Bioinformatics

2-year Master’s Programme at

The University of Aarhus

The Masters’ Programme adresses students with a Bachelor’s Degree in natural sciences or technical sciences. The programme focusses on development of IT-solutions in the biotechnological sector and it covers biological, computational and statistic topics.

Admission Requirements

The admission requirement is a Bachelor’s Degree. Since knowledge of mathematics at upper-secondary level is a pre-requisite for entering the programme, it primarily addresses applicants with a background in natural science s or techni-cal sciences e.g. biologists, molecular biologists, veterina-ries, statisticians, computer scientists or engineers.

The need for efficient computer based tools for analysis and handling of the increasing amounts of data in biologi-cal and medical research – e.g. in the hospital sector and the medical industry – is larger than ever. The development of these tools requires solid knowledge in biology and sta-tistics, combined with expertise in software development.

Bioinformatics covers a wide range of activities focussing on the development and application of computer based analysis of biological data – e.g. DNA sequences and pro-tein structures.

Over the last 25 years the amount of biological data has grown explosively and despite an almost correspond-ing growth in the calculation power of the computers and the capacity of the information technology in general, the analysis and handling of the fast growing amounts of data require highly effecient calculation methods and tools that utilise the available technology at the utmost.

The Master’s programme in bioinformatics at the University of Aarhus aims at giving the students a higher education in bioinformatics focussing software development and central subjects in biology and statistics.

The education typically gives acces to employment in the hospital sector, the i medical industry, in the growing bio-technology industri and the IT branch more generally.

Application www.au.dk/welcome/degree/howtoapplywww.au.dk/da/optag/overbygning/vejlkan.htm

Questions on application and admission:Studiekontoret Naturvidenskab, Ny Munkegade, Building 1520, DK-8000 Aarhus CPhone: +45 8942 1111, studiekontor.nat@au.dk

Bioinformatics Research Center - BiRCUniversity of AarhusHøegh Guldbergs Gade 10DK-8000 Aarhus CPhone +45 8942 3123www.birc.au.dk

Christian N. S. Pedersen, +45 8942 3121, cstorm@birc.au.dk Freddy B. Christiansen, +45 8942 3238, freddy@birc.au.dk Mikkel H. Schierup, +45 8942 3231, mheide@birc.au.dk

www.birc.au.dk/Studies/

Course of Study

Bioinformatics is a discipline where many different subjects meet. Thus, the education takes place in a cross-disciplinary environment including computer science, statistics, biology and molecular biology. The differences in the students’ profes-sional qualifications contribute essentially to the environment, but at the same time these are a challenge to the students as well as to the teachers.

The core areas of the education are programming, algorithms, data structures, development of large software systems, handling of large amounts of data, analysis of biological sequences and structures, molecular evolution and statistical models. These topics are covered through a 2-year fulltime study (8 quarters) with courses distributed over 6 quarters (1½ years) and a MSc thesis of 6 months. The teaching that emphasizes the importance of theory and practice equally, is a combination of lectures and training lessons in smaller teams.

Students with a biological background only

Introduction to ProgrammingIntroduction til programming concepts and methods for sys-tematic development and test of smaller programs.

Genome Analysis Methods for analysis of whole genomes, including gene finding, search for regulatory sequences, reconstruction of metabolic chains and functional classification of genes. Evolution of genomes, e.g. population variation and homol-ogy modelling at genome level. Analysis of genome and DNA chip data.

Molecular Population GeneticsMethods for investigations of evolutionary processes in populations. Visualisation of models by means of compu-ters.

Students with a computational background only

Introduction to Molecular Biology Basic concepts in molecular genetics and molecular biology.

GeneticsBasic concepts in classical genetics.

Evolution and DiversityEvolutionary science about life’s diversity.

Algorithms and Data StructuresIntroduction to basic algorithms and data structures, includ-ing searching, merging, sorting, lists, queues, decks, trees. basic theory on correctness/accuracy and efficiency of algorithms and going through basic calculation paradigms.

Algorithms in BioinformaticsIntroduction to efficient algorithms that solve specific bio-logical problems, comprising comparison of two or more biological sequences, search for genes, reconstruction of evolutionary trees and structure prediction. The exercises emphasize implementation of methods and practical exam-ination of their behaviour.

Bioinformatics Courses

Introduction to Bioinformatics Introduction to basic problems in sequence analysis, struc-ture analysis and expression analysis. The training empha-sizes application of bioinformatics tools.

Programing in BioinformaticsIntroduction to Unix environment and scripting languages. Focus on introductory bioinformatics problems, development and test of smaller programs solving these problems.

Software Development in BioinformaticsFocusses on the problems that arise when a software solu-tion has to combine several components. Introduction to use of larger program libraries, linking of existing programs to own programs using scripting languages and databases for handling large amounts of data.

Molecular EvolutionUse of DNA sequences in studies of phylogenetic correla-tions between organisms. Models, methods and problems in the analysis of DNA sequences from different orga-nisms.

Possible courses for students with a biological background

Possible courses for students with a computational background

Stochastic Models in BioinformaticsIntroduction to Markov chains and hidden Markov models often used for modelling biological systems.

8MSc Thesis

7

6 Algorithms inBioinformatics Optional

Optional5 Algorithms in

BioinformaticsStochastic Models in Bioinformatics

4 Algorithms and Data Structures

Software Developmentin Bioinformatics

Molecular Population Genetics

3 Algorithms and Data Structures Optional Molecular Evolution

2 Programming in Bioinformatics

OptionalGenome Analysis

1 Introduction to Programming

Introduction to Bioinformatics

8MSc Thesis

7

6

OptionalOptional

Optional5 Stochastic Models in

Bioinformatics4 Evolution and

DiversitySoftware Developmentin Bioinformatics

Molecular Population Genetics

3Genetics Optional Molecular Evolution

2 Algorithms in Bioinformatics

Programming in Bioinformatics Genome Analysis

1 Algorithms in Bioinformatics

Introduction to Molecular Biology

Introduction to Bioinformatics