MM552: Computational Biology (10 ECTS)

STADS: 13015501

Level
Bachelor course

Teaching period
The course is offered in the autumn semester.

Teacher responsible
Email: roettger@imada.sdu.dk

Timetable
Show entire timetable
Show personal time table for this course.

Comment:
Ubegrænset deltagerantal. Samlæses med DM847

Prerequisites:
None.

Academic preconditions:
Students taking the course are expected to:

• Have basic knowledge in probability theory
• Have basic knowledge in algorithmics
• Have proficiency in programming

Course introduction
The purpose of this course is to give an introduction to bioinformatics research. In each class, we will start with a concrete biological and/or medical question, transform it into a computational problem formulation, design a mathematical model, solve it, and finally derive and evaluate real-world answers from within the model. The course aims at providing the basic insights in modern bioinformatics research.

The course provides an academic basis for solving bioinformatics problems by modelling and implementing computer programs.

In relation to the learning outcomes of the degree the course has explicit focus on:

• giving the competence to plan and execute fundamental bioinformatics tasks
• knowledge of common supervised and unsupervised data mining methods
• application of common network enrichment and next-generation sequencing data analysis methods
• developing skills in the development of new OMICS data mining platforms and software

Expected learning outcome
The learning objectives of the course are that the student demonstrates the ability to:

• Explain and understand the central dogma of molecular biology, central aspects of gene regulation, the basic principle of epigenetic DNA modifications, and specialties w.r.t. bacteria & phage genetics
• Model ontologies for biomedical data dependencies
• Design of systems biology databases
• Explain and implement DNA & amino acid sequence analysis methods (HMMs, scoring matrices, and efficient statistics with them on data structures like suffix arrays)
• Explain and implement statistical learning methods on biological networks (network enrichment)
• Explain the specialties of bacterial genetics (the operon prediction trick).
• Explain and implement methods for suffix trees, suffix arrays, and the Burrows-Wheeler transformation
• Explain de novo sequence pattern screening with EM algorithm and entropy models.
• Explain and implement basic methods for supervised and unsupervised data mining, as well as their application to biomedical OMICS data sets

Subject overview
The following main topics are contained in the course:

• Central dogma of molecular genetics, epigenetics, and bacterial and phage genetics
• Design of online databases for molecular biology content (ontologies, and example databases: NCBI, CoryneRegNet, ONDEX)
• DNA and amino acid sequence pattern models (HMMS, scoring matrices, mixed models, efficient statistics with them on big data sets)
• Specialities in bacterial genetics (sequence models and functional models for operons prediction)
• De novo identification of transcription factor binding motifs (recursive expectation maximization, entropy-based models)
• Analysis of next-generation DNA sequencing data sets (memory-aware short sequence read mapping data with Burrows Wheeler transformation and suffix arrays, bi-modal peak calling)
• Visualization of biological networks (graph layouting: small but highly variable graphs vs. huge but rather static graphs)
• Systems biology and statistics on networks (network enrichment with CUSP, jActiveModules and KeyPathwayMiner)
• Basic supervised and unsupervised classification methods for OMICS data analysis

Literature

Meddeles ved kursets start.

Website
This course uses e-learn (blackboard).

Prerequisites for participating in the exam
Mandatory assignments. Pass/fail, internal marking by teacher.

Assessment and marking:
Oral exam. Danish 7-mark scale, external marking. Allowed exam aids: Blackboard/Whiteboard. Allowed IT-tools: Laptop.

Expected working hours
The teaching method is based on three phase model.
Intro phase: 41 hours
Skills training phase: 45 hours, hereof:
 - Tutorials: 41 hours
 - Excursion: 4 hours

Educational activities

Educational form
Activities during the study phase: The students will work alone or in their study groups with core concepts and exercises from the course’s syllabus.

Language
This course is taught in Danish or English, depending on the lecturer. However, if international students participate, the teaching language will always be English.

Course enrollment
See deadline of enrolment.

Tuition fees for single courses
See fees for single courses.