Li Manci, Bryant Damani N, Gresch Sarah, Milstein Marissa S, Christenson Peter R, Lichtenberg Stuart S, Larsen Peter A, Oh Sang-Hyun
Department of Electrical and Computer Engineering, College of Science and Engineering, University of Minnesota, Minneapolis, MN 55455, United States.
Minnesota Center for Prion Research and Outreach, College of Veterinary Medicine, University of Minnesota, St. Paul, MN 55108, United States.
Bioinformatics. 2024 Dec 26;41(1). doi: 10.1093/bioinformatics/btae752.
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. In addition, 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.
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 establishes the foundation for building an F-SAA data management and analysis framework, enabling more consistent and comparable results across different research groups.
QuICSeedR is freely available at: https://CRAN.R-project.org/package=QuICSeedR. Data and code used in this manuscript are provided in Supplementary Materials.
荧光团辅助种子扩增分析(F-SAA),如实时光振荡诱导转化(RT-QuIC)和荧光团辅助蛋白质错误折叠循环扩增(F-PMCA),已成为研究神经退行性疾病中蛋白质错误折叠不可或缺的工具。然而,分析这些技术产生的数据通常需要复杂且耗时的手动过程。此外,F-SAA数据分析缺乏标准化给不同实验室和研究中F-SAA结果的解释和可重复性带来了重大挑战。需要能够在确保不同研究环境下的一致性和可靠性的同时高效处理F-SAA数据的自动化、标准化分析工具。
在此,我们展示了QuICSeedR(发音为“快速播种器”),这是一个R包,通过提供用于F-SAA数据的自动化处理、分析和可视化的综合工具包来应对这些挑战。重要的是,QuICSeedR还为构建F-SAA数据管理和分析框架奠定了基础,使不同研究组之间能够获得更一致且可比的结果。
QuICSeedR可在以下网址免费获取:https://CRAN.R-project.org/package=QuICSeedR。本手稿中使用的数据和代码在补充材料中提供。