Ortholog prediction of the Aspergillus genus applicable for synthetic biology

Jane Lind Nybo Rasmussen, Tammi Camilla Vesth, Sebastian Theobald, Jens Christian Frisvad, Igor V. Grigoriev, Scott E. Baker, Mikael Rørdam Andersen

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    The Aspergillus genus contains leading industrial microorganisms, excelling in producing bioactive compounds and enzymes. Using synthetic biology and bioinformatics, we aim to re-engineer these organisms for applications within human health, pharmaceuticals, environmental engineering, and food production. In this project, we compare the genomes of +300 species from the Aspergillus genus to generate a high-resolution pan-genomic map, representing genetic diversity spanning ~200 million years. We are identifying genes specific to species and clades to allow for guilt-by-association-based mapping of genotype-to-phenotype. To achieve this, we have developed orthologous protein prediction software that utilizes genus-wide genetic diversity. The approach is optimized for large data sets, based on BLASTp considering protein identity and alignment coverage, and clustering using single linkage of bi-directional hits. The result is orthologous protein families describing the genomic and functional features of individual species, clades and the core/pan genome of Aspergillus; and applicable to genotype-to-phenotype analyses in other microbial genera.
    Original languageEnglish
    Publication date2016
    Number of pages1
    Publication statusPublished - 2016
    EventCell Symposia: Technology. Biology. Data Science 2016 - 41 Tunnel Road, Berkeley, United States
    Duration: 9 Oct 201611 Oct 2016
    Conference number: 1


    ConferenceCell Symposia: Technology. Biology. Data Science 2016
    Location41 Tunnel Road
    CountryUnited States
    Internet address

    Cite this

    Rasmussen, J. L. N., Vesth, T. C., Theobald, S., Frisvad, J. C., Grigoriev, I. V., Baker, S. E., & Andersen, M. R. (2016). Ortholog prediction of the Aspergillus genus applicable for synthetic biology. Abstract from Cell Symposia: Technology. Biology. Data Science 2016, Berkeley, United States.