Home - 27634


27634 Bioinformatics 2 for It and Health

Primary for students at the study line "It and Health" at Copenhagen University

Danish title:

Bioinformatik 2 for It og Sundhed


Point( ECTS )


Course type:



E1A (Mon 8-12) and E2A (Mon 13-17)
Teaching period Mondays November 17 to December 22 and January 5 and 12.


Campus Lyngby

Scope and form:

Lectures and exercises

Duration of Course:

13 weeks + 3 weeks

Date of examination:

Special day, Exam January 19, 2015. Re-exam February 26, 2015

Type of assessment:

Exam duration:



Qualified Prerequisites:


General course objectives:

The goal of the course is to learn to use existing bioinformatics tools, understand and evaluate available methods for classification and predictions. Remote genetic databases such as Genbank, RefSeq, UniProt, etc can be accessed BioPython libraries and web services. We aim at equipping the students with the skills to make and execute such scripts to access remote databases. We also aim that the students learn to access, collect and analyse large volume of data from files and databases using simple scripts made using UNIX commands. We also aim for the students to be able to visualise their data using R. Finally, we aim to provide insights into the principles and pros and cons of relevant classification, prediction and modelling methods so that students can critically evaluate and compare performances of such tools.

The students are expected to have basic IT skills and should be comfortable with programming in Python prior to attending the course.

Learning objectives:

A student who has met the objectives of the course will be able to:
  • Put together bioinformatics tools in a pipeline using Unix shell scripts and small programs (scripts).
  • Writing scripts to collect and filter datasets in R and visualise the data graphically.
  • Python for Bioinformatics. Implement programmatic data retrieval.
  • Writing webservices to collect data from remove bioinformatic databases.
  • Understand the principles behind the commonly used classification, modeling and prediction methods.
  • Evaluate the quality and usability of bioinformatics tools using common performance measures such as sensitivity, specificity, correlation coefficients and ROC curves.
  • .
  • .


The course covers the methods of accessing remote data bases using BioPython and web services and also introduces methods for data manipulation and visualisation using Unix and R. Finally, the course examines the principles behind the most common methods used in bioinformatics: artificial neural network (artificial neural networks, ANN), Markov Models (HMM), support vector machines (SVM), etc. and introduces the use of these methods in the relevant biological systems. It also shows how bioinformatics tools implemented (installed) and evaluated in practice, and there is a strong emphasis on streamlining work processes with those using programming.


online materials, papers

Green challenge participation:

Please contact the teacher for information on whether this course gives the student the opportunity to prepare a project that may participate in DTU´s Study Conference on sustainability, climate technology, and the environment (GRØN DYST). More infor http://www.groendyst.dtu.dk/english


27 Department of Systems Biology

Home page:


Registration Sign up:

At CampusNet
Last updated: 17. november, 2014