Back

qPyCR: A qPCR Data Analyzer for Template Quantification and the Detection of Performance Outliers

Moore, S. D.

2026-10-09 bioinformatics
10.64898/2026.10.08.757329 bioRxiv
Show abstract

Motivation: Real-time qPCR data are commonly analyzed to obtain a Cq values, which are used to compare the relative abundance of a targeted template. However, improper background subtraction distorts Cq and changing the assigned threshold can alter experimental conclusions. In addition, a Cq values provide no information on the quality of the qPCR for a given sample. Alternatively, global fitting of qPCR data provides agnostic quantification without the use of Cq or thresholds, and the fitting terms provide a useful metric of reaction performance. Current implementations of global fitting are limited to custom in-house programs. Results: qPyCR is comprehensive analysis tool written in Python that performs background signal subtractions, computes a threshold, and reports Cq values in a traditional manner. The program also performs global fitting and reports template abundances along with reaction quality values. The quality values can be used to assign performance standards, which allow researchers to distinguish aberrant reactions from true changes in template abundance. Availability and Implementation: Code and example data are freely available on GitHub (https://github.com/sdmoore-labs/qPyCR) and on Zenodo (https://doi.org/10.5281/zenodo.21115342).

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
BMC Bioinformatics
○ 457 papers in training set
Top 0.1%
26.6%
Similar papers in this journal
2
Bioinformatics
◐ 1204 papers in training set
Top 3%
9.7%
Similar papers in this journal
3
PLOS ONE
● 5266 papers in training set
Top 27%
5.5%
Similar papers in this journal
4
Bioinformatics Advances
◐ 203 papers in training set
Top 1%
4.1%
Similar papers in this journal
5
GigaScience
◐ 212 papers in training set
Top 1%
3.2%
Similar papers in this journal
6
BMC Genomics
○ 406 papers in training set
Top 3%
2.4%
50% of probability mass above
7
PeerJ
◐ 308 papers in training set
Top 3%
2.4%
8
PLOS Computational Biology
● 1863 papers in training set
Top 13%
2.0%
9
mSystems
● 394 papers in training set
Top 4%
1.9%
10
Molecular Ecology Resources
○ 171 papers in training set
Top 1%
1.9%
11
Briefings in Bioinformatics
◐ 354 papers in training set
Top 4%
1.7%
12
Microbiology Spectrum
● 469 papers in training set
Top 7%
1.7%
13
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 3%
1.5%
14
Scientific Reports
○ 3612 papers in training set
Top 60%
1.3%
15
ACS Synthetic Biology
● 287 papers in training set
Top 2%
1.3%
16
BioTechniques
○ 25 papers in training set
Top 0.2%
1.3%
17
F1000Research
○ 88 papers in training set
Top 2%
1.1%
18
Genes
○ 144 papers in training set
Top 3%
1.1%
19
Biology Methods and Protocols
◐ 61 papers in training set
Top 1%
1.1%
20
STAR Protocols
○ 18 papers in training set
Top 0.2%
1.0%
21
Biology
○ 45 papers in training set
Top 0.5%
1.0%
22
Methods in Ecology and Evolution
○ 176 papers in training set
Top 2%
1.0%
23
Journal of Bioinformatics and Systems Biology
○ 15 papers in training set
Top 0.2%
1.0%
24
Frontiers in Bioinformatics
○ 49 papers in training set
Top 1%
0.9%
25
Computational Biology and Chemistry
○ 28 papers in training set
Top 0.8%
0.9%
26
Ecology and Evolution
○ 267 papers in training set
Top 5%
0.8%
27
Genome Biology
○ 637 papers in training set
Top 9%
0.8%
28
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 6%
0.8%
29
SLAS Technology
○ 14 papers in training set
Top 0.2%
0.8%
30
Analytical Chemistry
● 218 papers in training set
Top 2%
0.8%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.