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Automated Reverse Transcription Polymerase Chain Reaction Data Analysis for Sars-CoV-2 Detection
Laura Gómez-Romero1, Hugo Tovar2, Joaquín Moreno-Contreras3
1Division of Computing/Systems Genomics, Instituto Nacional de Medicina Genómica, Mexico City, Mexico.
Summary
Automated RT-PCR analysis (ARPA) software accurately detects SARS-CoV-2 from RT-PCR data. This tool enhances diagnostic speed and reliability for public health.
Area of Science:
- Medical diagnostics
- Bioinformatics
- Infectious disease research
Background:
- The COVID-19 pandemic necessitates rapid and accurate SARS-CoV-2 detection.
- Reverse transcription polymerase chain reaction (RT-PCR) is the gold standard for SARS-CoV-2 diagnosis.
Purpose of the Study:
- To introduce Automated RT-PCR Analysis (ARPA), a software for analyzing RT-PCR data.
- To assess the performance and utility of ARPA in SARS-CoV-2 detection.
Main Methods:
- ARPA was developed using the R language and deployed as a Shiny application.
- The software was evaluated on 140 samples, comparing its automated analysis to manual classification.
Main Results:
- ARPA demonstrated high accuracy with a true-positive rate of 1 and a true-negative rate of 0.98.
- The software achieved a positive-predictive value of 0.95 and a negative-predictive value of 1.
- Two samples were misclassified as positive when manually negative, and two were flagged as invalid.
Conclusions:
- ARPA is a sensitive and specific tool for RT-PCR data analysis.
- Implementing ARPA can significantly expedite the SARS-CoV-2 diagnostic process.
