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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
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Rapid infectious disease identification by next-generation DNA sequencing.

Jeremy E Ellis1, Dara S Missan1, Matthew Shabilla1

  • 1Fry Laboratories, L.L.C., 15720 N. Greenway-Hayden Loop STE 3, Scottsdale, AZ 85260, United States.

Journal of Microbiological Methods
|September 24, 2016
PubMed
Summary

The new Rapid Infectious Disease Identification (RIDI™) system automates Next-Generation Sequencing (NGS) data analysis for faster pathogen detection. This NGS tool accurately identifies organisms in clinical samples, improving upon traditional culture methods.

Keywords:
Clinical NGSCommunity profilingNGS validationNext generation DNA sequencingRapid infectious disease identification

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Area of Science:

  • Clinical microbiology
  • Bioinformatics
  • Genomics

Background:

  • Culture-based methods for identifying infectious organisms are slow and have limitations.
  • Next-Generation Sequencing (NGS) offers potential for rapid pathogen identification but lacks standardized automated data processing.
  • A need exists for automated, user-friendly systems to analyze NGS data for clinical diagnostics.

Purpose of the Study:

  • To develop and evaluate the Rapid Infectious Disease Identification (RIDI™) system, an automated software solution for processing NGS data.
  • To enable rapid and accurate identification of infectious organisms directly from clinical samples using NGS.
  • To overcome the deficiencies of current culture-based methods and manual NGS data analysis.

Main Methods:

  • Development of the RIDI™ system, featuring automated data format detection, analysis configuration, and quality control.
  • Integration with major NGS platforms and utilization of NCBI and RIDI™-specific databases for sequence characterization.
  • Validation using American Type Culture Collection (ATCC) reference standards of 27 species, individually and in combinations, and simulated clinical samples.

Main Results:

  • The RIDI™ system achieved rapid detection of known organisms in under 12 hours with multi-sample throughput.
  • Accurate identification rates of 99.5% at the genus-level and 75.3% at the species-level for reference standards.
  • Demonstrated a limit of detection of 146 cells/ml and capability to identify components in polymicrobial samples with acceptable discrepancy rates.

Conclusions:

  • The RIDI™ system provides an effective, automated solution for rapid infectious disease identification using NGS data.
  • The system's speed and accuracy show potential to surpass current methods, particularly in time-sensitive clinical scenarios.
  • RIDI™ can improve patient outcomes by enabling faster diagnosis and treatment decisions compared to traditional culture methods.