BMB209: Workshops in Applied Bioinformatics
The Study Board for Science
Teaching language: English
EKA: N220007112, N220007102
Assessment: Second examiner: None
Grading: Pass/Fail
Offered in: Odense
Offered in: Autumn, Spring
Level: PhD
STADS ID (UVA): N220007101
ECTS value: 2.5
Date of Approval: 11-07-2018
Duration: 1 semester
Version: Archive
Comment
Entry requirements
Academic preconditions
Students taking the course are expected to:
- Basic knowledge in statistics and data analysis
- Understand the basic principles of molecular biology
Course introduction
Despite the large number of available modern software to process and simulate biological data (eg. more than 10,000 reported in bio.tools), lack of practice and experience limits actually used methods in computational biology to a much smaller selection of popular and easy-to-operate tools.
This workshop series aims to improve familiarity with a selection of up-to-date and powerful computational methods by hands-on experience supervised by experts in the respective fields including omics data analysis, statistics, image analysis and simulation of biological systems.
The students will learn how to create and apply software workflows for the analysis of noisy data and gain knowledge to visually and conceptually communicate underlying complex biological processes.
The workshops aim to provide insights into modern bioinformatics to students and interested researchers at the Department of Biochemistry and Molecular Biology as well as other interested parties.
The taught material will not only improve awareness of unknown or novel software, algorithms and methods but also lower the often steep learning curves for actually applying the methods.
In the workshops, renowned often international experts will furthermore promote and encourage best-practice in computational biology.
Expected learning outcome
The learning objective of the course is that the student demonstrates the ability to:
- Independently reproduce given bioinformatics examples
- Work with complex data
- Apply sophisticated software tools
- Describe and distinguish between analytical methods in computational biology
- Know how to objectively discuss applied data analysis methods presented e.g. in publications
- Use online bioinformatics resources
Content
The following main topics are contained in the course:
- Introduction into software resources in bioinformatics
- Popular data analysis workflows in bioinformatics
- Application of selected software tools
- Presentation of current challenges in bioinformatics by invited experts
Literature
Examination regulations
Prerequisites for participating in the exam a)
Timing
During the course
Tests
Attendance of workshops
EKA
N220007112
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
Autumn / Spring
Prerequisites
| Type | Prerequisite name | Prerequisite course |
|---|---|---|
| Examination part | Prerequisites for participating in the exam a) | N220007101, BMB209: Workshops in Applied Bioinformatics |
Tests
Individual report
EKA
N220007102
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
2.5
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
The teaching method is based on three phase model.
Intro phase: 8 hours
Skills training phase: 6 hours, hereof:
- Tutorials: 6 hours
Educational activities
In the individual report, the student reproduces one of the given bioinformatics workflows. Details on the individual assignment will be discussed during the workshops. The report should consist of at least 5 pages with a maximum of 10 pages excluding references. It should contain the following sections: a) Introduction and description of assignment; b) Overview of the student's procedure to solve the task; and c) evaluation of results and conclusion.