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

Area of Science:

• Biochemistry
• Bioinformatics
• Drug Discovery

Background:

• High-throughput screening (HTS) generates vast datasets crucial for preclinical drug discovery.
• Existing HTS data management methods often rely on disparate systems, hindering accessibility and reproducibility.
• Efficient data handling is paramount for identifying viable drug candidates.

Purpose of the Study:

• To introduce Lab Data Management (LDM), an open-source web application for automating HTS data management and visualization.
• To provide a customizable solution for integrating and analyzing data from diverse laboratory instruments.
• To improve data accessibility and reproducibility in HTS workflows.

Main Methods:

• Development of an open-source web application, Lab Data Management (LDM).
• Implementation of an intuitive user interface for managing HTS output data.
• Integration of data visualization tools and quality control metric calculations.
• Inclusion of an embedded Jupyter notebook for advanced data analysis.

Main Results:

• LDM offers a centralized platform for HTS data storage and management.
• The application supports data from various laboratory instruments, including plate readers and microscopes.
• LDM facilitates results visualization and quality control assessment.
• Integrated Jupyter notebooks enable seamless data retrieval for in-depth analysis.

Conclusions:

• Lab Data Management (LDM) provides an effective solution for the challenges in HTS data handling.
• The open-source application promotes data accessibility, reproducibility, and efficient analysis in drug discovery.
• LDM empowers researchers to better manage and interpret large-scale screening data.