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

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

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

Sampling Sites

<p>The circles represent the sampling locations in the Sargasso Sea (SAR), Gulf of Mexico (GOM), British Columbia (BBC), and the Arctic Ocean. The number of samples taken at each location and combined for sequencing, as well as the date and depth range, are shown in the boxes.</p

Types of Phages in the Four Metagenomes

<p>A new version of the Phage Proteomic Tree (left panel) was constructed from 510 complete phage and prophage genomes using the previously described method [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0040368#pbio-0040368-b023" target="_blank">23</a>]. The metagenomic sequences were compared to the phage on the Phage Proteomic Tree using TBLASTX, and the colored bars on the right represent significant similarities (<i>E</i>-value < 0.0001). Names of prophages are in red and the <i>Prochlorococcus</i> phage genomes are in green. An electronic version of the tree and a FASTA list of phage and prophage genomes used to make the tree are available at the SDSU Center for Universal Microbe Sequencing website (<a href="http://scums.sdsu.edu/phage/Oceans" target="_blank">http://scums.sdsu.edu/phage/Oceans</a>).</p

Monte Carlo Simulation of Cross-Contigs between Metagenomic Samples

<p>(A) For the intersample analysis, the maximum likelihood occurred at 35% fraction permuted and 100% fraction shared. (B) The maximum likelihood was between 0% and 0.5% fraction permuted and 85% and 95 % fraction shared for the intrasample controls.</p

Relationship between Geographic and Genetic Distances of Marine Viral Assemblages

<p>In addition to the four metagenomes sequenced for this study, the previous viral metagenomes from the San Diego area (California coast) were also included in this analysis [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0040368#pbio-0040368-b010" target="_blank">10</a>]. There was a significant correlation of 3.28 × 10⁻⁵ Φ_ST / km (Mantel test, <i>Z</i> = ₋78.9, <i>p</i> < 0.017, <i>r</i> = 0.585).</p

Additional file 1 of Single vs. multiple fraction non-inferiority trial of stereotactic ablative radiotherapy for the comprehensive treatment of oligo-metastases/progression: SIMPLIFY-SABR-COMET

Additional file 1: Appendix 1. BC CANCER PROVINCIAL SABR ORGAN-AT-RISK (OAR) CONSTRAINTS. Appendix 2. Reasonable reirradiation SBRT doses to the thecal sac Pmax following common initial conventional radiotherapy regimens