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:
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.
Code from this repository is available under the BSD-3-Clause-LBNL license.
Omics Mock Generator Library (OMG) Copyright (c) 2021, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.
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