In-depth NGS Data Analysis Course

Audience Computational Skills Prerequisites Duration
Biologists Beginner/Intermediate None 12-day workshop (~78 hours of trainer-led time)

This repository contains all course materials for the Fall 2018 version of HBC’s In-depth NGS Data Analysis Course, a 12-day course run over 6 weeks.

These materials are developed for a trainer-led workshop, but they are also amenable to self-guided learning.

The course is aimed at bench biologists who are interested in learning about NGS-based genomic analysis. The topics covered in-depth during this course are analysis of RNA-Seq and ChIP-Seq data, with an optional Variant Calling session. The sessions will also include functional analysis downstream of sequence data processing. During this course, participants will gain skills in the areas of:

At the end of this course, participants can expect to have the expertise to independently run data analysis for sequencing experiments.

No prior programming experience or command-line training is required.

This repo contains the materials for the six sessions of the course.


Description Time/Duration
Introduction to bash, O2 and NGS Data Analysis 2 Days
Differential Gene Expression Analysis Part I 2 Days
Differential Gene Expression Analysis Part II 2 Days
Functional Analysis & other RNA-seq applications 2 Days
ChIP-seq 2 Days
Variant Calling, Version Control 2 Days

NOTE: Additional materials are included in this repo but are not part of the main course.

Installation Requirements

Download and install the following programs:

NOTE: If you are going to be using a Mac laptop please use the instructions here to determine your OS X version. If you find that your Mac is running a version older than OS X 10.6, please email us.

Mac OS specific download:

Windows OS specific download:

These materials have been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.