RNA-seq data Analyses training

• The training is divided in 2 parts of 4 sessions

• Each session will alternate between:

  • theoretical subparts with examples on a training dataset
  • practical subparts on your data

• Training supports were derived or adapted from great online tutorials. Don’t hesitate to go and see to complete this training:

  • https://github.com/hbctraining/Intro-to-rnaseq-hpc-salmon-flipped/blob/main/schedule/links-to-lessons.md
  • https://github.com/hbctraining/DGE_workshop_salmon_online/blob/master/schedule/links-to-lessons.md
  • https://nbisweden.github.io/workshop-ngsintro/2302/index.html

Dates:

• Tuesday, 2026/02/24 13:30-17h

• Thursday, 2026/02/26 13:30-17h

• Tuesday, 2026/03/03 13:30-17h

• Thursday, 2026/03/05 13:30-17h

• Tuesday, 2026/03/10 13:30-17h

• Thursday, 2026/03/12 13:30-17h

• Tuesday, 2026/03/17 13:30-17h

• Thursday, 2026/03/19 13:30-17h

• Tuesday, 2026/03/24 13:30-17h

• Thursday, 2026/03/26 13:30-17h

• Tuesday, 2026/03/31 13:30-17h

Part 1: Raw reads to count table

• Session 1: Overview and first steps: Biological samples to fastq files
• Session 2: Data checking and cleaning
• Session 3: Aligning reads in theory & public reference genomic database
• Session 4: Aligning reads: in practice and mapping visualization

For those who have never done R, take a look to sessions 1 to 6 of the R for beginners tutorial

https://can.gitbiopages.ens-lyon.fr/R_basis/

Part 2: Count table to differentially expressed genes

• Session 5: Set up the statistical analysis and exploring your count table
• Session 6: Identifying and differentially expressed genes
• Session 7: Visualizing differentially expressed genes
• Session 8: Making functional analysis

Training machine will be available until end of June

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These materials have been developed by members of BIBS team of the CIRI (https://ciri.ens-lyon.fr/). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

• Some materials used in these lessons were derived or adapted from work made available by the Harvard Chan Bioinformatics Core (HBC) (https://github.com/hbctraining) under the Creative Commons Attribution license (CC BY 4.0).

sources

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