Advanced Methods in Bioinformatics

6 ECTS credits
180 h study time

Offer 1 with catalog number 4018221FNR for all students in the 2nd semester at a (F) Master - specialised level.

Semester

2nd semester

Enrollment based on exam contract

Impossible

Grading method

Grading (scale from 0 to 20)

Can retake in second session

Yes

Taught in

English

Partnership Agreement

Under agreement for exchange of courses

Faculty

Faculty of Sciences and Bioengineering Sciences

Department

Computer Science

Educational team

Wim Vranken (course titular)

Activities and contact hours

26 contact hours Lecture
26 contact hours Seminar, Exercises or Practicals
12 contact hours Independent or External Form of Study

Course Content

This course focuses on algorithms and methods in computational biology, with active participation of the students in implementing such algorithms. The course covers methods and associated algorithms for solving problems related to i) protein sequences, protein evolution and protein structure, as well as ii) Next Generation Sequencing data analysis and strategies for the discovery of regulatory motifs, in particular genomics and transcriptomics data in relation to cancer.

The aims of the course are to i) familiarise students with the background and concepts in these two fields, ii) give students insight in how data, computational methodology and results are interconnected in Computational Biology and Bioinformatics, iii) enable the student to implement algorithms in these fields and iv) critically investigate the results produced by such algorithms. This knowledge should enable the students to understand the literature on bioinformatics and computational biology.

This is achieved through a) lectures to transfer the basic concepts, including article fragments to train the student in how to extract information from scientific literature, b) a project where a relevant scientific article is reimplemented, critically assessed and written up in article form (individual or in student pairs), c) a peer review stage where students review each others' projects and provide critical feedback and d) a final open exam presentation on the project with questions from peers and instructors.

Course material

Handbook (Recommended) : Protein bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis, An Algorithmic Approach to Sequence and Structure Analysis, Ingvar Eidhammer, Inge Jonassen and William R. Taylor, Wiley, 9780470848395, 2004
Handbook (Recommended) : The molecular biology of the cell, Bruce Alberts, Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, and Peter Walter, The fourth edition, pubmed, 9780815344643, 2014
Handbook (Recommended) : Understanding bioinformatics, Marketa Zvelebil, Jeremy O. Baum, Garland Science, 9780815340249, 2007

Additional info

Students are expected to have intermediate or better skills in programming. All lectures will be provided in hybrid form.

Learning Outcomes

General competences

Recognition of key terms in computational biology and bioinformatics, and awareness of algorithms to solve biological or medical questions. Implementation of an algorithm from computational biology or bioinformatics.

Capacity to communicate

Ability to communicate constructively with peers in a joint project, and the clear and succinct presentation of information contained in a scientific article.

Capacity to evaluate algorithm

Understanding the concepts behind computational biology and bioinformatics, and application of this knowledge to algorithms that analyse or predict biological data. This includes awareness of pitfalls in using biological data, such as data quality, and bias or overlap in the data used.

Analyzing results

Analysis of the outcomes of your algorithms, with critical assessment of whether they perform correctly (or not).

Evaluating others

Evaluation of a presentation on a scientific article by your peers, including giving constructive and critical feedback about any concerns you might have.

Synthesize an article

Critical reading and active implementation of a scientific article in this field, with synthesis of the key points, to be communicated in a presentation.

Grading

The final grade is composed based on the following categories:
SELF Presentation determines 75% of the final mark.
SELF Paper determines 25% of the final mark.

Within the SELF Presentation category, the following assignments need to be completed:

Oral exam presentation with a relative weight of 1 which comprises 75% of the final mark.

Note: The oral examination consists of 1) presentation of a scientific article and 2) defense of one of the assignments.

Within the SELF Paper category, the following assignments need to be completed:

project report as manuscript with a relative weight of 1 which comprises 25% of the final mark.

Note: The students implement two assignments linked to specific course topics.

Additional info regarding evaluation

The evaluation is based on a project where an existing published method from the field is re-implemented and presented (at the exam, individual or in groups of two). Grades are assigned the report (25%) and the exam presentation (75%), with the following parameters taken into account:

- the quality of the project in terms of the implementation and the analysis of the results

- the presentation of the article

- critical insights by the student

- quality of the peer review and participation during the exam.

All source material has to be correctly referenced: plagiarism of code or text is not allowed and will result in a zero score and possible disciplinary sanctions.

Allowed unsatisfactory mark

The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context