BioWorkflow: Retrieving comprehensive bioinformatics workflows from publications

Reconstructing bioinformatics workflows from the literature is the foundation of scientific analysis. However, the required details—processing steps, software tools, versions, and parameter settings—are dispersed across narrative text, tables, figure captions, and supplemental files. Manual reconstruction typically takes hours per paper and is error-prone, while existing question-answering (QA) and retrieval systems focus on local passages and lack the full-text, multimodal capabilities needed to automatically rebuild complete workflows. We introduce BioWorkflow, a large language model (LLM)-based, retrieval-augmented framework that automates end-to-end workflow extraction from publications by (i) parsing PDFs and building a unified index over text, tables, and figures with chunk-level summaries and embeddings; (ii) hierarchically decomposing queries with dynamic reformulation when new entities or ambiguities emerge; (iii) performing iterative, context-aware retrieval and assembling a directed workflow that captures steps, tools, versions, and parameters; and (iv) linking each predicted element to its cited evidence and running automated consistency checks to suppress hallucinations and ensure traceability. Evaluated on 100 expert-annotated papers, BioWorkflow recovers ~80% of workflow steps (versus ~20% for existing tools), improves reproducibility, completeness, and accuracy by >20% over strong LLM baselines, and reduces curation time to 3–5 minutes per paper, enabling rapid and reliable reuse of published pipelines.