Bioinformatics

Course content

Learn how to extract relevant data from most essential databases and how to use the methods for molecular sequence and functional analysis within Bioinformatics.

Continuing education for medical doctors, academics within the health care system, research environments, medicinal industries and organisations working with personalised medicine.

The objective of the course is to provide you with a knowledge of the most essential databases and methods for molecular sequence and functional analysis.

These years, computer based methods play a crucial role in molecular biology, microbiology, and personalised medicine. Huge international databases of sequence and functional contain information, which in some cases can entirely replace experimental work, and in other cases can be used to optimize the benefit of experimental resources.

Introduction to Bioinformatics is a practically oriented course with focus on using the methods rather than deriving them mathematically. Bioinformatics is presented as a biological discipline rooted in evolutionary theory. A large part of the course consists of computer-based exercises, where the computational tools are applied based on the participants’ biological prior knowledge.

Education

This course is offered as part of the Master in Personalised Medicine.
Read more about the programme on the website: www.personligmedicin.ku.dk (in Danish)

 
Learning outcome

Once you have met the objectives of the course, you will be able to:

Knowledge

  • Rationally apply bioinformatics tool to answer biological questions relevant to applied personalised medicine
  • Explain how patient stratification is done based on genomics, transcriptomics, and proteomics data in practice using basic clustering and  classification

 

Skills

  • Explain how the information in biological macromolecules, such as DNA and protein can be represented in a digital format.
  • Explain how processing of NGS data is done with bioinformatics tools
  • Search for sequence data from the publicly available databases, such as GenBank and UniProt, and relevant disease omics data such as the cancer genome atlas (TCGA)

 

Competencies

  • Use programs to perform basic clustering of patient samples, based on critical feature selection
  • Search the clinvar and COSMIC databases of disease related mutations

2 x 2 days on campus:
Lectures and teamwork at DTU campus.

2 days online teaching:
Online teaching, group work with assignments, and presentations from the students.

Project work and report writing:
The course ends with an interdisciplinary group work based on a case.

See Literature list in Absalon

This module can be taken as a single course to external participants who meet the admission requirements for Master in Personalised Medicine:
You must meet the following criteria to be admitted to this course:

- Hold a relevant master degree or equivalent
- Have a minimum 2 years of professional experience within personal medicine in a clinical, research or academic field
- Be proficient in English

Find detailed information about the current admission criteria (in Danish) at: www.personligmedicin.dk

Continuous feedback during the course of the semester
ECTS
5 ECTS
Type of assessment
Written assignment
The course ends with an interdisciplinary group work based on a case.
Aid
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Criteria for exam assessment

In order to achieve the grade 12, the student must be able to:

Knowledge

  • Rationally apply bioinformatics tool to answer biological questions relevant to applied personalised medicine
  • Explain how patient stratification is done based on genomics, transcriptomics, and proteomics data in practice using basic clustering and  classification

 

Skills

  • Explain how the information in biological macromolecules, such as DNA and protein can be represented in a digital format.
  • Explain how processing of NGS data is done with bioinformatics tools
  • Search for sequence data from the publicly available databases, such as GenBank and UniProt, and relevant disease omics data such as the cancer genome atlas (TCGA)

 

Competencies

  • Use programs to perform basic clustering of patient samples, based on critical feature selection
  • Search the clinvar and COSMIC databases of disease related mutations

Part time Master and Diploma courses

  • Category
  • Hours
  • Lectures
  • 6
  • Class Instruction
  • 10
  • Preparation
  • 80
  • E-Learning
  • 12
  • Project work
  • 20
  • Exam
  • 10
  • English
  • 138