Publications

BibTex format

@article{Miglietta:2022:10.1021/acs.analchem.2c01883,
author = {Miglietta, L and Xu, K and Chhaya, P and Kreitmann, L and Hill-Cawthorne, K and Bolt, F and Holmes, A and Georgiou, P and Rodriguez-Manzano, J},
doi = {10.1021/acs.analchem.2c01883},
journal = {Analytical Chemistry},
pages = {14159--14168},
title = {Adaptive filtering framework to remove nonspecific and low-efficiency reactions in multiplex digital PCR based on sigmoidal trends.},
url = {http://dx.doi.org/10.1021/acs.analchem.2c01883},
volume = {94},
year = {2022}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific and low-efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics
AU  - Miglietta,L
AU  - Xu,K
AU  - Chhaya,P
AU  - Kreitmann,L
AU  - Hill-Cawthorne,K
AU  - Bolt,F
AU  - Holmes,A
AU  - Georgiou,P
AU  - Rodriguez-Manzano,J
DO  - 10.1021/acs.analchem.2c01883
EP  - 14168
PY  - 2022///
SN  - 0003-2700
SP  - 14159
TI  - Adaptive filtering framework to remove nonspecific and low-efficiency reactions in multiplex digital PCR based on sigmoidal trends.
T2  - Analytical Chemistry
UR  - http://dx.doi.org/10.1021/acs.analchem.2c01883
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36190816
UR  - https://pubs.acs.org/doi/10.1021/acs.analchem.2c01883
UR  - http://hdl.handle.net/10044/1/100075
VL  - 94
ER  -

Download

ProfessorAlisonHolmes

Contact

Location

Summary

Citation

BibTex format

RIS format (EndNote, RefMan)