Department of Natural Sciences

Bioinformatics and Data Analytics

Lecture

Compulsory Course

in BSc Applied Biology, 4^th Semester
6h/week (3L/3E/0P)
Credits: 7 ECTS

Learning outcomes:

At the end of the lecture the students are able:

to name and explain different bioinformatic methods for comparative sequence analysis
use the Python programming language to find, analyse and visualise scientific data and to automate iterative tasks
use biological databases to find, compare and analyse primary sequences with bioinformatic programs and to interpret the results
assess and apply the possibilities and limitations of protein structure prediction and modelling approaches
describe basic approaches to computer-aided drug discovery and evaluate resulting results

learning the concepts and algorithms of bioinformatics methods in the lecture and using them in practical, application-oriented exercises
to find DNA sequences in databases, compare them with unknown sequences, and construct phylogenetic trees, as well as perform protein structure and function predictions
learning the basics of programming and perform script-based, exemplary bioinformatics tasks to organise, analyse and visualise data
to apply the learned, theoretical basics directly in practical exercises in smaller groups on the computer and to reflect and discuss the results and approaches

in order to

build core competencies in the context of the digitisation of the life sciences and be able to use them to analyse different amounts of data from various sources, such as genomics, transcriptomics, proteomics and metabolomics
to gain practical knowledge of programming for biological questions
to achieve a basic understanding of the advantages and disadvantages of bioinformatics methods and the connection between gene sequences, protein structure and function

Exercises

learning the concepts and algorithms of bioinformatics methods in the lecture and using them in practical, application-oriented exercises
to find DNA sequences in databases, compare them with unknown sequences, and construct phylogenetic trees, as well as perform protein structure and function predictions
learning the basics of programming and perform script-based, exemplary bioinformatics tasks to organise, analyse and visualise data
to apply the learned, theoretical basics directly in practical exercises in smaller groups on the computer and to reflect and discuss the results and approaches

Requirements

Prerequisites acoording to examination regulations: none
Recommended prerequisites: successful participation in the modules Computing Science, General Chemistry, Physics/Statistics and Instrumental Analysis

Passing of module – graded
Successful participation in the exercise sessions. Graded written exam.

Literature

S. Choudhuri, Bioinformatics for Beginners: Genes, Genomes,
Molecular Evolution, Databases and Analytical Tools, Academic Press,
2014
A.M. Lesk, Introduction to Bioinformatics, Oxford University Press,
2019
A.D. Baxevanis, B.F.F. Ouellette, Bioinformatics: A Practical Guide to
the Analysis of Genes and Proteins, Wiley, 2004
P.M. Selzer, R. Marhöfer, A. Rohwer, Applied Bioinformatics,
Springer, 2008
R. Merkl, S. Waack, Bioinformatik interaktiv: Grundlagen,
Algorithmen, Anwendungen, Wiley-VCH, 2009
M.J. Zvelebil, J.O. Baum, Understanding Bioinformatics, Garland
Science, 2008
R. Durbin, S.R. Eddy, A. Krogh, G. Mitchison, Biological Sequence
Analysis, Cambridge University Press, 1998