Case Studies in Functional Genomics
offered by Harvard University

Private University
3 Campuses
Estd. 1636

Case Studies in Functional Genomics
at
Harvard University
Overview

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

Duration	5 weeks
Mode of learning	Online
Difficulty level	Advanced
Official Website	Go to Website
Course Level	UG Certificate

Case Studies in Functional Genomics
at
Harvard University
Highlights

Earn a certificate of completion

Case Studies in Functional Genomics
at
Harvard University
Course details

Skills you will learn

R Statistics Quality control Data analysis Data Science

What are the course deliverables?

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

More about this course

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.

Case Studies in Functional Genomics
at
Harvard University
Curriculum

Data Science, Data Analysis, R, Statistics, Biology, Genetics

Case Studies in Functional Genomics
at
Harvard University
Faculty details

Rafael Irizarry

Designation : Professor of Biostatistics, T.H. Chan School of Public Health

Michael Love

Designation : Assistant Professor, Departments of Biostatistics and Genetics, UNC Gillings School of Global Public Health

Vincent Carey

Designation : Professor of Medicine (Biostatistics) in the Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School