Pathway Enrichment Analysis--IPA & MetaCore

Workshop Objective:

This is a 4½ hour workshop. The morning session (10 am - 12 pm) provides a brief overview of bioinformatics concepts and software used for interpreting a gene list using pathway and network information. The afternoon session (1 pm - 3:30 pm) focuses on software demonstration using 3 HSLS-licensed tools: Correlation Engine, Ingenuity Pathway Analysis (IPA), and MetaCore.

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

Every Wednesday after a workshop, a follow up session (1 pm - 3 pm) will review the hands-on exercise questions distributed during the workshop.  We will not be addressing questions on data analysis for personal research projects—researchers are encouraged to request a one-on-one consultation with MBIS for this type of discussion. Registration is not required for the hands-on follow up session.  To attend, use the same Zoom link you receive upon registration for this workshop.

Target Audience:

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.

Workshop Requirements:

Workshop Guide:

Suggested Reading:

Attribution:

Please include the following statement in the acknowledgments section for all publications, posters, and presentations: Data analysis was performed using {name of software} software licensed through the Molecular Biology Information Service of the Health Sciences Library System, University of Pittsburgh.

Class Type

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