Abstract
BackgroundAccurate bacterial genome annotations provide a framework to understanding cellular functions, behavior and pathogenicity and are essential for metabolic engineering. Annotations based only on in silico predictions are inaccurate, particularly for large, high G + C content genomes due to the lack of similarities in gene length and gene organization to model organisms.ResultsHere we describe a 2D systems biology driven re-annotation of the Saccharopolyspora erythraea genome using proteogenomics, a genome-scale metabolic reconstruction, RNA-sequencing and small-RNA-sequencing. We observed transcription of more than 300 intergenic regions, detected 59 peptides in intergenic regions, confirmed 164 open reading frames previously annotated as hypothetical proteins and reassigned function to open reading frames using the genome-scale metabolic reconstruction. Finally, we present a novel way of mapping ribosomal binding sites across the genome by sequencing small RNAs.ConclusionsThe work presented here describes a novel framework for annotation of the Saccharopolyspora erythraea genome. Based on experimental observations, the 2D annotation framework greatly reduces errors that are commonly made when annotating large-high G + C content genomes using computational prediction algorithms.
Highlights
Accurate bacterial genome annotations provide a framework to understanding cellular functions, behavior and pathogenicity and are essential for metabolic engineering
Nielsen et al found that 60% of the annotated bacterial genomes contain substantial errors in start/stop codons predictions and are generally over-annotated due to a lack of thorough analysis between computationally assigned open reading frames (ORFs) and real genes [7]
Errors in annotation are abundant in large, high G + C content genomes, where gene length and gene organization vary significantly from wellannotated model organisms such as Escherichia coli, Saccharomyces cerevisiae or Bacillus subtilis
Summary
Accurate bacterial genome annotations provide a framework to understanding cellular functions, behavior and pathogenicity and are essential for metabolic engineering. Nielsen et al found that 60% of the annotated bacterial genomes contain substantial errors in start/stop codons predictions and are generally over-annotated due to a lack of thorough analysis between computationally assigned open reading frames (ORFs) and real genes [7]. This observation has been acknowledge by the National Centre for Biotechnology Information (NCBI), which is constantly developing. These long ORFs contain a large numbers of potential start codons that lead to a considerable drop in accuracy of the translation initiation site prediction and tend to predict too many genes [9]
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