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rockefeller cornell mskcc

Introduction to Differential Gene Expression Analysis using RNA-seq:
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Dates: [ one class in four parts ]

Part 1: Wednesday, August 16 - 11:30am to 2:30pm
Part 2: Friday, August 18 - 11:30am to 2:30pm
Part 3: Wednesday, August 23 - 11:30am to 2:30pm
Part 4: Friday, August 25 - 11:30am to 2:30pm

RNA-seq is a commonly used method of interrogating the transcriptome, enabling both measurement of gene expression levels and isoform quantification. There is a broad constellation of bioinformatics tools available for the analysis of the large datasets that result from an RNA-seq experiment. This workshop will review the appropriate selection and correct usage of these tools, and how these vary with the specific questions being investigated.

Instructor: Friederike Dündar (ABC, WCM)

Goals and Objectives:

This workshop will present current methods of mapping the reads generated by an RNA-seq experiment to a reference genome and assigning reads to genome features, and using the expression levels of those features to identify differentially expressed genes between conditions. At each step, we investigate some of the methods, their principles and biases. We will look at techniques to quantify our confidence in the results, and some of the pitfalls to be aware of.

At the end of this workshop, participants will have performed analysis of a realistic dataset, from data retrieval through differential gene expression, and have an appreciation of the available data sources and tools, be acutely aware of biases, and be able to use these insights to critically interpret published results.


The workflows taught in this workshop will be executed at the UNIX command line, or using R/RStudio. This is not a UNIX or R course, and participants must have a working knowledge of UNIX and R/RStudio.

Specifically, participants should be comfortable with basic operations in a UNIX/Linux environment, including (1) moving around the directory structure and viewing and manipulating files (mkdir, ls, head, cat, cut) (2) running programs, redirecting standard input and output, using the pipe (|) operator in the command shell; and (3) using the grep and sed commands and crafting basic regular expressions.

Needed R skills include familiarity with all data types, ability to subset lists, matrices and data frames, use factor levels, and generate plots with base R graphics.