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Playbook workflow builder: Interactive construction of bioinformatics workflows.

Daniel J B Clarke1, John Erol Evangelista1, Zhuorui Xie1

  • 1Department of Pharmacological Sciences, Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

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This summary is machine-generated.

The Playbook Workflow Builder (PWB) enables dynamic construction and execution of bioinformatics workflows. This user-friendly platform, enhanced by large language models, facilitates hypothesis generation and data analysis for diverse research applications.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Bioinformatics workflows are crucial for analyzing complex biological data.
  • Existing platforms may require significant technical expertise, limiting accessibility.
  • Integrating diverse data sources and visualization tools presents a challenge.

Purpose of the Study:

  • To introduce the Playbook Workflow Builder (PWB), a web-based platform for constructing and executing bioinformatics workflows.
  • To enable users, including those without extensive technical expertise, to build and run complex analyses.
  • To demonstrate the PWB's capability in generating hypotheses and facilitating data-driven research.

Main Methods:

  • Development of a web-based platform with a user-friendly interface for workflow construction.
  • Integration of a network of input datasets, semantically annotated API endpoints, and data visualization tools.
  • Implementation of a chat interface assisted by a large language model (LLM) for workflow design.
  • Ensuring compatibility with Common Workflow Language (CWL) for workflow execution and publication.

Main Results:

  • The PWB allows dynamic construction of bioinformatics workflows through a graphical interface or LLM-assisted chat.
  • Workflows generate comprehensive reports with textual descriptions, figures, tables, and references.
  • Demonstrated use cases include prioritizing drug targets for cancer patients using multiple NIH Common Fund datasets.
  • Generated workflows are reusable for similar analyses with different input data.

Conclusions:

  • The PWB democratizes bioinformatics workflow creation, making complex analyses accessible to a broader research community.
  • The platform supports hypothesis generation by integrating diverse biological data and computational tools.
  • PWB-generated workflows can be published and repurposed, accelerating scientific discovery and collaboration.