27634 Bioinformatics 2 for It and Health
Primarily for students at the study line "It
and Health" at Copenhagen University
|Bioinformatik 2 for It og Sundhed|
Point( ECTS )
|E1A (Mon 8-12) and E2A (Mon 13-17)
Teaching period Mondays November 16 to December 14 and January 4
Scope and form:
|Lectures, computer-based exercises and project|
Duration of Course:
|13 weeks + 3 weeks|
|Exam January 18, 2016.|
Type of assessment:
General course objectives:
The goal of the course is to learn to use and combine existing
bioinformatics tools to build a complete pipeline to collect,
analyze, visualize and model biological and clinical data.
The students will acquire the practical knowledge to build scripts
and use libraries (BioPython) to access remote databases for
biological molecules such as Genbank, RefSeq, and UniProt. The
students will learn how to collect and analyze large volume of data
from files and databases using simple scripts made using UNIX
The students will learn how to load, visualize and compute basic
operations and statistical analysis on their data using R. Finally,
we aim to provide insights into the principles and pros and cons of
statistical modeling, classification and prediction so that
students can critically evaluate and compare performances of such
The students are expected to have basic IT skills and should be
comfortable with programming in Python and R prior to attending the
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).
- Write scripts to collect and filter datasets.
- Process data using Unix commands.
- Analyze sequences in Python.
- Process data with R.
- Visualize data in R.
- Compute basic statistical analysis with R.
- Perform basic statistical modeling with R.
- Analyze and evaluate biological and medical
The course introduces the participants to the practical usage of
bioinformatics tools for common biological data analysis tasks. The
course covers the methods of accessing remote databases using
BioPython and methods for data manipulation and visualization using
Unix and R. The course presents the usage of common statistical and
modeling methods used in bioinformatics, such as artificial neural
networks and random forests, and introduces the use of these
methods in the relevant biological systems. There is a strong
emphasis on streamlining work processes with those using
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
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Last updated: 04. maj, 2015