Course: Transcriptomics credits: 10

Course code
BFVH24TRANSCRIPT
Name
Transcriptomics
Study year
2024-2025
ECTS credits
10
Language
Dutch, with parts in English
Coordinator
M. Kempenaar
Modes of delivery
  • Project-based learning
Assessments
  • Transcriptomics Paper - Other assessment
  • Transcriptomics Project - Other assessment

Learning outcomes

This module has the following learning outcomes:   

  1. You write a project proposal in which you explain the processing steps to be followed and the materials to be used in the context of selected research found in a relevant database. 

  1. You apply statistical analyses to transcriptomics data in relation to phenotypes and external factors and visually represent and interpret the results. 

  1. You link biological knowledge through annotation to the findings. For example, interpreting the effects of a transcriptomics experiment based on pathway and/or (gene-set) enrichment analysis results. 

  1. You can develop a basic interactive R Shiny dashboard displaying the relevant results from the downstream DEG-analysis within the biological context. 

  1. You describe the findings and key results of the analysis in article form according to the style of a relevant journal. 

 

Content

This project revolves around the analysis of gene expression data. The student will start by browsing for a topic of interest in a vast repository of published gene expression data sets (originating from RNA-Seq experiments). Data will be analysed in depth using the statistical programming language R. This includes exploratory data analysis, normalization, descriptive statistics and identifying relevant cases of differential gene expression. 

We first begin with reading through available material after selecting a suitable data set, such as the accompanying article and the NCBI GEO entry. This information can be used to write a project proposal in which both the experimental setup of the chosen data set, a listing of tools (limited to R libraries and possibly external webservices) to be used and a summary of the proposed analysis are explained.  

Special care is taken regarding the quality control of the (raw) read counts and – once satisfied – we use the chosen method to determine the DEGs given the biological questions we want to answer.  

The end products are a lab-journal reproducibly logging the complete workflow, a publication-style report of the research and findings as well as an interactive dashboard created with R Shiny where further downstream DEG analysis results will be displayed.  

Included in programme(s)

School(s)

  • Institute for Life Science & Technology
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