BioCurve Analyzer: a web-based shiny app for analyzing biological response curves.

IF 4.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Plant Methods Pub Date : 2025-04-27 DOI:10.1186/s13007-025-01372-x

Zenan Xing, James Eckhardt, Aditya S Vaidya, Sean R Cutler

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引用次数: 0

Abstract

Background: Dose-response and time-to-event data are common in enzymology, pharmacology, and agronomy studies. Diverse biological response curves can be generated from such data. The features of these curves can be elucidated through parameters such as ED₅₀ (the effective dose that gives 50% of the maximum response) and T₅₀ (the time required to reach 50% of the maximum response). Properly estimating these parameters is crucial for inferring the potency of compounds or the relative timings of biological processes.

Results: We present an open-source Shiny application, BioCurve Analyzer, that simplifies the process of inferring ED₅₀ and T₅₀ parameters from response curves exhibiting various patterns, including classic monotonic sigmoidal curves and more complicated biphasic curves. BioCurve Analyzer provides access to several packages and commonly used models for characterizing response patterns, assists users in identifying the models that best describe their data, and includes options for inferring ED₅₀ values on both sides of biphasic curves. BioCurve Analyzer also facilitates the visualization of response patterns and allows users to customize their final graphical representation to deliver publication-quality graphs of the data.

Conclusion: BioCurve Analyzer integrates multiple R packages in an easy-to-use web-based interface to facilitate dose-response and time-to-event analyses.

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生物曲线分析仪：一个基于网络的闪亮应用程序，用于分析生物反应曲线。

背景：剂量-反应和事件时间数据在酶学、药理学和农学研究中很常见。这些数据可以生成不同的生物反应曲线。这些曲线的特征可以通过ED50（达到最大应答的50%的有效剂量）和T50（达到最大应答的50%所需的时间）等参数来阐明。正确估计这些参数对于推断化合物的效力或生物过程的相对时间至关重要。结果：我们提出了一个开源的Shiny应用程序bicurve Analyzer，它简化了从各种模式的响应曲线（包括经典的单调s型曲线和更复杂的双相曲线）推断ED50和T50参数的过程。BioCurve Analyzer提供了几个软件包和常用模型来表征响应模式，帮助用户识别最能描述其数据的模型，并包括推断双相曲线两侧ED50值的选项。BioCurve Analyzer还促进了响应模式的可视化，并允许用户自定义其最终图形表示，以提供出版质量的数据图形。结论：biocurcurve Analyzer在一个易于使用的基于web的界面中集成了多个R包，以促进剂量反应和事件时间分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Plant Methods 生物-植物科学

CiteScore

9.20

自引率

3.90%

发文量

121

审稿时长

2 months

期刊介绍： Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences. There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics. Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.