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Evaluation validation of a qPCR curve analysis method and conventional approaches
BACKGROUND: Reverse Transcription quantitative polymerase chain reaction (RT-qPCR) is a sensitive and reliable method for mRNA quantification and rapid analysis of gene expression from a large number of starting templates. It is based on the statistical significance of the beginning of exponential p...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596907/ https://www.ncbi.nlm.nih.gov/pubmed/34789146 http://dx.doi.org/10.1186/s12864-021-07986-4 |
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author | Zhang, Yashu Li, Hongping Shang, Shucheng Meng, Shuoyu Lin, Ting Zhang, Yanhui Liu, Haixing |
author_facet | Zhang, Yashu Li, Hongping Shang, Shucheng Meng, Shuoyu Lin, Ting Zhang, Yanhui Liu, Haixing |
author_sort | Zhang, Yashu |
collection | PubMed |
description | BACKGROUND: Reverse Transcription quantitative polymerase chain reaction (RT-qPCR) is a sensitive and reliable method for mRNA quantification and rapid analysis of gene expression from a large number of starting templates. It is based on the statistical significance of the beginning of exponential phase in real-time PCR kinetics, reflecting quantitative cycle of the initial target quantity and the efficiency of the PCR reaction (the fold increase of product per cycle). RESULTS: We used the large clinical biomarker dataset and 94-replicates-4-dilutions set which was published previously as research tools, then proposed a new qPCR curve analysis method——C(q)MAN, to determine the position of quantitative cycle as well as the efficiency of the PCR reaction and applied in the calculations. To verify algorithm performance, 20 genes from biomarker and partial data with concentration gradients from 94-replicates-4-dilutions set of MYCN gene were used to compare our method with various publicly available methods and established a suitable evaluation index system. CONCLUSIONS: The results show that C(q)MAN method is comparable to other methods and can be a feasible method which applied to our self-developed qPCR data processing and analysis software, providing a simple tool for qPCR analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-07986-4. |
format | Online Article Text |
id | pubmed-8596907 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85969072021-11-17 Evaluation validation of a qPCR curve analysis method and conventional approaches Zhang, Yashu Li, Hongping Shang, Shucheng Meng, Shuoyu Lin, Ting Zhang, Yanhui Liu, Haixing BMC Genomics Research BACKGROUND: Reverse Transcription quantitative polymerase chain reaction (RT-qPCR) is a sensitive and reliable method for mRNA quantification and rapid analysis of gene expression from a large number of starting templates. It is based on the statistical significance of the beginning of exponential phase in real-time PCR kinetics, reflecting quantitative cycle of the initial target quantity and the efficiency of the PCR reaction (the fold increase of product per cycle). RESULTS: We used the large clinical biomarker dataset and 94-replicates-4-dilutions set which was published previously as research tools, then proposed a new qPCR curve analysis method——C(q)MAN, to determine the position of quantitative cycle as well as the efficiency of the PCR reaction and applied in the calculations. To verify algorithm performance, 20 genes from biomarker and partial data with concentration gradients from 94-replicates-4-dilutions set of MYCN gene were used to compare our method with various publicly available methods and established a suitable evaluation index system. CONCLUSIONS: The results show that C(q)MAN method is comparable to other methods and can be a feasible method which applied to our self-developed qPCR data processing and analysis software, providing a simple tool for qPCR analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-07986-4. BioMed Central 2021-11-16 /pmc/articles/PMC8596907/ /pubmed/34789146 http://dx.doi.org/10.1186/s12864-021-07986-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhang, Yashu Li, Hongping Shang, Shucheng Meng, Shuoyu Lin, Ting Zhang, Yanhui Liu, Haixing Evaluation validation of a qPCR curve analysis method and conventional approaches |
title | Evaluation validation of a qPCR curve analysis method and conventional approaches |
title_full | Evaluation validation of a qPCR curve analysis method and conventional approaches |
title_fullStr | Evaluation validation of a qPCR curve analysis method and conventional approaches |
title_full_unstemmed | Evaluation validation of a qPCR curve analysis method and conventional approaches |
title_short | Evaluation validation of a qPCR curve analysis method and conventional approaches |
title_sort | evaluation validation of a qpcr curve analysis method and conventional approaches |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596907/ https://www.ncbi.nlm.nih.gov/pubmed/34789146 http://dx.doi.org/10.1186/s12864-021-07986-4 |
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