Abstract
BackgroundEvolutionary divergence in the position of the translational start site among orthologous genes can have significant functional impacts. Divergence can alter the translation rate, degradation rate, subcellular location, and function of the encoded proteins.ResultsExisting Genbank gene maps for Burkholderia genomes suggest that extensive divergence has occurred--53% of ortholog sets based on Genbank gene maps had inconsistent gene start sites. However, most of these inconsistencies appear to be gene-calling errors. Evolutionary divergence was the most plausible explanation for only 17% of the ortholog sets. Correcting probable errors in the Genbank gene maps decreased the percentage of ortholog sets with inconsistent starts by 68%, increased the percentage of ortholog sets with extractable upstream intergenic regions by 32%, increased the sequence similarity of intergenic regions and predicted proteins, and increased the number of proteins with identifiable signal peptides.ConclusionsOur findings highlight an emerging problem in comparative genomics: single-digit percent errors in gene predictions can lead to double-digit percentages of inconsistent ortholog sets. The work demonstrates a simple approach to evaluate and improve the quality of gene maps.
Highlights
Evolutionary divergence in the position of the translational start site among orthologous genes can have significant functional impacts
We examined the nature of inconsistencies in gene start sites among Burkholderia orthologs
DNA sequence alignments showed that only 47% of the sets had consistent start sites. Given that this level of inconsistency might arise from gene-calling errors, we implemented a comparative genomics approach to assess whether consistency could be achieved
Summary
Evolutionary divergence in the position of the translational start site among orthologous genes can have significant functional impacts. When gene-coding regions are identified, one of a multitude of possible translational start sites must be selected. Gene-finding algorithms such as Glimmer [1], Genemark [2] and Prodigal [3] score each possible start site based on multiple features (e.g. start codon identity and upstream ribosome binding site), but the highest scoring site is not always the true site used in vivo. Even when the accuracy per genome is high, the aggregation of errors among groups of genomes can produce a large fraction of flawed results and significantly undermine comparative analyses
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