LYNX-RNA: A Scalable Nextflow Workflow for RNA-Seq Analysis with Integrated Large Language Models for Comprehensive Result Interpretation

RNA sequencing has become an essential technique in the life sciences, providing a comprehensive methodology. RNA sequencing has become an essential method in the life sciences, providing a comprehensive means to quantitatively evaluate RNA levels in tissues and cells. From alignment to subsequent pathway analysis, the increasing usage of RNA-seq has inspired continuous creation of creative tools for all phases of study. For non-experts especially, it is difficult to use these analytical approaches in a scalable, repeatable, and easily available manner. We have established an intuitive, rapid, efficient, and thorough process for RNA-seq analysis using the workflow management system “Nextflow.” LYNX-RNA (Language-augmented Yield for Nextflow-based RNA eXpression analysis) enhances RNA-seq analysis involves the processing of raw sequencing data through alignment, quality control, and subsequent differential expression and pathway analysis. LYNX-RNA incorporates a distinctive integration with a Large Language Model (LLM) that autonomously produces concise, comprehensible summaries of results, thereby rendering RNA-seq data analysis accessible to a diverse audience, including clinicians, biomedical researchers, pharmaceutical scientists, public health professionals, educators, and students. This enhanced accessibility enables other sectors to extract relevant insights from RNA-seq data without necessitating substantial bioinformatics knowledge. The pipeline was verified with patient-derived xenografts from leukemia and lymphoma, identifying differentially expressed genes and enriched pathways pertinent to disease development and therapy response. LYNX-RNA's applications encompass oncology, virology, immunology, personalized medicine, neuroscience, and agriculture, facilitating biomarker discovery, therapeutic target identification, and sustainable agricultural innovations, thereby rendering RNA-seq analysis both accessible and significant across various fields. Looking forward, LYNX-RNA aims to incorporate multi-omics integration, single-cell and spatial transcriptomics support, and machine learning-based predictive analytics. Planned enhancements include cloud deployment, improved visualization, compatibility with long-read sequencing, and clinical application readiness. These advancements will broaden LYNX-RNA's utility in biomarker discovery, personalized medicine, and agricultural innovations, solidifying its role as a comprehensive RNA-seq analysis tool across various fields.