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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
Published on: August 29, 2014
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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
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.
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.
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