Altmetric

Framework for DNA quantification and outlier detection using multidimensional standard curves

File Description SizeFormat 
ac9b01466_si_001.pdfSupporting information237.26 kBAdobe PDFView/Open
acs.analchem.9b01466.pdfPublished version2.41 MBAdobe PDFView/Open
Title: Framework for DNA quantification and outlier detection using multidimensional standard curves
Authors: Moniri, A
Rodriguez-Manzano, J
Malpartida-Cardenas, K
Yu, L-S
Didelot, X
Holmes, A
Georgiou, P
Item Type: Journal Article
Abstract: Real-time PCR is a highly sensitive and powerful technology for the quantification of DNA and has become the method of choice in microbiology, bioengineering, and molecular biology. Currently, the analysis of real-time PCR data is hampered by only considering a single feature of the amplification profile to generate a standard curve. The current “gold standard” is the cycle-threshold (Ct) method which is known to provide poor quantification under inconsistent reaction efficiencies. Multiple single-feature methods have been developed to overcome the limitations of the Ct method; however, there is an unexplored area of combining multiple features in order to benefit from their joint information. Here, we propose a novel framework that combines existing standard curve methods into a multidimensional standard curve. This is achieved by considering multiple features together such that each amplification curve is viewed as a point in a multidimensional space. Contrary to only considering a single-feature, in the multidimensional space, data points do not fall exactly on the standard curve, which enables a similarity measure between amplification curves based on distances between data points. We show that this framework expands the capabilities of standard curves in order to optimize quantification performance, provide a measure of how suitable an amplification curve is for a standard, and thus automatically detect outliers and increase the reliability of quantification. Our aim is to provide an affordable solution to enhance existing diagnostic settings through maximizing the amount of information extracted from conventional instruments.
Issue Date: 6-May-2019
Date of Acceptance: 6-May-2019
URI: http://hdl.handle.net/10044/1/70470
DOI: https://dx.doi.org/10.1021/acs.analchem.9b01466
ISSN: 0003-2700
Publisher: American Chemical Society (ACS)
Start Page: 7426
End Page: 7434
Journal / Book Title: Analytical Chemistry
Volume: 91
Issue: 11
Copyright Statement: © 2019 American Chemical Society. This is an open access article published under a Creative Commons Attribution (CC-BY)License, which permits unrestricted use, distribution and reproduction in any medium,provided the author and source are cited.
Sponsor/Funder: National Institute for Health Research
National Institute for Health Research
Funder's Grant Number: NF-SI-0617-10176
RDF04
Keywords: 0301 Analytical Chemistry
0904 Chemical Engineering
0399 Other Chemical Sciences
Analytical Chemistry
Publication Status: Published
Open Access location: https://pubs.acs.org/doi/10.1021/acs.analchem.9b01466
Article Number: acs.analchem.9b01466
Online Publication Date: 2019-05-14
Appears in Collections:Electrical and Electronic Engineering
Department of Medicine
Faculty of Medicine
Epidemiology, Public Health and Primary Care



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons