Course Details

  • Date: November 13th, 2017 - November 13th, 2017
  • Time: 9:30 am - 4:00 pm
  • Location: NIH Bldg 10, FAES Room 4 (B1C205)
  • Presenter(s): Matthew Wampole (Clarivate Analytics)

MetaCore™ is an integrated curated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, RNAi screens, gene variants, proteomics, metabolomics, Co-IP pull-out and other custom interactions which can all by analyzed in tandem.

In this workshop, the attendees will be analyzing data about NOTCH1 signaling in chronic lymphocytic leukemia: http://www.pnas.org/content/114/14/E2911.abstract (PNAS publication).

You’ll explore pathways enriched by ICN1-HA up-regulated genes from RNA-seq data, and bound to NOTCH1 in ChIP-seq experiments. The morning session will cover different basic modules available in MetaCore to extract meaningful information. The afternoon session will focus on advanced modules, and also introduce Key Pathway Advisor (KPA), which is a recent web application developed for easy biological pathway analysis of OMICs data.

NOTE: This is a BYOC (Bring your own laptop Computer) class. Government issued or personal computers are permitted. We will be able to supply a very limited set of computers, so if you want to take the class but cannot bring your own computer please indicate such in the Comment section on the registration form.

AGENDA

9:30am – 12:30pm

  • MetaCore Basics Training
    • MetaCore Overview
    • EZ searching for interactions and pathways involved with NOTCH1
    • Uploading data into MetaCore.
    • Pathway analysis of ICN1 RNA-seq data bound to NOTCH1 in ChIP-seq experiments.
    • Compare pathways enriched by leading edge genes from peripheral blood of chronic lymphocytic leukemia patients.

1:30pm – 4:00pm

  • MetaCore Advanced Training
    • Use overconnectivity analysis to associate transcription factors regulating the ICN1 RNA-seq dataset.
    • Find other publicly available GEO microarray datasets to find similar signatures in other diseases.
    • Identifying causal networks and synergistic pathways from ICN1 RNA-seq data bound to NOTCH1 in ChIP-seq experiments using Key Pathway Advisor.

Course Material 1: PDF icon PNAS-NOTCH1-CLL-MetaCoreWorkshop-13Nov2017.pdf

Course Material 2:  MetaCore-TrainingAbstract.docx

Course Material 3:  GSE92626-ICN1-HAvsGFP.txt

Course Material 4:  CLL-patientPBsamples.txt