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twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis
SUMMARY: Droplet Digital PCR (ddPCR) is a sensitive platform used to quantify specific nucleic acid molecules amplified by polymerase chain reactions. Its sensitivity makes it particularly useful for the detection of rare mutant molecules, such as those present in a sample of circulating free tumour...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860069/ https://www.ncbi.nlm.nih.gov/pubmed/28475662 http://dx.doi.org/10.1093/bioinformatics/btx308 |
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author | Chiu, Anthony Ayub, Mahmood Dive, Caroline Brady, Ged Miller, Crispin J |
author_facet | Chiu, Anthony Ayub, Mahmood Dive, Caroline Brady, Ged Miller, Crispin J |
author_sort | Chiu, Anthony |
collection | PubMed |
description | SUMMARY: Droplet Digital PCR (ddPCR) is a sensitive platform used to quantify specific nucleic acid molecules amplified by polymerase chain reactions. Its sensitivity makes it particularly useful for the detection of rare mutant molecules, such as those present in a sample of circulating free tumour DNA obtained from cancer patients. ddPCR works by partitioning a sample into individual droplets for which the majority contain only zero or one target molecule. Each droplet then becomes a reaction chamber for PCR, which through the use of fluorochrome labelled probes allows the target molecules to be detected by measuring the fluorescence intensity of each droplet. The technology supports two channels, allowing, for example, mutant and wild type molecules to be detected simultaneously in the same sample. As yet, no open source software is available for the automatic gating of two channel ddPCR experiments in the case where the droplets can be grouped into four clusters. Here, we present an open source R package ‘twoddpcr’, which uses Poisson statistics to estimate the number of molecules in such two channel ddPCR data. Using the Shiny framework, an accompanying graphical user interface (GUI) is also included for the package, allowing users to adjust parameters and see the results in real-time. AVAILABILITY AND IMPLEMENTATION: twoddpcr is available from Bioconductor (3.5) at https://bioconductor.org/packages/twoddpcr/. A Shiny-based GUI suitable for non-R users is available as a standalone application from within the package and also as a web application at http://shiny.cruk.manchester.ac.uk/twoddpcr/. PACKAGE MAINTAINER: anthony.chiu@cruk.manchester.ac.uk |
format | Online Article Text |
id | pubmed-5860069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-58600692018-03-23 twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis Chiu, Anthony Ayub, Mahmood Dive, Caroline Brady, Ged Miller, Crispin J Bioinformatics Applications Notes SUMMARY: Droplet Digital PCR (ddPCR) is a sensitive platform used to quantify specific nucleic acid molecules amplified by polymerase chain reactions. Its sensitivity makes it particularly useful for the detection of rare mutant molecules, such as those present in a sample of circulating free tumour DNA obtained from cancer patients. ddPCR works by partitioning a sample into individual droplets for which the majority contain only zero or one target molecule. Each droplet then becomes a reaction chamber for PCR, which through the use of fluorochrome labelled probes allows the target molecules to be detected by measuring the fluorescence intensity of each droplet. The technology supports two channels, allowing, for example, mutant and wild type molecules to be detected simultaneously in the same sample. As yet, no open source software is available for the automatic gating of two channel ddPCR experiments in the case where the droplets can be grouped into four clusters. Here, we present an open source R package ‘twoddpcr’, which uses Poisson statistics to estimate the number of molecules in such two channel ddPCR data. Using the Shiny framework, an accompanying graphical user interface (GUI) is also included for the package, allowing users to adjust parameters and see the results in real-time. AVAILABILITY AND IMPLEMENTATION: twoddpcr is available from Bioconductor (3.5) at https://bioconductor.org/packages/twoddpcr/. A Shiny-based GUI suitable for non-R users is available as a standalone application from within the package and also as a web application at http://shiny.cruk.manchester.ac.uk/twoddpcr/. PACKAGE MAINTAINER: anthony.chiu@cruk.manchester.ac.uk Oxford University Press 2017-09-01 2017-05-05 /pmc/articles/PMC5860069/ /pubmed/28475662 http://dx.doi.org/10.1093/bioinformatics/btx308 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Chiu, Anthony Ayub, Mahmood Dive, Caroline Brady, Ged Miller, Crispin J twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis |
title | twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis |
title_full | twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis |
title_fullStr | twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis |
title_full_unstemmed | twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis |
title_short | twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis |
title_sort | twoddpcr: an r/bioconductor package and shiny app for droplet digital pcr analysis |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860069/ https://www.ncbi.nlm.nih.gov/pubmed/28475662 http://dx.doi.org/10.1093/bioinformatics/btx308 |
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