This session showcases cutting-edge advances in bioinformatics powering precision medicine and cancer genomics. Speakers will present new methods for accelerating secondary analysis, enhancing variant detection accuracy, optimizing large-scale sequencing pipelines, and improving reproducibility across heterogeneous datasets. With perspectives from industry, academia, and clinical informatics, the session highlights practical approaches for scaling genomic computation, reducing processing bottlenecks, and enabling high-confidence insights that directly support translational research and clinical decision-making.

Hackathons offer fast-paced, collaborative learning that mirror the needs of modern omics research. This session will highlight how integrating Common Fund Data Ecosystem resources enable teams to tackle real data challenges, build practical skills, share strategies for supporting new users, and spark discovery. Outcomes from the Bio-IT Hackathon on May 18–19, 2026, will be shared, including project themes, open tools, and data used.

Based on the concept of competing endogenous RNA (ceRNA), this talk presents a new framework to analyze the potential contributions of microRNAs in response to ICI therapy in mesothelioma. We first constructed mesothelioma ceRNA networks based on two largest sets of publicly available gene and microRNA expression data and six public microRNA databases. We then used these networks to ascertain the contributions of differentially expressed microRNAs in mesothelioma patients’ survival outcomes with ICI.

Metabolic dysfunction and insulin resistance remain central challenges in cardiometabolic disease research, demanding integrative frameworks that bridge biology and bioinformatics. Leveraging a microphysiological liver model and bulk RNA-Seq, we developed a Nextflow-based bioinformatics pipeline integrating batch correction and statistical power modeling to ensure reproducible and interpretable results. This approach operationalizes design and computational rigor, transforming transcriptomic data into actionable insights to advance understanding of metabolic regulation and therapeutic targets.

Novel 3D IF (immunofluorescence) imaging technologies are shifting analysis of the tissue microenvironment from low-resolution 2D to high-resolution 3D approaches, allowing study of cellular interaction in unprecedented detail. This talk presents progress from displaying and analyzing spatial relations in tabular (2D) data towards scalable 3D multi-volume visualization and detailed image-based analysis of cellular features and cell-cell interactions. I will showcase different approaches and tools on healthy and cancerous tissue data.

Analyzing somatic mosaicism with DRAGEN using blood and sperm tissues, enhanced by a personalized reference genome for improved accuracy and detection. This approach will enable more sensitive detection of low-frequency variants and provide deeper insights into tissue-specific mosaic patterns.

Building scalable genomics analysis platforms traditionally requires significant infrastructure investment and specialized IT expertise, limiting access to well-funded institutions. We present a production-ready platform architecture using miniWDL and AWS Batch that enables research organizations to deploy enterprise-grade genomics infrastructure supporting complex genomics pipelines while achieving 30–50% cost reduction through intelligent spot instance orchestration. This platform democratizes genomics by providing researchers with self-service access to secure, reproducible, and cost-efficient large-scale analyses.

Whole genome sequencing is becoming widespread, yet access, usability, and data ownership remain controlled by sequencing providers. But that doesn’t have to be. We’ll introduce novel architectures and tools that enables individuals to query their complete genomic data using natural language—directly from their personal devices, without uploading sensitive information to third-party servers. Users can ask health questions like “Do I have pathogenic variants related to hemochromatosis?” or questions related to kinship or traits. By shifting genomic analysis to the edge—leveraging modern mobile computing power and AI-driven natural language interfaces—we return data ownership to individuals while maintaining the analytical sophistication of centralized platforms.

AI, genomics, and multimodal data science are reshaping rare-disease diagnosis and dramatically reducing the diagnostic odyssey. This closing joint session brings together leaders in precision medicine, bioinformatics, national rare-disease infrastructure, and real-world patient advocacy to highlight breakthrough models for rapid genomic interpretation, data integration, and clinical deployment. Attendees will gain a unified, cross-disciplinary view of what’s required to deliver faster, more accurate, and more equitable rare-disease diagnoses.