DM847: Introduction to Bioinformatics (10 ECTS)

STADS: 15017301

Level
Master's level course

Teaching period
The course is offered when needed.

Teacher responsible
Email: jbaumbac@imada.sdu.dk

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

Comment:
Ubegrænset deltagerantal

Prerequisites:
None

Academic preconditions:
The content of DM507 Algorithms and Data Structures should be known.

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. It will be designed as prerequisite for a planned Bioinformatics II special course.

Expected learning outcome

• 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, GraphLets) 
• 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.

Subject overview
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, Graphlet degree signatures)

Literature

Meddeles ved kursets start

Website
This course uses e-learn (blackboard).

Prerequisites for participating in the exam
None

Assessment and marking:
Oral exam, Danish 7-mark scale, external examiner.

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

Educational activities

Language
This course is taught in English.

Course enrollment
See deadline of enrolment.

Tuition fees for single courses
See fees for single courses.