LDM: A web application for automated management and visualization of laboratory screening data

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology Pub Date : 2025-02-21 DOI:10.1016/j.slast.2025.100258

David Meyer , Anastasia Escher , Eva Riegler , David Keller , Michael Prummer , Stephanie Huber , Tijmen Booij

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引用次数: 0

Abstract

High-throughput screening (HTS) is essential in preclinical research to identify new drug candidates for specific diseases. This process typically generates large amounts of data that require effective storage, management, and analysis. Traditional methods for handling HTS data involve several standalone solutions, which can present challenges regarding data accessibility and reproducibility. We introduce Lab Data Management (LDM), an open-source web application developed to automate the management and visualization of HTS data. LDM provides a highly customizable data management system with an intuitive user interface for handling output data from various laboratory instruments, such as plate readers, microscopes, liquid handlers, and barcode readers. The app allows for results visualization and calculation of quality control metrics. An integrated Jupyter notebook can be used to retrieve the stored data and proceed with a more detailed analysis.

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LDM：实验室筛选数据自动化管理和可视化的Web应用程序。

高通量筛选（HTS）在临床前研究中确定特定疾病的新候选药物是必不可少的。此过程通常生成大量数据，这些数据需要有效的存储、管理和分析。处理HTS数据的传统方法涉及几个独立的解决方案，这可能会带来数据可访问性和再现性方面的挑战。我们介绍了实验室数据管理（LDM），这是一个开源的web应用程序，用于自动管理和可视化HTS数据。LDM提供了一个高度可定制的数据管理系统，具有直观的用户界面，用于处理来自各种实验室仪器的输出数据，如平板阅读器，显微镜，液体处理器和条形码阅读器。该应用程序允许结果可视化和质量控制指标的计算。集成的Jupyter笔记本可用于检索存储的数据并进行更详细的分析。

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

SLAS Technology Computer Science-Computer Science Applications

CiteScore

6.30

自引率

7.40%

发文量

审稿时长

106 days

期刊介绍： SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.