Course Details

  • Date: March 29th, 2017 - March 29th, 2017
  • Time: 2:00 pm - 4:30 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 Flow for scientists at NCI-Frederick.

Partek Flow software is designed specifically for the analysis needs of next generation sequencing (NGS) applications including RNA, small RNA, and DNA sequencing. With an easy-to-use graphical interface, one can perform alignment, quantification, quality control, statistics and visualization for their NGS data.

Date: Wednesday, March 29, 2017

Time: 2:00 – 4:30 pm

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

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

2:00 – 4:30 pm               RNA-Seq Analysis using Partek Flow
                                       Presenter: Eric Seiser, PhD – Partek Field Application Specialist
An overview of getting started on the NIH Helix server and then a live demo of RNA-seq analysis in Partek Flow. The training will highlight key concepts in RNA-seq analysis and their implementation Flow.  This will be followed by a hands-on session utilizing Partek Flow to importing raw sequence data in fastq format from a published study, followed by performing QA/QC, alignment, quantification, differential expression detection and finally biological interpretation.

Students will learn how to use basic features of Partek Flow, including:

    •    Getting set up on NIH Helix server
    •    Importing data
    •    Performing QA/AC
    •    Alignment
    •    Gene/transcript abundance estimation
    •    Differential expression detection
    •    Go Enrichment analysis
    •    Visualization (PCA, dotplot, volcano plot, chromosome view, hierarchical clustering etc.)
    •    Microarray analysis and integration with RNA-seq data