Classes & Events

Simple Questions Your RAG System Can't Answer

Qlucore Omics Explorer is a point-and-click package available to NCI CCR scientists that enables visualization-based analysis of multi-omics data including RNA-seq, scRNA-seq, proteomics, metabolomics, as well as enabling machine learning classification of cell types. Submit a ticket at https://service.cancer.gov/ncisp to get it installed. In this session, participants will learn to apply regression approaches to identify correlation between bulk RNA and protein expression using this software. Experience using or installation of Qlucore Omics Explorer is not needed to attend. Attendance is restricted to NIH staff. Meeting link will be provided upon approval of registration.

This one-hour online training offers an overview of the NIH-sponsored Generalist Repository Ecosystem Initiative (GREI) (Dataverse, Dryad, Figshare, Mendeley Data, Open Science Framework, Vivli, and Zenodo), and the role of participating in these repositories in the NIH data repository landscape for intramural researchers. The session will highlight how these repositories support compliance with the NIH Data Management and Sharing Policy.

By the end of this training, attendees will be able to:  

• Describe how generalist repositories fit into the NIH data repository landscape for intramural researchers.
• Understand how these repositories support compliance with the NIH Data Management and Sharing Policy
• Learn about the resources developed by GREI repositories to support data sharing workflows, including a generalist repository comparison chart, a generalist repository selection flowchart, a data submission checklist, and a data management and sharing plan guide.
• Gain practical insights from real-world examples, demonstrating  how researchers use generalist repositories for data sharing and reuse, and how these efforts contribute to the broader NIH data sharing ecosystem.

Attendees are not expected to have any prior knowledge of the NIH Data Repository Landscape.    

The role of computational science in biomedical research has typically been downstream of experiments, where it plays important roles in signal processing, data integration, pattern detection, and hypothesis testing. But this is changing, and predictive models are now being used to generate and test hypotheses in silico. In this talk, Dr. Pollard will share examples from human genetics, where they have built deep learning models of 3D chromatin interactions that take only sequence as input and then used them to interpret disease variants. This strategy leads to causal hypotheses and enables them to prioritize variants with predicted functional effects. Experiments designed using model outputs are accelerating the rate of discoveries, shedding light on genetic mechanisms in cancer and developmental disorders. This prediction-first strategy exemplifies Dr. Pollard's vision for a more proactive, rather than reactive, role for computational science in biomedical research.

Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is a point-and-click software suitable for those who wish to avoid the steep learning curve involved with analyzing sequencing data through coding. In this class, taught by Partek scientist, participants will learn about conducting QA/QC, performing cell type classification, obtaining differential analysis results, performing pathway analysis, and creating visualizations for single cell RNA sequencing data. This session is a demonstration and not hands-on. Experience using or access to Partek Flow is not needed for participation. Attendance is restricted to NIH staff.