MAY 14, 2020 10:00 AM PDT

Massively Parallel Genome Engineering followed by pooled growth selections for rapid target discovery in microbes

Sponsored by: Inscripta
Speaker
  • Senior Scientist, Computational Biology, Inscripta
    Biography
      Stephen Federowicz is a bioinformatician and systems biologist with over 5 years of industrial experience in NGS analysis, network biology, algorithm development, and machine learning. He is passionate about developing genome-scale understandings of organism function via high-throughput experimentation and computational techniques. Dr. Federowicz is a Senior Computational Scientist in Inscriptas Applications Divison where he is responsible for the analysis and interpretation of the complex datasets generated from the Onyx Digital Genome Engineering Platform. Before joining Inscripta Stephen held positions at Roche and Intrexon. He received his undergraduate degree in Bioinformatics from UC Santa Cruz and his PhD in Bioinformatics from UC San Diego.

    Abstract
    DATE:  May 14, 2020
    TIME:  10:00am PT, 1:00pm ET
     
    Massively parallel genome engineering enables rapid and simultaneous evaluation of genotype-phenotype relationships at a genomic scale. With the Inscripta Onyx™ Platform we replaced every promoter in the E. coli genome with one of five synthetic constitutive promoters across an expression ladder from low to high relative strength. Additionally, we generated two versions of a genome scale knockout library by inserting three premature stop codons in every gene at two different positions near the 5’ end. We then pooled these libraries for a total of 23,576 genomic edits, and under strong selective pressure quantified shifts in the edited populations to determine relative strain performance.
     
    From this experiment we readily identify thousands of genotype-phenotype interactions that confirm known mechanisms and reveal large sets of novel interactions in coordinated functional modules. These results demonstrate the power of high efficiency automated genome engineering and encourage a future in which any research group can readily engage in both new target discovery and obtain a global view of the relevant genotype-phenotype interactions in their system of study.
     
     
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