BioInformatics I

Course manual 2023/2024

Course content

In general, a limited number of students sign up for Bioinformatics I and II. Therefore, in these courses, we define individual projects for the students who are interested taking one of these courses. Bioinformatics I and II both focus on computational/bioinformatics methods used to analyse biological systems.

Bioinformatics I and Bioinformatics II are identical courses. You can participate in one of these courses or in both. In case you participate in both courses, then in one of the courses you will have to work on a bioinformatics project, while in the other course you have to work on a computational modelling project.

The students that sign up for this course will be divided into a maximum of three different groups depending on the number of students that enrol the course. Each group will work on a specific project. The three projects are:

1. Analysis of B-cell receptor sequencing data (bioinformatics; supervisor: Barbera van Schaik)
2. DNA methylation prediction using machine learning (bioinformatics; supervisor: Perry Moerland)
3. Computational modelling (differential equations) of plasma cell differentiation (computational modelling; supervisor: Antoine van Kampen)

We aim for equally sized groups.

During the kick-off meeting we will briefly present the three projects and you will be given the opportunity to switch preference (and project). The project may be tuned towards the background/skills of the student.

For this course we did not make a pre-defined schedule in Datanose. Instead, a schedule for the progress meetings (see below) and deadlines will be defined during the kick-off based on your availability. However, the assigned projects need to be finished within 6 weeks and, therefore, we will define a strict time schedule. Once you completed all parts of the project you will receive a grade.

Keep in mind that this is a 6EC course and, consequently, it requires a substantial amount of work to complete it successfully. 

 An individual project will be defined that comprises the following parts:

• Literature review.
• Practical work (bioinformatics, modelling).
• Two or three progress meetings during which the student present results from the literature review and practical work.
• Final presentation of all the work (30 minutes) for supervisor(s) and other students in the course (if any).
• Report (10-15 pages excluding references) describing the project, literature review and results from the practical work.

The grade will be based on the quality of the literature summary (20%), kick-off meeting/practical work/progress meetings (40%), final presentation (20%), and report (20%).

The project will be defined based on possibilities offered by the Bioinformatics Laboratory (AUMC), the interest(s) of the student, and the background/skills of student.

Study materials

Literature

• The required papers will be provided during the course

Software

• Use of open source software (R and Python)

Other

• Study material(s) will be made available to the student after a topic is chosen.

Objectives

• The course Bioinformatics I focusses on statistical methods for analysis of omics data and/or computational modelling of biological systems

Teaching methods

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

Individual student projects

Learning activities

Attendance

This programme does not have requirements concerning attendance (Ter part B).

Additional requirements for this course:

All meetings are mandatory. Absence needs to be communicated to the course coordinator to determine an alternative date.

Assessment

See general description for 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

Additional information

Recomended prior knowledge: Knowledge about statistics, modelling, and (molecular) biology will be an advantage

Contact information

Coordinator

• prof. dr. Antoine van Kampen