Case Studies in Functional Genomics

Perform the standard processing and normalization steps

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

Featuring faculty from:
Self-Paced
Length
5 weeks
2-4 hours a week
Certificate Price
$219
Program Dates
Start Case Studies in Functional Genomics Today.

What You'll Learn

We will explain how to perform the standard processing and normalization steps, starting with raw data, to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment. We start with RNA-seq data analysis covering basic concepts and a first look at FASTQ files. We will also go over quality control of FASTQ files; aligning RNA-seq reads; visualizing alignments and move on to analyzing RNA-seq at the gene-level : counting reads in genes; Exploratory Data Analysis and variance stabilization for counts; count-based differential expression; normalization and batch effects. Finally, we cover RNA-seq at the transcript-level : inferring expression of transcripts (i.e. alternative isoforms); differential exon usage. We will learn the basic steps in analyzing DNA methylation data, including reading the raw data, normalization, and finding regions of differential methylation across multiple samples. The course will end with a brief description of the basic steps for analyzing ChIP-seq datasets, from read alignment, to peak calling, and assessing differential binding patterns across multiple samples.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

The course will be delivered via edX and connect learners around the world. By the end of the course, participants will understand the following concepts:

  • Mapping reads
  • Quality assessment of Next Generation Data
  • Analyzing RNA-seq data
  • Analyzing DNA methylation data
  • Analyzing ChIP Seq data

Your Instructors

Image
Rafael Irizarry

Rafael Irizarry

Professor of Biostatistics at Harvard University
Read full bio.

Image
Michael Love

Michael Love

Assistant Professor, Departments of Biostatistics and Genetics at UNC Gillings School of Global Public Health
Read full bio.

Image
Vincent Carey

Vincent Carey

Professor, Medicine at Harvard Medical School
Read full bio.

Ways to take this course

When you enroll in this course, you will have the option of pursuing a Verified Certificate or Auditing the Course.

A Verified Certificate costs $219 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate. 

Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums. Please note that this track does not offer a certificate for learners who earn a passing grade.

Read More

Introduction to Data Science with Python

Learn Python for data analysis

Join Harvard University Professor Pavlos Protopapas, in this online course to learn how to use Python to harness and analyze data.

Read More

Advanced Bioconductor

Speed discovery and interpretation

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

Read More

Introduction to Bioconductor

Learn what we measure and why in relevant biology.

Join Harvard faculty in this online course to learn the structure, annotation, normalization, and interpretation of genome scale assays.