Experiment Data Depot
A repository for Learn-friendly formatting of multi-omics measurements
The Experiment Data Depot (EDD) is a web-based service for storing and visualizing data collected in biological experiments, including measurements, protocols, and metadata.
EDD provides a novel way to keep all actionable data from an experiment in a single place, where this data can be conveniently browsed and quality checked. EDD hosts different types of data (proteomics, metabolomics, transcriptomics, etc.) so as to provide actionable information. EDD output is standardized into different output types that can be immediately used for modeling the systems.
EDD provides a standardized description of experiments and significantly facilitates the Test and Learn phases in the Design-Build-Test-Learn (DBTL) cycle.
EDD is publicly available as part of an open source project. Learn more about EDD and create an account.
USE CASE
EDD was used to store the data used to train ART (ABF, left) and EVOLVE (Teselagen, Right) to predict promoter combinations that led to a 105% increase in tryptophan productivity.
Cross-validated predictions vs average of measured GFP synthesis rate (proxy for tryptophan productivity) for ART(e) and EVOLVE (f) respectively. Data are shown for library and control strains (gray markers; green markers show starting strain), as well as for recommended strains (blue markers; orange markers show recommendations that overlap between the two approaches). R-square values are for cross-validated predictions for the whole dataset (not only training set data). MFI = fluorescence intensity.
Publications:
- The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization
- Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering
- A machine learning Automated Recommendation Tool for synthetic biology
- Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning
- Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism