Transcriptomics

Course manual 2022/2023

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

Attendance

Requirements of the programme concerning attendance (OER-B):

1. Attendance during practical components exercises is mandatory.

Additional requirements for this course:

Attendance during practical components exercises is mandatory. 

Absence must be reported to the coordinator.

Assessment

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

Timetable

The schedule for this course is published on DataNose.

Contact information

Coordinator

• dr. M.J. Jonker