Bioinformatics Training and Education Program

ChIP-Seq Data Analysis Workshop (2 day)

ChIP-Seq Data Analysis Workshop (2 day)

 When: Nov. 21st, 2013 - Nov. 22nd, 2013 9:30 am - 5:00 pm

To Know

Presented By:
Peter FitzGerald (GAU)
This class has ended.

About this Class

This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of ChIP-Seq data analysis. Learn everything from experimental design to statistical analysis and several downstream motif and pattern discovery methods using both commercial (Genomatix) and open source software. Those who successfully complete this course will receive a certificate, that will not only look good on their wall, but will also entitle their lab to an additional subsidy from OSTR towards the cost of a ChIP-Seq sequencing run. 

Day 1 - AM (9:30-12)  Introductory Lecture
(Peter FitzGerald, PhD - CCR, NCI)

  • Introduction
    • Historical Perspective and Technical Variations
    • Experimental methodology
    • Comparison to ChIP-Chip
  • Data Analysis
    • Experimental Design
    • Quality Control 
    • Peak Calling (Different methodologies)
    • Major Sources of Error
    • Causes of Fail Experiments
    • Validation Methods
  • Sequence Specific Binding
    • Identification of Motifs
    • Overexpressed sequences
    • Pathways
  • Resources
    • Public Repositories
    • Literature References
    • Software directories

Bioinformatics Core Presentation 
 (Anand Merchant, PhD - CCRIFX)

  • Lessons learned
  • How to work with the Core
  • Encode "Best Practices"
  • Guides to success

Day 1 - AM (2:00-5:00)  Hands-On with Genomatix
(Susan Dombrowski, PhD - Genomatix)

  • Interacting with the system
  • Importing Data
  • Peak Calling

Day 2 - AM (9:30-12:00)  Hands-On with Genomatix
(Susan Dombrowski, PhD - Genomatix)

  • Biological insights
  • Motif Finding
  • Pathways

Day 2 - AM (2:00-5:00)  Data Visualization 

(Peter FitzGerald, PhD - CCR, NCI)

  • Review
  • Visualization Tools
  • Examples of good and bad data