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HarvardX: Case Studies in Functional Genomics

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

5 weeks
2–4 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

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Starts Mar 19
Ends Nov 27

About this course

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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.

These courses make up two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

Genomics Data Analysis:

This class was supported in part by NIH grant R25GM114818.

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At a glance

  • Language: English
  • Video Transcript: English
  • Associated skills:Open-Source Software, DNA Methylation, Statistical Inference, Statistics, Bioconductor (Bioinformatics Software), Quality Control, Life Sciences, Functional Genomics, Biology, R (Programming Language), Exploratory Data Analysis, Data Warehousing, Ribonucleic Acid Sequencing, Data Analysis, Matrix Algebra, Software Engineering, Linear Model, Experimentation

What you'll learn

Skip What you'll learn
  • Mapping reads
  • Quality assessment of Next Generation Data
  • Analyzing RNA-seq data
  • Analyzing DNA methylation data
  • Analyzing ChIP Seq data

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

This course is part of Data Analysis for Genomics Professional Certificate Program

Learn more 
Expert instruction
3 skill-building courses
Self-paced
Progress at your own speed
3 months
2 - 4 hours per week

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