DM847: Introduction to Bioinformatics
Study Board of Science
Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N340004112, N340004102
Censorship: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master
STADS ID (UVA): N340004101
ECTS value: 10
Date of Approval: 25-04-2019
Duration: 1 semester
Version: Approved - active
Comment
Entry requirements
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 knowledge in computer science models and methods designed for application in biology and medicine.
The course provides an academic basis for solving bioinformatics problems by modelling and implementing computer programs. The course also provides a scientific basis for analyzing the advantages and disadvantages of different computational methods in bioinformatics, develop new variants of the methods if required by the specific problem, and communicate research-based knowledge and discuss professional and scientific problems with both, specialists and non- specialists.
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
- planning and carrying out scientific projects at a high professional level including skills to design work packages in different situations that are complex, unpredictable and require novel solutions
- developing competences to start and implement disciplinary and interdisciplinary cooperations
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
Content
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
Examination regulations
Prerequisites for participating in the exam a)
Timing
Autumn
Tests
Mandatory assignments
EKA
N340004112
Censorship
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
0
Additional information
The prerequisite examination is a prerequisite for participation in exam element a)
Exam element a)
Timing
January
Prerequisites
| Type | Prerequisite name | Prerequisite course |
|---|---|---|
| Prerequisite not found |
Tests
Oral exam
EKA
N340004102
Censorship
Second examiner: External
Grading
7-point grading scale
Identification
Student Identification Card
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
10
Indicative number of lessons
Teaching Method
At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.
These teaching activities result in an estimated indicative distribution of the work effort of an average student in the following way:
- Intro phase (lecture, holding hours) - Number of hours: 41
- Training phase: Number of hours: 45 of these; tutorial: 41 hours and excursion: 4 hours
Activities during the study phase:
- Solve assignments
- Read the assigned literature
- Practice to apply the acquired knowledge
Teacher responsible
| Name | Department | |
|---|---|---|
| Konrad Krawczyk | konradk@imada.sdu.dk | Institut for Biokemi og Molekylær Biologi |
| Richard Röttger | roettger@imada.sdu.dk | Institut for Matematik og Datalogi, Datalogi, Datavidenskab & Statistik |