|Teaching method||Contact hours|
|Course coordinator(s)||dr. S Smit|
|Lecturer(s)||dr. S Smit|
|dr. M Suarez Diez|
|prof. dr. ir. D de Ridder|
|Examiner(s)||prof. dr. ir. D de Ridder|
Language of instruction:
Assumed knowledge on:
INF-22306 Programming in Python.
Modern biology routinely generates huge amounts of data: sequences, from NGS experiments; quantitative data, from -omics experiments; and graphs, representing molecular interactions. At the heart of many bioinformatics applications are algorithms that handle such types of data in time- and memory-efficient ways. Almost invariably these algorithms optimize some criterion - e.g. alignment quality, energy function or probability measure - using the data available.
In this course, the main types of algorithms will be discussed, aiming to gain a deeper understanding of the computational strategies underlying these algorithms. This will allow students to recognize which type of algorithm will be applicable in the development of new bioinformatics tools to help answer new questions in (computational) biology.
After successful completion of this course students are expected to be able to:
- enumerate the main algorithmic approaches in bioinformatics and explain the differences between them;
- explain in detail the working of a number of fundamental bioinformatics algorithms;
- identify in a given algorithm the underlying criterion and the optimization scheme used;
- identify and explain algorithms discussed in state-of-the-art bioinformatics/computational biology literature.
- study theory and program practice problems.
- read and present literature.
Students will be graded based on reports handed in on the practical assignments (60%), on the presentations given (30%) and on participation in the discussion (10%). Each component needs a minimum mark of 5 to pass.
Literature and exercises are made available in electronic form during the course.
|Restricted Optional for:||MBF||Bioinformatics||MSc||6AF|