Omondi Cleopa, Chou Austin, Fond Kenneth A, Morioka Kazuhito, Joseph Nadine R, Sacramento Jeffrey A, Iorio Emma, Torres-Espin Abel, Radabaugh Hannah L, Davis Jacob A, Gumbel Jason H, Russell Huie J, Ferguson Adam R
Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA USA.
School of Public Health Sciences, Faculty of Health Sciences, University of Waterloo, ON, Canada.
bioRxiv. 2023 Aug 5:2023.08.02.551674. doi: 10.1101/2023.08.02.551674.
Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.
蛋白质免疫印迹法是一种常用的生物分子分析方法，用于测定复杂生物样品中独立蛋白质的相对含量。然而，蛋白质免疫印迹定量数据分析的变异性给设计可重复的实验带来了挑战。当前蛋白质免疫印迹统计方法缺乏严格的定量方法，可能导致不可重复的推断。在这里，我们描述了蛋白质免疫印迹实验设计和分析的最佳实践，并举例说明了不同的分析方法如何导致差异很大的结果。为了促进最佳实践，我们开发了blotRig工具，用于设计和分析蛋白质免疫印迹实验，以提高其严谨性和可重复性。blotRig应用程序包括通过泳道位置平衡实验设计、跨凝胶批次管理以及使用协变量和随机效应进行分析的功能。
