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RNA-seq data analysis with Chipster
Date: 05.03.2020 9:00 - 05.03.2020 17:00
Location details: The event is organised at the CSC Training Facilities located in the premises of CSC at Keilaranta 14, Espoo, Finland. The best way to reach us is by public transportation; more detailed travel tips are available.
Language: english-language
lecturers: Eija Korpelainen
  • 60 for-finnish-academics
  • 280 for-others
The fee covers all materials, lunches as well as morning and afternoon coffees.
The course is full. If a cancellation is received five (5) business days prior to the course, the course fee will be refunded with the exception of a handling fee of 10 €. For no-shows and cancellations after the cut of date no refunds will be made. Registration can be transferred to someone else from the same organization without additional charge.
Additional Information

This hands-on course introduces the participants to RNA-seq data analysis methods, tools and file formats. It covers the whole workflow from quality control and alignment to quantification and differential expression analysis. The free and user-friendly Chipster software is used in the exercises, so no previous knowledge of Unix or R is required.

The course consists of lectures and practical exercises. The lectures are available as short videos, and the participants are requested to view them prior to the course. This gives you more time to reflect on the concepts so that you can use the classroom time more efficiently. The lectures are summarized and questions answered during the course. You can also submit questions prior to the course.

You will learn how to

  • check the quality of reads with FastQC
  •  remove bad quality data with Trimmomatic
  •  infer strandedness with RseQC
  •  align RNA-seq reads to the reference genome with HISAT2 and STAR
  •  perform alignment level quality control using RseQC
  •  quantify expression by counting reads per genes using HTSeq
  •  check the experiment level quality with PCA plots and heatmaps
  •  analyze differential expression with DESeq2 and edgeR
  •  take multiple factors (including batch effects) into account in differential expression analysis

Target audience: Life scientists who are planning to use RNA-seq in their research. This course is suitable also for those researchers who do not plan to analyse data themselves, but who need to understand the concepts in order to discuss with bioinformaticians.

Course materials