Introduction to Bulk RNAseq analysis (Fall 2025)

Bulk RNAseq Analysis course logo

A 3-day introductory course targeted towards SUND researchers to learn how to do RNA-seq analysis

This course is an introduction for how to approach bulk RNAseq data, starting from the sequencing reads. It will provide an overview of the fundamentals of RNAseq analysis, including read preprocessing, data normalization, data exploration with PCAs and heatmaps, performing differential expression analysis and annotation of the differentially expressed genes. Participants will also learn how to evaluate confounding and batch effects in the data. The course will further touch upon laboratory protocols, library preparation, and experimental design of RNA sequencing experiments, especially about how they influence downstream bioinformatic analysis.

By the end of this workshop, you will be able to:

  • Gain insight into how to design an RNAseq 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.

Requirements

Participants should have basic knowledge of RNA sequencing.
Participant should also possess basic knowledge of data science concepts, such as principal component analysis, clustering, and statistical testing.

Please come prepared for the first day of the course by following the directions provided in advance by course instructors regarding small set up tasks.

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 fee 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 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