This sequence provides a detailed explanation of the process of RNA-seq analysis and teaches you to the skills to carry out your own analyses. You will explore real RNA-seq data from the fruit-fly midgut. You will learn how to use several different R packages for RNA-seq analysis, as well as learning how to prepare and reformat your data so that it can be analyzed by these programs.
- Basic R skills
- Transcription (Biology)
- Sequencing Technology
- Log-scaling of data
- Scientific Notation
- Standard R packages:
- BioConductor packages:
For each tutorial, download the file and open it using RStudio. You may need to right-click the link and choose “Download File”. Make sure you save it as a .Rmd file - some browsers like to save it as a text file by adding .txt to the end of the file name.
- Compare and contrast the genome (DNA) and transcriptome (RNA)
- Explain what is being measured by RNA-seq
- Describe the steps involved in RNA-seq
Explore Read Count Data (HTSeq files)
- Understand the columns and rows of an HTSeq file
- Use R to generate basic statistics for HTSeq files
- Create and analyze histograms from HTSeq files
Calculate Differential Expression with DESeq2
- Calculate differential expression statistics using DESeq2
- Extract DESeq2 results and reformat them to be more useful for future analysis
- Understand the columns and rows of DESeq2 results
- Use R to extract results for a single gene of interest
- Use R to create a list of significantly different genes for different significance cutoffs.
More coming soon!
Check back soon for more tutorials teaching the nitty-gritty details of analyzing RNA-seq data!