MM552: Computational Biology
Study Board of Science
Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
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: 25-04-2019
Duration: 1 semester
Version: Archive
Comment
13015501(former UVA) is identical with this course description.
This course is co-read with DM847: Introduction to Bioinformatics (10 ECTS)
This course is co-read with DM847: Introduction to Bioinformatics (10 ECTS)
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.
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
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 |
|---|---|---|
| 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
Normally, the same as teaching language
Examination aids
Allowed exam aids: Blackboard/Whiteboard. Allowed IT-tools: Laptop.
ECTS value
10
Additional information
The examination form for re-examination may be different from the exam form at the regular exam.
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.