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Related Experiment Video

Updated: Jun 4, 2025

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control

Published on: March 30, 2015

QuICSeedR: an R package for analyzing fluorophore-assisted seed amplification assay data.

Manci Li^1,2, Damani N Bryant^2,3, Sarah Gresch^2,3

¹Department of Electrical and Computer Engineering, College of Science and Engineering, University of Minnesota, Minneapolis, MN 55455, United States.

Bioinformatics (Oxford, England)

|December 24, 2024

Summary

This summary is machine-generated.

Automated analysis of fluorophore-assisted seed amplification assays (F-SAAs) is now possible with QuICSeedR. This R package simplifies data processing and enhances reproducibility for neurodegenerative disease research.

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Last Updated: Jun 4, 2025

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control

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Area of Science:

Biochemistry
Neuroscience
Computational Biology

Background:

Fluorophore-assisted seed amplification assays (F-SAAs), including RT-QuIM and F-PMCA, are vital for studying protein misfolding in neurodegenerative diseases.
Current data analysis methods for F-SAAs are manual, complex, and lack standardization, hindering reproducibility.
There is a critical need for automated and standardized tools to analyze F-SAA data efficiently and reliably.

Purpose of the Study:

To introduce QuICSeedR, an R package designed for automated processing, analysis, and visualization of F-SAA data.
To provide a foundation for a standardized data management and analysis framework for F-SAAs.
To improve consistency and comparability of F-SAA results across research groups.

Main Methods:

Development of QuICSeedR, an R package offering a comprehensive toolkit for F-SAA data analysis.
Implementation of automated workflows for processing, analyzing, and visualizing F-SAA experimental outputs.
Establishment of a framework for data management to ensure consistency.

Main Results:

QuICSeedR provides automated and standardized analysis of F-SAA data.
The R package facilitates efficient processing and visualization of complex experimental results.
QuICSeedR promotes consistency and comparability of F-SAA data across different research settings.

Conclusions:

QuICSeedR addresses the need for automated, standardized analysis of F-SAA data.
This R package enhances the efficiency, reproducibility, and comparability of research on protein misfolding diseases.
QuICSeedR is freely available, supporting broader adoption and advancement in the field.