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ACS Synth Biol. 2017 Dec 15;6(12):2248-2259. doi: 10.1021/acssynbio.7b00204. Epub 2017 Sep 8.

The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization.

Author information

1
DOE Joint BioEnergy Institute , Emeryville, California 94608, United States.
2
Biotechnology and Bioengineering and Biomass Science and Conversion Department, Sandia National Laboratories , Livermore, California 94550, United States.
3
DOE Agile BioFoundry , Emeryville, California 94608, United States.
4
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States.
5
Department of Chemical and Biomolecular Engineering, University of California , Berkeley, California 94720, United States.
6
Department of Bioengineering, University of California , Berkeley, California 94720, United States.
7
Novo Nordisk Foundation Center for Biosustainability, Technical University Denmark , DK2970 Horsholm, Denmark.
8
Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States.
9
DNA Synthesis Science Program, DOE Joint Genome Institute , Walnut Creek, California 94598, United States.
10
BCAM, Basque Center for Applied Mathematics , 48009 Bilbao, Spain.

Abstract

Although recent advances in synthetic biology allow us to produce biological designs more efficiently than ever, our ability to predict the end result of these designs is still nascent. Predictive models require large amounts of high-quality data to be parametrized and tested, which are not generally available. Here, we present the Experiment Data Depot (EDD), an online tool designed as a repository of experimental data and metadata. EDD provides a convenient way to upload a variety of data types, visualize these data, and export them in a standardized fashion for use with predictive algorithms. In this paper, we describe EDD and showcase its utility for three different use cases: storage of characterized synthetic biology parts, leveraging proteomics data to improve biofuel yield, and the use of extracellular metabolite concentrations to predict intracellular metabolic fluxes.

KEYWORDS:

-omics data; data mining; data standards; database; flux analysis; synthetic biology

PMID:
28826210
DOI:
10.1021/acssynbio.7b00204
[Indexed for MEDLINE]
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