Transcriptomics

6 EC

Semester 1, period 1

5234TRAN6Y

Owner Master Biomedical Sciences
Coordinator dr. M.J. Jonker
Part of Master Biomedical Sciences, track Oncology, Master Biomedical Sciences, track Cognitive Neurobiology and Clinical Neurophysiology, Master Biomedical Sciences, track Psychopharmacology and Pathophysiology, Master Biomedical Sciences, track Infection and Immunity, Master Biomedical Sciences, track Cell Biology and Advanced Microscopy, Master Biomedical Sciences, track Experimental Internal Medicine, Master Biomedical Sciences, track Molecular Neurosciences, Master Biomedical Sciences, track Physiology of Synapses and Networks, Master Biomedical Sciences, track Developmental and Therapeutic Biology, Master Biomedical Sciences, track Medical Biochemistry and Biotechnology, Major Big Biomedical Data Analysis, year 1Master Biomedical Sciences, track Cardiovascular Sciences, year 1

Course manual 2025/2026

Course content

Modern biology and biomedical research is undergoing an historical transformation, becoming –among other things – increasingly data driven. New technologies that enable the analysis of complete genomes generate large data sets, and combining statistical, computational, and biological methods has become important in modern genomic research. In this course we teach to MSc Life Sciences students how bioinformatics tools and methods are used to analyze transcriptomics data. The students will get hands on experience with analyzing different types of transcriptomics data, such as mRNA sequencing data, small RNA sequencing data, and single cell sequencing data. We will teach how to work with open-source bioinformatics software. This course is part of the Big Biomedical Data Analysis Major program.

Study materials

Literature

  • Available on Canvas

Practical training material

  • Available on Canvas

Software

  • R or RStudio, All other software will be provided.

Objectives

  • Explain basic principles of transcriptomics technologies.
  • Maintain Bioinformatics Labjournal
  • Advanced scripting skills for relevant programming languages; R and Linux bash.
  • Explain the (statistical) concepts and principles of some selected relevant bioinformatics methods that are required for analyzing and interpreting large scale transcriptomics data sets.
  • Is able to clearly structure complex omics data/display in clear figures and tables
  • Demonstrate that he/she can use selected relevant transcriptomics bioinformatics methods and tools in a correct way, and such that answers can be formulated to biomedical research questions.
  • Demonstrate that he/she can formulate relevant data analysis questions, generate results, and interpret the results such that these data analysis questions can be answered.
  • Demonstrate that he/she can experiment with large scale omics data sets in a data-driven research approach.

Teaching methods

  • Lecture
  • Computer lab session/practical training
  • Presentation/symposium
  • Self-study
  • Working independently on e.g. a project or thesis
  • Supervision/feedback meeting

Lecture: Theoretical background.

Computer lab session/practical training: Training in using software, computer languages, big data analysis, data visualization and statistics.

Presentation: Formulating answers to biomedical and biological research questions.

Self-study: Training in using software, computer languages, big data analysis, data visualization and statistics.

Supervision/feedback meeting: Training in using software, computer languages, big data analysis, data visualization and statistics.

Working independently on a project: Learning how to use transcriptomics data analysis and bioinformatics to answer biomedical and biological research questions.

Learning activities

Activity

Hours

Laptop practicals / lectures

150

Self study

18

Total

168

(6 EC x 28 uur)

Attendance

  • Some course components require compulsory attendance. If compulsory attendance applies, this will be indicated in the Course Catalogue which can be consulted via the UvA-website. The rationale for and implementation of this compulsory attendance may vary per course and, if applicable, is included in the Course Manual.

    • Additional requirements for this course:

    Attendance during practical components exercises is mandatory. 

    Absence must be reported to the coordinator.

    Assessment

    Item and weight Details

    Final grade

    1 (50%)

    smallRNAseq assignment

    1 (50%)

    scRNAseq assignment

    Inspection of assessed work

    This is communicated by email

    Assignments

    The computer practicals can be made in groups and are not graded, feedback is provided directly, and in plenary sessions.

    The assignments are sometimes made in groups, but also sometimes individually, and are graded (see assessment).

    Fraud and plagiarism

    The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

    Course structure

    Weeknummer Onderwerpen Studiestof
    1 Bulk RNAseq, model and non-model organisms see Canvas
    2 Small RNAseq, with assignment see Canvas
    3 Biomarkers, predictive models see Canvas
    4 Single Cell RNAseq, with assignment see Canvas
    5    
    6    
    7    
    8    

    Contact information

    Coordinator

    • dr. M.J. Jonker