Phenotypic profiling data for elucidating genomic gaps

<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

Phenotypic profiling data for elucidating genomic gaps

<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

SEED Servers: High-Performance Access to the SEED Genomes, Annotations, and Metabolic Models

<div><p>The remarkable advance in sequencing technology and the rising interest in medical and environmental microbiology, biotechnology, and synthetic biology resulted in a deluge of published microbial genomes. Yet, genome annotation, comparison, and modeling remain a major bottleneck to the translation of sequence information into biological knowledge, hence computational analysis tools are continuously being developed for rapid genome annotation and interpretation. Among the earliest, most comprehensive resources for prokaryotic genome analysis, the SEED project, initiated in 2003 as an integration of genomic data and analysis tools, now contains >5,000 complete genomes, a constantly updated set of curated annotations embodied in a large and growing collection of encoded subsystems, a derived set of protein families, and hundreds of genome-scale metabolic models. Until recently, however, maintaining current copies of the SEED code and data at remote locations has been a pressing issue. To allow high-performance remote access to the SEED database, we developed the SEED Servers (<a href="http://www.theseed.org/servers">http://www.theseed.org/servers</a>): four network-based servers intended to expose the data in the underlying relational database, support basic annotation services, offer programmatic access to the capabilities of the RAST annotation server, and provide access to a growing collection of metabolic models that support flux balance analysis. The SEED servers offer open access to regularly updated data, the ability to annotate prokaryotic genomes, the ability to create metabolic reconstructions and detailed models of metabolism, and access to hundreds of existing metabolic models. This work offers and supports a framework upon which other groups can build independent research efforts. Large integrations of genomic data represent one of the major intellectual resources driving research in biology, and programmatic access to the SEED data will provide significant utility to a broad collection of potential users.</p> </div

Architecture of the SEED servers.

<p>The client packages (currently available for Perl or Java) handle the HTTP requests and responses, and parse the data from the appropriate lightweight data exchange formats to data structures. The four servers access the SEED data.</p

Processing ids_to_sequences.

<p>(a) The ids_to_sequences function call accepts multiple IDs as an argument and uses the Sapling server to process the calls. These are returned as a single table. (b) A detailed description of each call (in this example, the ids_to_sequences) is provided online and is automatically generated from the entity-relationship models shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048053#pone-0048053-g002" target="_blank">Figure 2</a>.</p