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      RNA-Seq Analysis

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Introduction and Lectures
Intro to the Workshop and Core
Schedule
What is Bioinformatics/Genomics?
Experimental Design and Cost Estimation
RNA Sequencing Technologies - Dr. Lutz Froenicke
Support
Zoom
Slack
Cheat Sheets
Software and Links
Scripts
Prerequisites
CLI
R
Cluster Computing
Data Reduction
Files and Filetypes
Prepare dataset
Preprocessing raw data
Indexing a Genome
Alignment with Star
Generating counts tables
Alignment/Counts with Salmon (Extra)
Data analysis
Prepare R for data analysis
Annotation from BioMart
Differential Expression Analysis
DE Quizzes R Code
Pathway Analysis
Enrichment Quizzes R Code
Comparison between STAR and Salmon
ETC
Closing thoughts
Workshop Photos
Github page
Report Errors
Biocore website

R and Rstudio

The analyses in this course are done in R, using the Rstudio environment. Some familiarity with R will help you make the most of your time. While we will provide R code that can be run even if you have never used R before, we find that participants who have spent some time exploring R before the course get the maximum value out of our workshops. There is not be enough time to provide full instruction in introductory R, and still get through all of the material we need to cover.

Beginners without any previous knowledge will be able to complete this course, and achieve a more thorough understanding of the techniques and analyses covered, but will probably not be able to conduct an experiment on their own.

If you don’t have any experience with R (or it has been a while) working through the first part of the Introduction to R for Bioinformatics course should give you a good foundation for this workshop. Those who are interested in running their own analyses may want to keep going through parts two and three, or even register for the introduction to R course.