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Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control
Published on: March 30, 2015
shinyCurves, a shiny web application to analyse multisource qPCR amplification data: a COVID-19 case study
S Olaechea-Lázaro1, I García-Santisteban1,2, J R Pineda3,4
1Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940, Leioa, Spain.
shinyCurves is a free, user-friendly software for analyzing quantitative reverse transcription PCR (qRT-PCR) data. It automates the process for large volumes of samples, aiding in viral detection during pandemics.
Area of Science:
- Molecular Biology
- Bioinformatics
- Virology
Background:
- Quantitative, reverse transcription PCR (qRT-PCR) is the gold-standard for viral detection, including SARS-CoV-2.
- Manual analysis of qRT-PCR data is feasible for small lab volumes but insufficient for high-throughput needs during pandemics.
Purpose of the Study:
- To develop an automated, integrative software solution for analyzing large volumes of qRT-PCR data.
- To provide a user-friendly platform for accurate viral detection from multi-plate and multi-platform qRT-PCR experiments.
Main Methods:
- Development of shinyCurves, a free, online, shiny-based software application.
- Implementation of automated data analysis for qRT-PCR amplification and melting curves.
- Inclusion of user-defined settings for sample classification (Positive, Negative, Undetermined).
Main Results:
- shinyCurves enables analysis of qRT-PCR data from diverse formats without programming knowledge.
- The software automatically classifies samples and generates comprehensive amplification and melting curve plots.
- Visual inspection of results is facilitated through detailed curve plots.
Conclusions:
- shinyCurves offers a flexible, integrative, and user-friendly solution for high-throughput qRT-PCR data analysis.
- The software accelerates the evaluation of massive datasets from various sources.
- Automated generation and evaluation of curve plots enhance the efficiency of viral detection.
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