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Phenotypic profiling data for elucidating genomic gaps

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posted on 2016-10-17, 15:52 authored by Daniel A. Cuevas, Daniel GarzaDaniel Garza, Savannah E. Sanchez, Jason Rostron, Chris S. Henry, Veronika Vonstein, Ross A. Overbeek, Anca Segall, Forest Rohwer, Elizabeth Dinsdale, Robert A. Edwards

Dataset 1. Raw OD600 growth curves (raw_od_curves.csv).

MAPs optical density measurements from the plate reader for 96 wells. Numbered headers indicate the time (hrs) and the column contents indicate the OD600 measurement.

Dataset 2. Parameters for logistic curves (curve_logistic_parameters.csv).

Lag, maximum growth rate, and carrying capacity parameters for the 96 wells. Sum-squared error and growth level are included.

Dataset 3. C.sedlakii KBase phenotypes (c.sedlakii_phenotypes.csv).

Phenotype csv file required for KBase phenotype simulations. This file specifies media data object name, the KBase workspace, and growth. The gene knockout and additional compound columns were not used and set to none.

Dataset 4. (C. sedlakii_nogapfill.sbml)

The initial metabolic model of Citrobacter sedlakii built solely from the functional annotations.

Dataset 5.  (C.sedlakii_ArgonneLB_gapfill.sbml)

The initial metabolic model of Citrobacter sedlakii with reactions identified by the gap-fill algorithm on the LB media condition.

Dataset 6. (C.sedlakii_MAP_gapfill.sbml)

The LB-gap-filled model with reactions identified by the gap-fill algorithm on the MAPs media conditions.