Efficient, Automatic, and Reproducible Patch Clamp Data Analysis with "Auto ANT", a User-Friendly Interface for Batch Analysis of Patch Clamp Recordings.

IF 2.7 4区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Neuroinformatics Pub Date : 2025-03-18 DOI:10.1007/s12021-025-09721-w

Giusy Pizzirusso, Simon Sundström, Luis Enrique Arroyo-García

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Abstract

Patch-clamp recordings are vital for investigating the electrical properties of excitable cells, yet the analysis of these recordings often involves time-consuming manual procedures prone to variability. To address this challenge, we developed the Auto ANT (Automated Analysis and Tables) open-source software, an automated, user-friendly graphical interface for the extraction of firing properties and passive membrane properties from patch-clamp recordings. Thanks to the novel built-in automation feature, Auto ANT enables batch analysis of multiple files recorded with the same protocol in minutes. Our tool is designed to streamline data analysis, providing a fast, efficient, and reproducible alternative to manual methods. With a focus on accessibility, Auto ANT allows the users to perform precise comprehensive electrophysiological analyses without requiring programming expertise. By combining automation with a user-centric design, Auto ANT offers a valuable resource for researchers to accelerate data analysis while promoting consistency and reproducibility across different studies.

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高效，自动，可重复膜片钳数据分析与“Auto ANT”，一个用户友好的界面，用于批量分析膜片钳记录。

膜片钳记录对于研究可兴奋细胞的电特性是至关重要的，然而对这些记录的分析往往涉及耗时的人工程序，容易发生变化。为了应对这一挑战，我们开发了Auto ANT（自动分析和表）开源软件，这是一个自动化的、用户友好的图形界面，用于从膜片钳记录中提取激发特性和被动膜特性。由于新颖的内置自动化功能，Auto ANT可以在几分钟内批量分析使用同一协议记录的多个文件。我们的工具旨在简化数据分析，为手动方法提供快速、高效和可重复的替代方法。专注于可访问性，Auto ANT允许用户执行精确的全面电生理分析，而无需编程专业知识。通过将自动化与以用户为中心的设计相结合，Auto ANT为研究人员提供了宝贵的资源，可以加速数据分析，同时促进不同研究之间的一致性和可重复性。

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

Neuroinformatics 医学-计算机：跨学科应用

CiteScore

6.00

自引率

6.70%

发文量

审稿时长

3 months

期刊介绍： Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.