We present a set of tools that, combined, provide the ability to store, visualize and leverage multiomics data to predict the outcome of bioengineering efforts.
We show how to use:
- ICE to store strain information
- OMG to generate a mock dataset of omics data
- EDD to store experiment data and metadata
- ART to leverage these data to suggest new experiments that improve isoprenol production.
By combining these tools with Jupyter notebooks we show how to pinpoint genetic modifications that improve production of isoprenol, a potential biofuel, in a simulated data set. We expect the same procedures to be applicable in the case of real experimental data.
Provided notebooks run on two different Jupyter lab kernels, for which we provide a set of requirements in the kernel_requirements directory.
Running the notebooks requires the ART and OMG libraries. In addition, OMG relies on a commercial package CPLEX, for which academic licenses are available. Academic and commercial licenses for ART are available upon request.
Clone this repository to your local machine:
git clone https://github.com/AgileBioFoundry/multiomicspaper.git
Step-by-step instructions for guiding metabolic engineering via multiomics data and machine learning using a set of notebooks and screencasts are provided here.
Roy S., Radivojević T. et al. Multiomics data collection, visualization, and utilization for guiding metabolic engineering (biorxiv 2020)
Code from this repository is available under the BSD-3-Clause-LBNL license.