Scientific Workflow Systems such as Galaxy and Nextflow are essential for scalable, reproducible, and automated bioinformatics analyses. However, developing and understanding scientific workflows remains challenging for many domain scientists due to the complexity of tool/module selection, infrastructure requirements, and limited programming expertise. This study explores whether state-of-the-art Large Language Models such as GPT-4o, Gemini 2.5 Flash, and DeepSeek-V3 can assist in generating accurate, complete, and usable bioinformatics workflows. We evaluate a set of representative workflows covering tasks such as RNA-seq, SNP analysis, and DNA methylation across both Galaxy (graphical) and Nextflow (script-based) platforms. To simulate realistic usage, we adopt a tiered prompting strategy: each workflow is first generated using an instruction-only prompt; if the output is incomplete or incorrect, we escalate to a role-based prompt, and finally to chain-of-thought prompting if needed. The generated workflows are evaluated against community-curated baselines from the Galaxy Training Network and nf-core, using criteria including correctness, completeness, tool appropriateness, and executability. Results show that LLMs exhibit strong potential in workflow development. Gemini 2.5 Flash produced the most accurate and user-friendly workflows in Galaxy, while DeepSeek-V3 excelled in Nextflow pipeline generation. GPT-4o performed nicely with structured prompts. Prompting strategy significantly influenced output quality, with role-based and chain-of-thought prompts enhancing correctness and completeness. Overall, LLMs can reduce the cognitive and technical barriers to workflow development, making SWSs more accessible to novice and expert users. This work highlights the practical utility of LLMs and provides actionable insights for integrating them into real-world bioinformatics workflow design.
Snakemaker: Seamlessly transforming ad-hoc analyses into sustainable Snakemake workflows with generative AI
Dipartimento di Scienze della Vita e Biologia dei Sistemi and MBC, Università di Torino（托里尼大学生命科学与系统生物学系及MBC）；Algorithms for reproducible bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen（杜伊斯堡-埃森大学生物信息学算法、基因组信息学、人类遗传学研究所、埃森大学医院）
Biomedical systems biology workflow orchestration and execution with PoSyMed
Institute for Computational Systems Biomedicine, University of Hamburg（计算系统生物医学研究所，汉堡大学）；Faculty of Science, Computer Science, Vrije Universiteit Amsterdam（科学与计算机科学学院，阿姆斯特丹自由大学）；Department for Mathematics and Computer Science, University of Southern Denmark（数学与计算机科学系，南丹麦大学）
Learning Service Selection Decision Making Behaviors During Scientific Workflow Development
Southern Methodist University（南卫理公会大学）；Marshall Space Flight Center, NASA（美国国家航空航天局马歇尔太空飞行中心）；Science Mission Directorate, NASA Headquarters（美国国家航空航天局总部科学任务局）；Jet Propulsion Laboratory, NASA（美国国家航空航天局喷气推进实验室）
