decode more than one codon. Although the anticodon-codon mapping of tRNA to mRNA is a prerequisite for certain codon usage indices and can contribute to the understanding of the evolution of alternative genetic codes, it is usually not determined experimentally because such assays are prohibitively expensive and elaborate. Instead, the codon reading is approximated from theoretical inferences of nucleotide binding, the wobble rules. Unfortunately, these rules fail to capture all of the nuances of codon reading. This study addresses the codon reading properties of tRNAs and their evolutionary impact on codon usage bias. RESULTS: Using three different computational methods, the signal of tRNA decoding in codon usage bias is identified. The predictions by the methods generally agree with each other and compare well with experimental evidence of codon reading. This analysis suggests a revised codon reading for cytosolic tRNA in the yeast genome (Saccharomyces cerevisiae) that is more accurate than the common assignment by wobble rules. The results confirm the earlier observation that the wobble rules are not sufficient for a complete description of codon reading, because they depend on genome-specific factors. The computational methods presented here are applicable to any fully sequenced genome. AVAILABILITY: By request from the author. CONTACT: [email protected].

Since the publication of our article (Roth, Gonnet, and Dessimoz: BMC Bioinformatics 2008 9: 518), we have noticed several errors, which we correct in the following.

BACKGROUND: OMA is a project that aims to identify orthologs within publicly available, complete genomes. With 657 genomes analyzed to date, OMA is one of the largest projects of its kind. RESULTS: The algorithm of OMA improves upon standard bidirectional best-hit approach in several respects: it uses evolutionary distances instead of scores, considers distance inference uncertainty, includes many-to-many orthologous relations, and accounts for differential gene losses. Herein, we describe in detail the algorithm for inference of orthology and provide the rationale for parameter selection through multiple tests. CONCLUSION: OMA contains several novel improvement ideas for orthology inference and provides a unique dataset of large-scale orthology assignments.