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Characterization of a Pathogenic Escherichia coli Strain Derived from Oreochromis spp. Farms Using Whole-Genome Sequencing
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Pathogen metadata platform: software for accessing and analyzing pathogen strain information.

Wenling E Chang1, Matthew W Peterson2, Christopher D Garay2

  • 1Data Analytics Department, The MITRE Corporation, 2280 Historic Decatur Rd, San Diego, CA, 92106, USA.

BMC Bioinformatics
|September 17, 2016
PubMed
Summary
This summary is machine-generated.

A new platform simplifies pathogen metadata analysis for biosurveillance and public health. It provides easy data access, querying, and visualizations for non-bioinformaticians.

Keywords:
BioSampleBiosurveillanceGeocodingJavaLabKeyMetadataPathogenPostgreSQL

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Area of Science:

  • Microbiology and Bioinformatics
  • Public Health and Epidemiology
  • Data Science

Background:

  • Pathogen metadata, including collection location, time, and environment, is crucial for research, biosurveillance, and public health.
  • Genomic nucleotide sequence data combined with metadata is rapidly expanding.
  • Existing tools for analyzing pathogen metadata are often complex, requiring bioinformatics expertise and lacking user-friendly summaries and visualizations.

Purpose of the Study:

  • To develop a user-friendly platform for accessing, summarizing, and visualizing pathogen metadata.
  • To facilitate data analysis for disease outbreak investigations and public health initiatives.

Main Methods:

  • Designed a software platform incorporating a PostgreSQL database for pathogen metadata.
  • Developed scripts to download and parse data from NCBI BioSample and BioProject.
  • Integrated a user interface for metadata querying and standardized result export.
  • Implemented 2D histograms and geolocated mapping for visual data summarization.
  • Built the platform on the open-source LabKey data platform.

Main Results:

  • The platform enables easy creation and population of a local pathogen metadata database from NCBI.
  • Users can query metadata and obtain standardized, exportable results.
  • Visualizations include 2D histograms of selected metadata and mapping of geolocated data points.
  • Demonstrated successful querying for pathogen serovar and genome sequence identifiers.

Conclusions:

  • The developed software empowers users to manage and analyze pathogen metadata effectively.
  • Modular components, like the database, can be integrated into other data platforms.
  • The source code is freely available, promoting further development and application.