PATHWAY ENRICHMENT ANALYSIS USING IPA AND CE - ONLINE
This is a flipped class; links to PowerPoint slides, lecture videos, and practice exercises that you can view on your own schedule are available upon registration. During this in-person, hands-on session, you will learn how to solve the exercise problems.
The workshop lecture video provides a brief overview of bioinformatics concepts and software used for interpreting a gene list using pathway and network information, followed by a step-by-step guide on pathway enrichment analysis using two HSLS-licensed tools: Correlation Engine, and Ingenuity Pathway Analysis (IPA)
Participants will learn how to:
- retrieve a list of differentially expressed genes (DEG) associated with a genome-scale experiment such as an RNA-Seq gene expression study ("treatment vs. control," "tumor vs. normal" or "infected vs. Mock") by searching gene expression data repositories (NCBI GEO)
- glean mechanistic insights by finding statistically overrepresented terms (biological functions, molecular processes, diseases, etc.) and pathways present in that DEG list
- predict upstream causal regulators (transcription factors, miRNA, etc.)
- retrieve datasets from GEO that show similar or opposite gene expression profiles
Experimental biologists working with human, mouse, or rat tissues and seeking to interpret gene lists generated through omics experiments such as gene expression, protein interactions, and SNP arrays. The software covered in the workshop operates through a user-friendly, point-and-click graphical user interface, so neither programming experience nor familiarity with the command-line interface is required.
Registration is required. Please register here.
Wednesday, July 27 at 11:00 a.m. to 1:00 p.m.
Virtual Event
EVENT TYPE
Trainings & Workshops
TOPIC
Research, Technology, Continuing Education
TARGET AUDIENCE
Staff, Faculty, Graduate Students, Postdocs
TAGS
Health Sciences, Molecular Biology, HSLS, Catalog of Opportunities
WEBSITE
https://www.hsls.pitt.edu/instruction...
UNIVERSITY UNIT
Health Sciences Library System
HASHTAG
#HealthSciences
Original source can be found here.
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