This webinar will help introduce users to GDC tools for analyzing data from cancer genomic studies. As an example, we will explore most frequently mutated genes and mutations and perform a survival analysis for cases with and without these mutations, view the distribution of particular mutations and mutated genes across the GDC and visualize associated transcripts in a protein viewer, build custom gene sets for targeted analysis, perform integrated analysis on the most mutated cases in an OncoGrid, and analyze cases within and across projects.
Intended Audience
This webinar is intended for cancer researchers and bioinformaticians who are interested in using the NCI Genomic Data Commons (GDC) and supporting tools to analyze data from cancer genomics studies.
Abstract
The Analyzing Data using GDC Data Analysis, Visualization, and Exploration (DAVE) Tools webinar will help introduce users to GDC tools for analyzing data from cancer genomic studies. As an example, we will explore most frequently mutated genes and mutations and perform a survival analysis for cases with and without these mutations, view the distribution of particular mutations and mutated genes across the GDC and visualize associated transcripts in a protein viewer, build custom gene sets for targeted analysis, perform integrated analysis on the most mutated cases in an OncoGrid, and analyze cases within and across projects.
Included Topics
- Visualize most frequently mutated genes and view most frequent somatic mutations for a project
- Perform a survival analysis for cases with a mutated form of a certain gene and cases without the mutation
- Visualize mutations and their frequencies across protein domains
- Build custom gene sets for targeted analysis
- Plot all cases for a project in an OncoGrid and visualize the top 50 mutated genes affected by high impact mutations
- View the number of cases affected by particular mutation across all projects
Webex Information
Analyzing Data using GDC Data Analysis, Visualization, and Exploration (DAVE) Tools
- Thursday, October 12, 2017
- 12:00 pm | Eastern Standard Time (New York, GMT-05:00) | 1 hr
- Meeting number (access code): 739 100 511
- Meeting password: 3PfXmHw?
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