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.

The master's program is continuing education for health professionals.

The Master of Personal Medicine has been developed in close collaboration between the four faculties of health sciences at University of Copenhagen, Aarhus University, Aalborg University and the University of Southern Denmark as well as the Technical University of Denmark. In this way, we ensure that you are taught by national experts from internationally recognized research environments in Denmark.

Read more about the programme on the website: www.personligmedicin.ku.dk

 
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

The course consists of:
-Lectures and teamwork at DTU campus.
-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.

The readings will be available to the students on the online course platform.

Read more about application requirements on the programme homepage. To find more information, please go to 'Sign Up' below.

Continuous feedback during the course of the semester
ECTS
5 ECTS
Type of assessment
Written assignment
Oral examination
Type of assessment details
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
More than one internal examiner
Exam period

See information about exam time in the  exam plan. The exam plan is published on this website:   https://sund.ku.dk/uddannelse/studieinformation/eksamensplaner/

Re-exam

A re-examination will be possible if the student fails the first examination.

A new assignment/examination will be provided and in the same format as in the initial examination.

See information about re-exam time in the exam plan. The exam plan is published on this website:   https://sund.ku.dk/uddannelse/studieinformation/eksamensplaner/

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

Kursusinformation

Language
English
Course number
SPMM21006U
ECTS
5 ECTS
Programme level
Part Time Master
Duration

1 semester

Placement
Spring
Schedulegroup
- 4 days on campus
- 2 days online teaching
- individual preparation
- group work
Capacity
30
Studyboard
Study Board for the Professionel Master´s Degree Programmes at The Faculty og Health and Medical Science
Contracting department
  • Department of Clinical Medicine
Contracting faculty
  • Faculty of Health and Medical Sciences
Course Coordinator
  • Carolina Mercedes Barra Quaglia   (7-656374716e67764266767730666d)
Teacher

Course director:
Carolina Barra Quaglia
Associate Professor, Group leader of Protein Immunoinformatics, Department of Health Technology, Technical University of Denmark.

Saved on the 03-04-2024

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students