Molecular Detection Mapping and Analysis Platform for R (MDMAPR) facilitating the standardization, analysis, visualization, and sharing of qPCR data and metadata
Author(s) -
Jiaojia Yu,
Robert G. Young,
Lorna E. Deeth,
Robert Hanner
Publication year - 2020
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.9974
Subject(s) - metadata , standardization , visualization , computer science , data sharing , data visualization , computational biology , data mapping , data mining , information retrieval , data science , database , world wide web , biology , medicine , operating system , alternative medicine , pathology
Quantitative polymerase chain reaction (qPCR) has been used as a standard molecular detection tool in many scientific fields. Unfortunately, there is no standard method for managing published qPCR data, and those currently used generally focus on only managing raw fluorescence data. However, associated with qPCR experiments are extensive sample and assay metadata, often under-examined and under-reported. Here, we present the Molecular Detection Mapping and Analysis Platform for R (MDMAPR), an open-source and fully scalable informatics tool for researchers to merge raw qPCR fluorescence data with associated metadata into a standard format, while geospatially visualizing the distribution of the data and relative intensity of the qPCR results. The advance of this approach is in the ability to use MDMAPR to store varied qPCR data. This includes pathogen and environmental qPCR species detection studies ideally suited to geographical visualization. However, it also goes beyond these and can be utilized with other qPCR data including gene expression studies, quantification studies used in identifying health dangers associated with food and water bacteria, and the identification of unknown samples. In addition, MDMAPR’s novel centralized management and geospatial visualization of qPCR data can further enable cross-discipline large-scale qPCR data standardization and accessibility to support research spanning multiple fields of science and qPCR applications.
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