TDA507 / DIT742 Computational methods in bioinformatics lp2 HT19 (7.5 hp)
Course is offered by the department of Computer Science and Engineering
- Graham Kemp (Examiner), email@example.com
This course demonstrates how computational methods that have possibly been presented in other computing courses can be applied to solve problems in an application area.
We look at problems related to the analysis of biological sequence data (sequence bioinformatics) and macromolecular structures (structural bioinformatics). Computing scientists need to be able to understand problems that originate in areas that may be unfamiliar to them, and to identify computational methods and approaches that can be used to solve them. Biological concepts needed to understand the problems will be introduced.
This is an advanced level course which uses research articles as the main reference materials. Reading research articles is valuable training for scientists and researchers.
These demonstrate how to present ideas and methods, and how to critically evaluate them. Developing skill in reading research articles is useful preparation for future scientific investigations, and one's own scientific writing can improve through reading.
Computational methods and concepts featured in this course include: dynamic programming; heuristic algorithms; graph partitioning; image skeletonisation, smoothing and edge detection; clustering; sub-matrix matching; geometric hashing; constraint logic programming; Monte Carlo optimisation; simulated annealing; self-avoiding walks.
Biological problems featured in this course include: sequence alignment; domain assignment; structure comparison; comparative modelling; protein folding; fold recognition; finding channels; molecular docking; protein design.
Lecture slides; web-based resources; selected research articles
The preliminary schedule for the course is as follows:
|Wed 2019-11-06||Sequence alignment|
|Mon 2019-11-11||Protein conformation; protein domains|
|Wed 2019-11-13||Macromolecular structure determination by X-ray crystallography|
|Mon 2019-11-18||Macromolecular structure determination by NMR|
|Wed 2019-11-20||Molecular mechanics; Monte Carlo algorithms; comparative modelling; side chain modelling|
|Mon 2019-11-25||Fold recognition; de novo protein modelling; lattice models|
|Wed 2019-11-27||Protein design|
|Mon 2019-12-02||Surface representation; docking|
|Wed 2019-12-04||Channels; multi-resolution modelling|
|Wed 2019-12-11||Guest lecture: Per-Georg Nyholm (Biognos), Computational Drug Design|
|Mon 2019-12-16||(no class)|
Guest lecture: Alexander Schliep, HMMs in Bioinformatics from Genomics to Transcriptomics
Lecture slides, supplementary material and assignment task descriptions will be made available through the Canvas system. The provisional plan is to use Canvas also for assignment submissions.
Research articles will be listed on these web pages during the course. All of them should be accessible from within the Chalmers (and probably also GU) network. If you are not on campus, you can use VPN (Virtual Private Network) (Windows, Mac, Linux). If you have difficulty accessing any of the materials, please let me know.
Later in this course you will find it useful to look at protein structures with molecular visualisation software. You might want to take a look at RasMol, UCSF Chimera, Jmol or PyMOL. RasMol and UCSF Chimera are installed on the Chalmers Linux system.
Changes made since the last occasion
There are no major changes since the last occasion.
Learning objectives and syllabus
Knowledge and understanding
- describe bioinformatics problems and computational approaches to solving them.
Skills and abilities
- implement computational solutions to problems in bioinformatics.
Judgement and approach
- summarise problems and methods described in research articles
- critically discuss different methods that address the same task
- identify situations where methods can be applied across different application areas
The course is examined by individual programming assignments, written assignments and oral presentations.
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