Decoding properties of tRNA leave a detectable signal in codon usage bias.

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: alexander.roth@isb-sib.ch.

A role for codon order in translation dynamics.

The genetic code is degenerate. Each amino acid is encoded by up to six synonymous codons; the choice between these codons influences gene expression. Here, we show that in coding sequences, once a particular codon has been used, subsequent occurrences of the same amino acid do not use codons randomly, but favor codons that use the same tRNA. The effect is pronounced in rapidly induced genes, involves both frequent and rare codons and diminishes only slowly as a function of the distance between subsequent synonymous codons. Furthermore, we found that in S. cerevisiae codon correlation accelerates translation relative to the translation of synonymous yet anticorrelated sequences. The data suggest that tRNA diffusion away from the ribosome is slower than translation, and that some tRNA channeling takes place at the ribosome. They also establish that the dynamics of translation leave a significant signature at the level of the genome.

Algorithm of OMA for large-scale orthology inference.

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.

Detecting non-orthology in the COGs database and other approaches grouping orthologs using genome-specific best hits.

Correct orthology assignment is a critical prerequisite of numerous comparative genomics procedures, such as function prediction, construction of phylogenetic species trees and genome rearrangement analysis. We present an algorithm for the detection of non-orthologs that arise by mistake in current orthology classification methods based on genome-specific best hits, such as the COGs database. The algorithm works with pairwise distance estimates, rather than computationally expensive and error-prone tree-building methods. The accuracy of the algorithm is evaluated through verification of the distribution of predicted cases, case-by-case phylogenetic analysis and comparisons with predictions from other projects using independent methods. Our results show that a very significant fraction of the COG groups include non-orthologs: using conservative parameters, the algorithm detects non-orthology in a third of all COG groups. Consequently, sequence analysis sensitive to correct orthology assignments will greatly benefit from these findings.