MM552: Computational Biology
Study Board of Science
Teaching language: English
EKA: N300011112, N300011102
Assessment: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Bachelor
STADS ID (UVA): N300011101
ECTS value: 10
Date of Approval: 15-05-2023
Duration: 1 semester
Version: Archive
Comment
Entry requirements
The course cannot be chosen if you have passed, registered, or have followed DM847, or if DM847 is a constituent part of your Curriculum.
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
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
N300011112
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
English
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 |
---|---|---|
Examination part | Prerequisites for participating in the exam a) | N300011101, MM552: Computational Biology |
Tests
Oral exam
EKA
N300011102
Assessment
Second examiner: External
Grading
7-point grading scale
Identification
Student Identification Card
Language
English
Examination aids
Allowed exam aids: Blackboard/Whiteboard. Allowed IT-tools: Laptop.
ECTS value
10
Indicative number of lessons
Teaching Method
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.
Teacher responsible
Name | Department | |
---|---|---|
Ricardo Jose Gabrielli Barreto Campello | campello@imada.sdu.dk | Data Science |
Richard Röttger | roettger@imada.sdu.dk | Data Science |
Timetable
Administrative Unit
Team at Educational Law & Registration
Offered in
Recommended course of study
Transition rules
Transitional arrangements describe how a course replaces another course when changes are made to the course of study.
If a transitional arrangement has been made for a course, it will be stated in the list.
See transitional arrangements for all courses at the Faculty of Science.