General Information
| Course Code | X_405052 |
|---|---|
| Credits | 6 EC |
| Period | P1 |
| Course Level | 400 |
| Language of Tuition | English |
| Faculty | Faculty of Science |
| Course Coordinator | dr. J.M. Urtasun Elizari |
| Examiner | dr. ir. K.A. Feenstra |
| Teaching Staff |
dr. S. Abeln prof. dr. J. Heringa dr. Q. Peng MSc dr. J.M. Urtasun Elizari dr. ir. K.A. Feenstra dr. D. Molenaar dr. S. Pissis dr. M. Postma |
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Part of programme(s)
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Practical Information
You need to register for this course yourself. The (de)registration deadlines can be found on VU.nl
| Teaching Methods | Seminar, Computer lab, Written partial exam, Lecture |
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Target audiences
This course is also available as:
Course Objective
Are you interested in Bioinformatics? Do you want to find out howbiology can make an exciting application domain? Or do you want to learn
what more you could do with your data, and with less effort? Enter here
to start!
Fundamentals of Bioinformatics (FoB) is the starting course of the
Bioinformatics master. It aims to give you a broad overview of important
topics relevant to the field, with a focus on current open problems. You
will be made aware of these open problems during practical sessions that
will cover active research in a major Bioinformatics problem (mutation
impact prediction). Based on your background, you will be assigned to
separate classes where you will be working to fill gaps in your
background knowledge in programming and/or biology.
Course Objectives [Dublin descriptors]:
* To make you aware of gaps in your own background knowledge [learning
skills]
* To address gaps in your background knowledge (programming, mathematics
and molecular biology) and skills [knowledge and understanding]
* To make you aware of the major issues when developing and benchmarking
new methodology in bioinformatics research [making judgements]
* To work together in a group of diverse backgrounds, to discuss and
report the results [communication]
* To apply and understand fundamental methods in bioinformatics, e.g.
sequence alignment, impact prediction, and to understand and use
fundamental concepts in bioinformatics, e.g. homology, evolutionary
conservation, and gene function [applying knowledge and understanding].
Course Content
Topics:• Evolution, genomics, sequence alignment, Blast/PSI-Blast, FAIR data,
benchmarking, machine learning, genome assembly, mutation impact
prediction, protein stucture analysis, substitution matrices, scientific
writing
Project:
• Group project to benchmark different mutation impact prediction
approaches. The groups will be composed to include different BSc
backgrounds.
Background knowledge (1 out of 3, see below):
- Molecular cell biology
- Programming and computational thinking in Python
- Calculus and linear algebra
Additional Information Teaching Methods
• 8 lectures (two-hour lecture in the morning, two days per week) Exercises during/in between lectures
• 12 computer practicals for the group project - compulsory attendance (online or on campus) (two hour sessions following the morning lectures, two days per week), supervised.
Feedback (theoretical and practical) will be given during the computer practical sessions.
Conversion classes for background knowledge:
- Molecular cell biology (7 x double lectures)
- Programming and computational thinking in Python (10 computer practicals)
Depending on their background, students will be assigned 1 of the 2 conversion classes. Students that are already well versed in both topics (molecular biology and programming) may be assigned to the following class, which is organised as part of the Introduction to Systems Biology:
- Calculus and linear algebra (10 maths classes)
Method of Assessment
• Programming or Biology classes (Maths classes will count towardsIntroduction to Systems Biology) [~30%]
• Project work [~30%] :
◦ Project reports
• Digital exam to assess [~40%]:
◦ Exercises and question (as provided in lectures)
◦ Concept learned during the project (individual)
◦ Lecture topics
Further assessment and grading details will be posted on Canvas (resits
and compensation rules)
Entry Requirements
Bachelor in any science discipline (including medicine), or strongprogramming background.
Literature
• course material (slides, scientific papers) on canvas.vu.nl• book: Marketa Zvelebil and Jeremy O. Baum Understanding Bioinformatics
Garland Science 2008 ISBN-10: 0-8153-4024-9
Additional Information Target Audience
M Artificial IntelligenceM Bioinformatics and Systems Biology
M Biomolecular Sciences
M Oncology
M Computational Science
Additional Information
BYOD policy (Bring Your Own Device)We expect students in this course to use their own laptop. This laptop
should at the very least support an SSH client, for remote shell access
to the VU Linux servers. We will help you to set this up.