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The ITS2 Database
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SnapperDB: a database solution for routine sequencing analysis of bacterial isolates.

Timothy Dallman1, Philip Ashton1,2, Ulf Schafer3

  • 1Gastrointestinal Bacteria Reference Unit, National Infections Service, Public Health England, London, UK.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

SnapperDB v1.0 offers scalable tools for storing bacterial variant data and performing routine single nucleotide polymorphism (SNP) analysis. This enables reproducible surveillance of infectious diseases using whole genome sequencing.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Infectious Disease Surveillance

Background:

  • Real-time infectious disease surveillance using whole genome sequencing (WGS) faces challenges in data analysis and communication.
  • Storing and analyzing bacterial variant data for large populations requires efficient and scalable solutions.

Purpose of the Study:

  • To introduce SnapperDB, a set of tools for storing bacterial variant data and enabling reproducible, scalable analysis of microbial populations.
  • To present the 'SNP address' nomenclature for describing isolate relationships at single nucleotide resolution.
  • To announce the release of SnapperDB v1.0 for routine single nucleotide polymorphism (SNP) analysis and data storage.

Main Methods:

  • Development of SnapperDB, a Python application for bacterial variant data management.
  • Implementation of a 'SNP address' system for precise isolate comparison.
  • Creation of scalable infrastructure for routine SNP analysis of microbial populations.

Main Results:

  • SnapperDB v1.0 provides a robust platform for storing and analyzing bacterial variant data.
  • The 'SNP address' nomenclature allows for detailed characterization of isolate relatedness.
  • The system facilitates scalable and reproducible microbial population analysis.

Conclusions:

  • SnapperDB v1.0 addresses key challenges in infectious disease surveillance by providing efficient data management and analysis tools.
  • The software supports reproducible and scalable genomic epidemiology.
  • Accessible via open-source license, promoting widespread adoption in public health.