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Digital PCR cluster predictor: a universal R-package and shiny app for the automated analysis of multiplex digital PCR data.
De Falco, Alfonso; Olinger, Christophe M; Klink, Barbara; Mittelbronn, Michel; Stieber, Daniel.
Afiliação
  • De Falco A; National Center of Genetics (NCG), Laboratoire National de Santé (LNS), Dudelange 3555, Luxembourg.
  • Olinger CM; Faculty of Science, Technology and Medicine (FSTM), Luxembourg Center of Neuropathology (LCNP), University of Luxembourg (LNS), Belvaux 4367, Luxembourg.
  • Klink B; Department of Sports Medicine, Rehabilitation and Disease Prevention, University of Mainz, Mainz 55128, Germany.
  • Mittelbronn M; National Center of Genetics (NCG), Laboratoire National de Santé (LNS), Dudelange 3555, Luxembourg.
  • Stieber D; National Center of Genetics (NCG), Laboratoire National de Santé (LNS), Dudelange 3555, Luxembourg.
Bioinformatics ; 39(5)2023 05 04.
Article em En | MEDLINE | ID: mdl-37086434
ABSTRACT
Digital polymerase chain reaction (dPCR) is an emerging technology that enables accurate and sensitive quantification of nucleic acids. Most available dPCR systems have two channel optics, with ad hoc software limited to the analysis of single and duplex assays. Although multiplexing strategies were developed, variable assay designs, dPCR systems, and the analysis of low DNA input data restricted the ability for a universal automated clustering approach. To overcome these issues, we developed dPCR Cluster Predictor (dPCP), an R package and a Shiny app for automated analysis of up to 4-plex dPCR data. dPCP can analyse and visualize data generated by multiple dPCR systems carrying out accurate and fast clustering not influenced by the amount and integrity of input of nucleic acids. With the companion Shiny app, the functionalities of dPCP can be accessed through a web browser.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aplicativos Móveis Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aplicativos Móveis Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article