In this course on case studies in Functional Genomics you will perform data analysis on RNA-Seq, ChIP-Seq, and DNA methylation, using open-source software, including R and Bioconductor.
About this course
A detailed explanation of 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, you will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment. You start with RNA-seq data analysis covering basic concepts and the first look at FASTQ files.
You 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, you will have covered RNA-seq at the transcript level: inferring expression of transcripts (i.e. alternative isoforms); differential exon usage. You will be able to 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 reading alignment to peak calling, and assessing differential binding patterns across multiple samples.
How to take this course?
Given the diversity in the 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.
This is the second course in the Genomics Data Analysis XSeries from HarvardX. We assume that you have taken the first course in the series, PH525.5x Introduction to Bioconductor, or have similar content knowledge. This series is designed to be taken after the Data Analysis for the Life Sciences XSeries, but can also be taken independently if you have a basic working knowledge of R.
These courses makeup two Professional Certificates and are self-paced:
- PH525.1x: Statistics and R for the Life Sciences
- PH525.2x: Introduction to Linear Models and Matrix Algebra
- PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
- PH525.4x: High-Dimensional Data Analysis
- PH525.5x: Introduction to Bioconductor
- PH525.6x: Case Studies in Functional Genomics
- PH525.7x: Advanced Bioconductor
- This class was supported in part by NIH grant R25GM114818.
HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. R
What you will learn from Case Studies in Functional Genomics?
- Mapping reads
- Quality assessment of Next Generation Data
- Analyzing RNA-seq data
- Perform analysis on DNA methylation data
- Analyzing ChIP Seq data
- PH525.3x, PH525.4x
Introduction and Resources
- Course Materials and R Resources
Section 1: RNA-seq
- Introduction to RNA sequencing
- RNA-seq alignment
- Normalization and EDA of gene counts
- Differential expression at gene-level
- Differential expression across isoforms
Section 2: DNA Methylation
- Introduction to DNA methylation
- DNA Methylation Measurement
- Data Analysis and Integration
- Whole Genome Analysis
Section 3: ChIP-seq
- Introduction to ChIP-seq
- Advanced ChIP-seq Analysis
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- Harvard University
- Online Course
- 1-3 Months
- Free Course (Affordable Certificate)
- Bioinformatics Data Analysis Genomics Research methodology