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From FASTQ to Figures — RNA-Seq Edition

 A self-paced online course built for researchers to learn RNA-seq data analysis — in short video lessons that fit around a busy research schedule.

Who This Is For

  • You're a reseacher enerating RNA-seq data and need to analyze it yourself
  • You've been copy-pasting code from tutorials without really understanding what each step does
  • You want to stop depending on a bioinformatics core facility or a busy labmate for every analysis
  • You need a course that fits around experiments and deadlines, not a fixed weekly class schedule

 

Who this is NOT for: if you're already running your own RNA-seq pipelines confidently, this course will feel too basic. It's built for the transition point — from "I generate the data" to "I can analyze it myself."

How the Course Works

This course runs over several months and is built specifically around the reality of a doctoral candidate's schedule: concise, flexible, and self-paced.

  • Each module is broken into several short videos, typically 10–15 minutes each — watch whenever you have time, not when a schedule dictates
  • Most videos end with questions and exercises to deepen understanding before moving on
  • Regular live Q&A sessions via Zoom — ask questions, discuss exercises, and work through your own data with guidance
  • Rolling enrollment — join at any time, no need to wait for a fixed start date
  • Once enrolled, you keep access with an ongoing monthly live Q&A session — so support doesn't stop once you've finished the videos

What You'll Learn

Introductory Session Introduction to the platform and the course concept — what to know before getting started.

Module 1 — Setup & Quality Control Get your environment running (cloud-based, works on any laptop), understand what FastQC results actually mean, and learn to spot problems in raw data before they cost you a week of confusion later.

Module 2 — Alignment Map reads to a reference genome, understand what "mapping rate" and "duplication rate" numbers are telling you, and learn to sanity-check your own output.

Module 3 — Counting & Normalization Turn aligned reads into a count matrix. Understand why normalization matters and what happens when it's skipped.

Module 4 — Differential Expression Analysis Introduction to R, and running DESeq2 (or edgeR) — understand p-values vs. adjusted p-values, and learn to read a volcano plot like you wrote it yourself.

Module 5 — Downstream Analysis & Figures Pathway analysis, functional annotation, and building the publication-ready figures your paper or presentation actually needs.

Format

  • Short video lessons (10–15 min each) — rewatch anytime, work through at your own pace
  • Questions and exercises after most videos to reinforce what you've learned
  • Live Zoom Q&A sessions — regular during the course, then monthly on an ongoing basis
  • Rolling enrollment — start whenever you're ready, no fixed cohort start date
  • Mode of instruction: English
Contact us

Your Trainer

Dr. Michaela Höhne-Wiechmann

Biologist by training, she moved into bioinformatics toward the end of her PhD and throughout her postdoc — teaching herself along the way, detours and frustration included. Today, she works as a Scientist in Bioinformatics at TRON gGmbH in Mainz, where she's the go-to expert for RNA-seq analysis and training. She has also built and maintains an internal R package for differential expression analysis.

It's exactly that self-taught experience that shaped this course: built by a biologist, for biologists, so you can finally analyze — and truly understand — your own data.

Call To Action

What participants say:

1) It is really step by step. 2) To be honest, I don’t think I can remember all the steps... Therefore it is really good that we can access the material for lifetime.

C Leung  - PhD Candidate QBM LMU Munich

 

The course was very suitable for beginners, Michaela was always available if there were any questions and she answered quickly.

Sebastian Kraus-Römer  - PhD Candidate UoC

Michaela was always available to answer any questions, the provided material is excellent and comprehensive. 

Alice Descoeudres -  PhD Candidate QBM LMU Munich