Course Details

  • Date: April 26th, 2017 - April 26th, 2017
  • Time: 11:00 am - 4:00 pm
  • Location: NCI-F Bldg549, Scientific Library Training Room
  • Presenter(s):

The CCR Bioinformatics Training and Education Program (BTEP) is pleased to organize a workshop on Partek Genomics Suite for scientists at NCI-Frederick.

Partek® Genomics Suite® is a versatile scientific software with an easy-to-use graphical interface for expression and genomic data analysis, as well as statistics and visualization needs. There are comprehensive workflows for many data types, including microarray, qPCR platforms, and NGS as well. This workshop will focus on the gene expression workflows from microarray, discuss other relevant analytical modules available in the PGS system, and provide attendees an opportunity to perform hands-on training on this software.

Date: Wednesday, April 26, 2017

Time: 11:00 am – 4:00 pm

Location: NCI-F Building 549, Scientific Library Training Room

Presenter: Eric Seiser, PhD – Partek Field Application Specialist

Registration is required.

Note: The workshop is limited to 12 seats (10 seats with desktops available for use, and 2 seats for those who can bring their own laptops).


For more information about the venue, please contact:

Alan Doss
Informationist, Scientific Library
Phone: 301-846-1093

 
WORKSHOP AGENDA
 

11:00 am – 12:00 pm               Introduction to Partek Genomics Suite

This will be an overview of the software, including statistics, workflows and other analytical modules relevant for gene expression analysis.

12:00 – 1:00 pm                        LUNCH BREAK

1:00 – 4:00 pm                         Comprehensive hands-on training on PGS

This session will be covering end-to-end analysis of gene expression data (example set will be provided). The complete gene expression analysis workflow will be followed that includes QA/QC of the data, differential expression detection, and biological interpretation using Partek Pathway. The presenter will also show participants how to perform batch effect removal, integration with other relevant data, and generating applicable visuals for the data.