Bulk RNA-sequencing (RNA-seq) on the NIH Integrated Data Analysis Portal (NIDAP)

This page contains links to recorded video lectures and tutorials that will require approximately 4 hours in total to complete.  It is your responsibility to complete all parts of this training prior to attending the Discussion course that you registered for. During the live Discussion seminar, you can ask any questions about RNA-seq or the NIDAP workflow. You can also reach out to us at NCIBTEP@mail.nih.gov with any questions.  We look forward to seeing you in class and hope you find these materials helpful in better understanding RNA-sequencing and the downstream analysis of RNA-seq workflows on NIDAP.

Please begin with the recorded lecture on the background and methodology of RNA-seq.  Then, follow instructions on how to log-into NIDAP and use Parts 1-4 of the tutorial to guide your own downstream analysis of an RNA-seq training dataset.  At the end of this course, please consider responding to the survey link at the bottom of this page to provide us with feedback and suggestions for improving this course in the future.

Troubleshooting Tips:

    • For security purposes, you must be on the secure NIH network to access NIDAP.  This means that you must either be:
      • On-campus and able to connect to the NIH secure network there
      • Off-campus while using a VPN to gain access to the NIH secure network
    • Use only the Chrome web browser to access NIDAP; other browsers (e.g. Safari, Edge, Firefox, etc.) are not supported at this time


Lecture: Bulk RNA-seq Analysis – Background & Methodology (Lecture Part A, Lecture Part B, Lecture Part C, Lecture Part D, PowerPoint Slides)

    • Introduction to course
    • RNA-seq: Background
    • RNA-seq: Overview of upstream analysis
    • RNA-seq: Overview of downstream analysis
    • Understanding filtering, normalization, and batch correction of RNA-seq data
    • RNA-seq: Experimental design considerations


Guided Tutorial:  Next, you will need to follow along with a guided video tutorial that will walk you through each step of the downstream analysis of Bulk RNA-seq datasets on NIDAP.  The videos for each part of this tutorial and a list of the topics discussed in each section are below.  To follow-along with these videos, open another tab in your Chrome web browser and log-in to NIDAP using your NIH credentials here:  https://nidap.nih.gov/

Remember that you will either need to be either on an NIH campus and connected to the secure network there or connected to the NIH network from off-campus using a VPN in order to access NIDAP.  This is necessary to ensure the privacy of the data hosted on NIDAP.


Tutorial Part 1:  Bulk RNA-seq Analysis on NIDAP – Accessing NIDAP and Preparing your Inputs (Tutorial Part 1A, Tutorial Part 1B)

    • How to access NIDAP
    • Orientation to the NIDAP home page
    • Accessing the Bulk RNA-seq training dataset
    • Launching your first code workbook with the training dataset
    • Orientation to the code workbook
    • Understanding the inputs needed to begin your analysis
    • Understanding your metadata
    • Template organization: layout and coloring


Tutorial Part 2: Bulk RNA-seq Analysis on NIDAP – The QC Path (Tutorial Part 2A, Tutorial Part 2B, Tutorial Part 2C)

    • Filtering out low count genes
    • Normalizing counts
    • Batch correction
    • Making and understanding your first sample-wise heatmap


Tutorial Part 3: Bulk RNA-seq Analysis on NIDAP – The DEG Path (Tutorial Part 3A, Tutorial Part 3B, Tutorial Part 3C, Tutorial Part 3D)

    • Differential Expression of Genes (DEG) analysis
    • Making and understanding your first Volcano plot
    • Making a Venn diagram of your DE genes across multiple contrasts


Tutorial Part 4: Bulk RNA-seq Analysis on NIDAP – The GSEA Path (Tutorial Part 4A, Tutorial Part 4B, Tutorial Part 4C)

    • Preranked Gene Set Enrichment Analysis (GSEA)
    • Visualizing and understanding the results of a GSEA finding


Course Survey:  Bulk RNA-seq Analysis on NIDAP (Course Survey)

    • Please consider taking this short survey to provide us with feedback and suggestions on how to improve this course in the future.


Thank you for your time and attention!  We hope to see you in this or another class soon!