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Analyzing Genomic Data in R

Are you interested in analyzing next-generation sequencing data but lacking in strong computational skills? In this skills track, geared towards non-computational biologists, you will learn to use Bioconductor, the specialized repository for bioinformatics software, along with essential Bioconductor packages. Then, you'll learn about current best-practice workflows for RNA sequencing differential expression analysis, as well as Chip-sequencing data.

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1
R
Introduction to Bioconductor in R

Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!

4 hours

James Chapman Headshot

James Chapman

Curriculum Manager, DataCamp

2
R
RNA-Seq with Bioconductor in R

Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

4 hours

Mary Piper Headshot

Mary Piper

Bioinformatics Consultant and Trainer

3
R
Differential Expression Analysis with limma in R

Learn to use the Bioconductor package limma for differential gene expression analysis.

4 hours

John Blischak Headshot

John Blischak

Postdoctoral Scholar at University of Chicago

4
R
ChIP-seq with Bioconductor in R

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

4 hours

Peter Humburg Headshot

Peter Humburg

Statistician

Track statement of accomplishment
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