A powerful new software platform called the Playbook Workflow Builder is set to transform biomedical research by allowing scientists to conduct complex and customized data analyses without advanced programming skills. An article that describes the new platform was published in the April 3online issue of the journal PLOS Computational Biology.
Developed by a multi-institutional team that was led by Icahn School of Medicine at Mount Sinai investigators as part of the National Institutes of Health Common Fund Data Ecosystem (CFDE) program, researchers from across the United States developed the web-based platform that enables scientists to analyze and visualize their own data independently through an intuitive, interactive interface.
Traditionally, experimental biologists rely heavily on bioinformaticians to process and analyze the large datasets they collect. The new software platform changes this paradigm by offering a modular, user-friendly system where scientists can design custom workflows using pre-built analytical components — akin to assembling “LEGO® pieces,” say the investigators.
“This approach could fundamentally reinvent data analysis in biomedical research and beyond. By allowing researchers to conduct sophisticated analyses without requiring advanced programming skills, it removes a major barrier to data-driven discovery,” says first author Daniel J.B. Clarke, MS, a data scientist in the lab of senior corresponding author Avi Ma’ayan, PhD, Professor, Pharmacological Sciences, and Director of the Mount Sinai Center for Bioinformatics at the Icahn School of Medicine at Mount Sinai. “With greater accessibility and efficiency, scientists across disciplines can explore complex datasets more easily, uncover new insights faster, and ultimately accelerate the pace of scientific publication and discovery.”
Users of the Playbook Workflow Builder engage with an intuitive user interface, clicking on cards to construct their data analysis workflows. Alternatively, users can interface with a large language model-powered chatbot to design and build their data analysis pipelines. As they upload their data and input their instructions, the system automatically generates detailed documentation, including interactive figures, clear figure legends, and step-by-step automatically generated method descriptions.
This ensures that the entire workflow is well-organized and easy to follow. Additionally, researchers can export their workflows in multiple formats, making it easier to share their work with colleagues, which also enhances reproducibility across different studies.
Currently, the team is actively working to expand the platform’s capabilities by adding more analytical components, refining the user experience, and fostering a larger community of users and contributors.
“We see this as just the beginning. By reducing reliance on bioinformatics specialists, this platform has the potential to dramatically accelerate the pace of research by enabling experimental biologists to perform sophisticated analyses independently,” says Dr. Ma’ayan “As more scientists adopt this tool, we expect it to streamline workflows, speed up the sharing of findings, and inspire new AI-driven systems that further enhance scientific reasoning and data interpretation. In the years to come, this could become an essential resource for biomedical research, unlocking new insights and driving innovation across the field.”
