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.
2-4 hours a week
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
Michael Love
Assistant Professor, Departments of Biostatistics and Genetics at UNC Gillings School of Global Public Health
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.