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

Manci Li; Damani N Bryant; Sarah Gresh; Marissa Milstein; Peter R Christenson; Stuart S Lichtenberg; Peter A Larsen; Sang-Hyun Oh

doi:10.1093/bioinformatics/btae752

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

Bioinformatics. 2024 Dec 24:btae752. doi: 10.1093/bioinformatics/btae752. Online ahead of print.

Authors

Manci Li^{1

2}, Damani N Bryant^{2

3}, Sarah Gresh^{2

3}, Marissa Milstein^{2

3}, Peter R Christenson^{1

2}, Stuart S Lichtenberg^{2

3}, Peter A Larsen^{2

3}, Sang-Hyun Oh^{1

2}

Affiliations

¹ Department of Electrical and Computer Engineering, College of Science and Engineering, University of Minnesota, Minneapolis, MN United States.
² Minnesota Center for Prion Research and Outreach, College of Veterinary Medicine, University of Minnesota, St. Paul, MN United States.
³ Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, St. Paul, MN United States.

PMID: 39718778
DOI: 10.1093/bioinformatics/btae752

Abstract

Motivation: Fluorophore-assisted seed amplification assays (F-SAAs), such as real-time quaking-induced conversion (RT-QuIC) and fluorophore-assisted protein misfolding cyclic amplification (F-PMCA), have become indispensable tools for studying protein misfolding in neurodegenerative diseases. However, analyzing data generated by these techniques often requires complex and time-consuming manual processes. Additionally, the lack of standardization in F-SAA data analysis presents a significant challenge to the interpretation and reproducibility of F-SAA results across different laboratories and studies. There is a need for automated, standardized analysis tools that can efficiently process F-SAA data while ensuring consistency and reliability across different research settings.

Results: Here, we present QuICSeedR (pronounced as "quick seeder"), an R package that addresses these challenges by providing a comprehensive toolkit for the automated processing, analysis, and visualization of F-SAA data. Importantly, QuICSeedR also sets up the foundation for building an F-SAA data management and analysis framework, enabling more consistent and comparable results across different research groups.

Availability: QuICSeedR is freely available at: https://CRAN.R-project.org/package=QuICSeedR. Data and code used in this manuscript are provided in Supplementary Materials.

Supplementary information: Supplementary data are available at Bioinformatics online.