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Background  Since ancient times the filamentous fungus  Aspergillus oryzae  has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of  A. oryzae  with 12,074 annotated genes was released but the number of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other related fungi. Here we proposed the gene prediction by construction of an  A. oryzae  Expressed Sequence Tag (EST) library, sequencing and assembly. We enhanced the function assignment by our developed annotation strategy. The resulting better annotation was used to reconstruct the metabolic network leading to a genome scale metabolic model of  A. oryzae .    Results  Our assembled EST sequences we identified 1,046 newly predicted genes in the  A. oryzae  genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted in assignment of new putative functions to 1,469 hypothetical proteins already present in the  A. oryzae  genome database. Using the substantially improved annotated genome we reconstructed the metabolic network of  A. oryzae . This network contains 729 enzymes, 1,314 enzyme-encoding genes, 1,073 metabolites and 1,846 (1,053 unique) biochemical reactions. The metabolic reactions are compartmentalized into the cytosol, the mitochondria, the peroxisome and the extracellular space. Transport steps between the compartments and the extracellular space represent 281 reactions, of which 161 are unique. The metabolic model was validated and shown to correctly describe the phenotypic behavior of  A. oryzae  grown on different carbon sources.    Conclusion  A much enhanced annotation of the  A. oryzae  genome was performed and a genome-scale metabolic model of  A. oryzae  was reconstructed. The model accurately predicted the growth and biomass yield on different carbon sources. The model serves as an important resource for gaining further insight into our understanding of  A. oryzae  physiology.

Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae