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From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline
Below are video tutorials for this GTN material, created for various (past) events.
Tutorial Video ()
RNA sequencing (RNA-seq) has become a very widely used technology for
profiling gene expression. One of the most common aims of RNA-seq profiling
is to identify genes or molecular pathways that are differentially expressed
(DE) between two or more biological conditions. This article demonstrates a
computational workflow for the detection of DE genes and pathways from RNA-seq
data by providing a complete analysis of an RNA-seq experiment profiling
epithelial cell subsets in the mouse mammary gland. The workflow uses R
software packages from the open-source Bioconductor project and covers all
steps of the analysis pipeline, including alignment of read sequences, data
exploration, differential expression analysis, visualization and pathway
analysis. Read alignment and count quantification is conducted using the
Rsubread package and the statistical analyses are performed using the edgeR
package. The differential expression analysis uses the quasi-likelihood
functionality of edgeR.
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