Introduction to Bulk RNAseq analysis (November 2024)
![Bulk RNAseq Analysis course logo Bulk RNAseq Analysis course logo](/images_new/courses/1100x600_course-images/1100x600_Course_Image_RNAseq.png)
A 3-day introductory course targeted towards SUND researchers to learn how to do RNA-seq analysis
This workshop contains a basic tutorial on how to approach bioinformatics analyses of bulk RNAseq data, starting from the count matrix.
By the end of this workshop, you will be able to:
- Gain insight into how to design an RNA-seq experiment
- Preprocess sequencing reads
- Analyze bulk RNAseq data using the R package DESeq2
- Know best practices for performing Differential Expression Analysis
- Annotate and interpret their results
The workshop material includes a tutorial on how to approach RNAseq data, starting from your sequencing data. Thus, the workshop only briefly touches upon laboratory protocols, library preparation, and experimental design of RNA sequencing experiments, mainly for the purpose of outlining considerations in the downstream bioinformatic analysis.
The workshop is based on the materials developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC), a collection of modified tutorials from the DESeq2 and R language vignettes.
This workshop is presented in collaboration between the Center for Health Data Science (HeaDS) and The Novo Nordisk Foundation Center for Stem Cell Medicine, (reNEW)
Interested?
➡️ If you are an employee, sign up here
➡️ If you are a PhD student, sign up here
Course Disclaimers:
- The course is free of charge and intended for all KU researchers, and especially PhD students, at SUND
- A no-show free will be implemented for registrants who do not attend the course
- If you are a PhD student and you would like to receive ECTS credits for participating in this course, you must enroll through the PhD school school system
- If you are not a PhD student and sign up via the PhD school registration link, you will be charged for the course
- Please be advised that course participation via the PhD school happens under the PhD school's terms and conditions which HeaDS has no influence over