Automated Reverse Transcription Polymerase Chain Reaction Data Analysis for Sars-CoV-2 Detection

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is a current public health concern. Rapid diagnosis is crucial, and reverse transcription polymerase chain reaction (RT-PCR) is presently the reference standard for SARS-CoV-2 detection. Objective: Automated RT-PCR...

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Autores principales:	Laura Gómez-Romero, Hugo Tovar, Joaquín Moreno-Contreras, Marco A. Espinoza, Guillermo de-Anda-Jáuregui
Formato:	article
Lenguaje:	EN
Publicado:	Permanyer 2021
Materias:	Severe acute respiratory syndrome coronavirus-2 detection. Reverse transcription polymerase chain reaction. Automatic analysis. Amplification curves. Internal medicine RC31-1245
Acceso en línea:	https://doaj.org/article/74f477a6fcbd423db72effe13acd08d0
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spelling	oai:doaj.org-article:74f477a6fcbd423db72effe13acd08d02021-11-09T14:23:42ZAutomated Reverse Transcription Polymerase Chain Reaction Data Analysis for Sars-CoV-2 Detection10.24875/RIC.210001890034-83762564-8896https://doaj.org/article/74f477a6fcbd423db72effe13acd08d02021-01-01T00:00:00Zhttps://www.clinicalandtranslationalinvestigation.com/frame_esp.php?id=383https://doaj.org/toc/0034-8376https://doaj.org/toc/2564-8896Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is a current public health concern. Rapid diagnosis is crucial, and reverse transcription polymerase chain reaction (RT-PCR) is presently the reference standard for SARS-CoV-2 detection. Objective: Automated RT-PCR analysis (ARPA) is a software designed to analyze RT-PCR data for SARSCoV-2 detection. ARPA loads the RT-PCR data, classifies each sample by assessing its amplification curve behavior, evaluates the experiment’s quality, and generates reports. Methods: ARPA was implemented in the R language and deployed as a Shiny application. We evaluated the performance of ARPA in 140 samples. The samples were manually classified and automatically analyzed using ARPA. Results: ARPA had a true-positive rate = 1, true-negative rate = 0.98, positive-predictive value = 0.95, and negative-predictive value = 1, with 36 samples correctly classified as positive, 100 samples correctly classified as negative, and two samples classified as positive even when labeled as negative by manual inspection. Two samples were labeled as invalid by ARPA and were not considered in the performance metrics calculation. Conclusions: ARPA is a sensitive and specific software that facilitates the analysis of RT-PCR data, and its implementation can reduce the time required in the diagnostic pipeline. Laura Gómez-RomeroHugo TovarJoaquín Moreno-ContrerasMarco A. EspinozaGuillermo de-Anda-JáureguiPermanyerarticleSevere acute respiratory syndrome coronavirus-2 detection. Reverse transcription polymerase chain reaction. Automatic analysis. Amplification curves.Internal medicineRC31-1245ENRevista de Investigación Clínica, Vol 73, Iss 6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Severe acute respiratory syndrome coronavirus-2 detection. Reverse transcription polymerase chain reaction. Automatic analysis. Amplification curves. Internal medicine RC31-1245
spellingShingle	Severe acute respiratory syndrome coronavirus-2 detection. Reverse transcription polymerase chain reaction. Automatic analysis. Amplification curves. Internal medicine RC31-1245 Laura Gómez-Romero Hugo Tovar Joaquín Moreno-Contreras Marco A. Espinoza Guillermo de-Anda-Jáuregui Automated Reverse Transcription Polymerase Chain Reaction Data Analysis for Sars-CoV-2 Detection
description	Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is a current public health concern. Rapid diagnosis is crucial, and reverse transcription polymerase chain reaction (RT-PCR) is presently the reference standard for SARS-CoV-2 detection. Objective: Automated RT-PCR analysis (ARPA) is a software designed to analyze RT-PCR data for SARSCoV-2 detection. ARPA loads the RT-PCR data, classifies each sample by assessing its amplification curve behavior, evaluates the experiment’s quality, and generates reports. Methods: ARPA was implemented in the R language and deployed as a Shiny application. We evaluated the performance of ARPA in 140 samples. The samples were manually classified and automatically analyzed using ARPA. Results: ARPA had a true-positive rate = 1, true-negative rate = 0.98, positive-predictive value = 0.95, and negative-predictive value = 1, with 36 samples correctly classified as positive, 100 samples correctly classified as negative, and two samples classified as positive even when labeled as negative by manual inspection. Two samples were labeled as invalid by ARPA and were not considered in the performance metrics calculation. Conclusions: ARPA is a sensitive and specific software that facilitates the analysis of RT-PCR data, and its implementation can reduce the time required in the diagnostic pipeline.
format	article
author	Laura Gómez-Romero Hugo Tovar Joaquín Moreno-Contreras Marco A. Espinoza Guillermo de-Anda-Jáuregui
author_facet	Laura Gómez-Romero Hugo Tovar Joaquín Moreno-Contreras Marco A. Espinoza Guillermo de-Anda-Jáuregui
author_sort	Laura Gómez-Romero
title	Automated Reverse Transcription Polymerase Chain Reaction Data Analysis for Sars-CoV-2 Detection
title_short	Automated Reverse Transcription Polymerase Chain Reaction Data Analysis for Sars-CoV-2 Detection
title_full	Automated Reverse Transcription Polymerase Chain Reaction Data Analysis for Sars-CoV-2 Detection
title_fullStr	Automated Reverse Transcription Polymerase Chain Reaction Data Analysis for Sars-CoV-2 Detection
title_full_unstemmed	Automated Reverse Transcription Polymerase Chain Reaction Data Analysis for Sars-CoV-2 Detection
title_sort	automated reverse transcription polymerase chain reaction data analysis for sars-cov-2 detection
publisher	Permanyer
publishDate	2021
url	https://doaj.org/article/74f477a6fcbd423db72effe13acd08d0
work_keys_str_mv	AT lauragomezromero automatedreversetranscriptionpolymerasechainreactiondataanalysisforsarscov2detection AT hugotovar automatedreversetranscriptionpolymerasechainreactiondataanalysisforsarscov2detection AT joaquinmorenocontreras automatedreversetranscriptionpolymerasechainreactiondataanalysisforsarscov2detection AT marcoaespinoza automatedreversetranscriptionpolymerasechainreactiondataanalysisforsarscov2detection AT guillermodeandajauregui automatedreversetranscriptionpolymerasechainreactiondataanalysisforsarscov2detection
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Automated Reverse Transcription Polymerase Chain Reaction Data Analysis for Sars-CoV-2 Detection

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