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. 2025 Apr 3;21(4):e1012901.
doi: 10.1371/journal.pcbi.1012901. eCollection 2025 Apr.

Playbook workflow builder: Interactive construction of bioinformatics workflows

Daniel J B Clarke  1 , John Erol Evangelista  1 , Zhuorui Xie  1 , Giacomo B Marino  1 , Anna I Byrd  1 , Mano R Maurya  2 , Sumana Srinivasan  2 , Keyang Yu  3 , Varduhi Petrosyan  3 , Matthew E Roth  3 , Miroslav Milinkov  4 , Charles Hadley King  5 , Jeet Kiran Vora  5 , Jonathon Keeney  5 , Christopher Nemarich  6   7 , William Khan  6   7 , Alexander Lachmann  1 , Nasheath Ahmed  1 , Alexandra Agris  1 , Juncheng Pan  1 , Srinivasan Ramachandran  2 , Eoin Fahy  2 , Emmanuel Esquivel  3 , Aleksandar Mihajlovic  4 , Bosko Jevtic  4 , Vuk Milinovic  4 , Sean Kim  5 , Patrick McNeely  5 , Tianyi Wang  5 , Eric Wenger  6   7 , Miguel A Brown  6   7 , Alexander Sickler  6   7 , Yuankun Zhu  6   7 , Sherry L Jenkins  1 , Philip D Blood  8 , Deanne M Taylor  6   7 , Adam C Resnick  6   7 , Raja Mazumder  5 , Aleksandar Milosavljevic  3 , Shankar Subramaniam  2 , Avi Ma'ayan  1
Affiliations

Playbook workflow builder: Interactive construction of bioinformatics workflows

Daniel J B Clarke et al. PLoS Comput Biol. .

Abstract

The Playbook Workflow Builder (PWB) is a web-based platform to dynamically construct and execute bioinformatics workflows by utilizing a growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem of collaborators. Via a user-friendly user interface, workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflow is added into reports containing textual descriptions, figures, tables, and references. To construct workflows, users can click on cards that represent each step in a workflow, or construct workflows via a chat interface that is assisted by a large language model (LLM). Completed workflows are compatible with Common Workflow Language (CWL) and can be published as research publications, slideshows, and posters. To demonstrate how the PWB generates meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases prioritizes drug targets for individual cancer patients using data from the NIH Common Fund programs GTEx, LINCS, Metabolomics, GlyGen, and ExRNA. The workflows created with PWB can be repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The different PWB metanode types are strung together to form workflows.
A. In this example, the prompt type of metanode takes a gene as the input; then the resolver metanode uses the GTEx API to obtain the expression of the input gene from across human tissues. Finally, a view metanode visualizes the contents returned from the API as a bar chart. B. Screenshot from the executed workflow in the PWB platform.
Fig 2
Fig 2. Network visualization of the PWB knowledge resolution graph (KRG).
The network of connected metanodes is interactive and can be explored from the user interface.
Fig 3
Fig 3. The landing page of the PWB UI provides access to a collection of prompt metanodes to begin constructing workflows.
Fig 4
Fig 4. The structure of the Persistent Process Resolution Graph (FPPRG) Database.
The FPPRG database stores the data that flows through workflows in four tables. The first table is a dependency graph of each constructed step of a workflow. The second table stores the sequential order of a workflow. The third table is a Result record, and the 4th table is a Data record.
Fig 5
Fig 5. Published workflows are curated workflows that are listed on a dedicated page that catalogs these in a table.
Each workflow entry can be expanded to obtain more information about the workflow and to launch the workflow within the PWB platform in report mode.
Fig 6
Fig 6. SuperVenn diagram to visualize the overlap between sets of genes that are up and down regulated by aspirin and warfarin based on LINCS L1000 signatures, as well as knockout mouse, HPO, and GWAS phenotypes associated with the term “bleeding”.
The permanent URL for a description of this workflow is: https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.1237.3.
See this image and copyright information in PMC

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