Phenotypic profiling data for elucidating genomic gaps
Daniel A. Cuevas
Daniel Garza
Savannah E. Sanchez
Jason Rostron
Chris S. Henry
Veronika Vonstein
Ross A. Overbeek
Anca Segall
Forest Rohwer
Elizabeth Dinsdale
Robert A. Edwards
10.6084/m9.figshare.3969072.v1
https://f1000.figshare.com/articles/dataset/Phenotypic_profiling_data_for_elucidating_genomic_gaps/3969072
<p>Dataset 1. Raw OD600 growth curves (raw_od_curves.csv).</p><p>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.</p><p>Dataset 2. Parameters for logistic curves (curve_logistic_parameters.csv).</p><p>Lag, maximum growth rate, and carrying capacity parameters for the 96 wells. Sum-squared error and growth level are included.</p><p>Dataset 3. C.sedlakii KBase phenotypes (c.sedlakii_phenotypes.csv).</p><p>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.</p><p>Dataset 4. (C. sedlakii_nogapfill.sbml)</p><p>The initial metabolic model of Citrobacter sedlakii built solely from the functional annotations.</p><p>Dataset 5. (C.sedlakii_ArgonneLB_gapfill.sbml)</p><p>The initial metabolic model of Citrobacter sedlakii with reactions identified by the gap-fill algorithm on the LB media
condition.</p><p>Dataset 6. (C.sedlakii_MAP_gapfill.sbml)</p><p>The LB-gap-filled model with reactions identified by
the gap-fill algorithm on the MAPs media conditions.</p>
2016-10-17 15:52:46
phenotypic profiles
genomic gaps
Bacterial growth
Bioinformatics
Genetics