Bioinformatics - For Studerende

breakfastcorrieΒιοτεχνολογία

22 Φεβ 2013 (πριν από 4 χρόνια και 6 μήνες)

958 εμφανίσεις

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/howtoapply
www.au.
dk/da/optag/overbygning/vejlkan.htm
Questions on application and admission:
Studiekontoret Naturvidenskab,
Ny Munkegade, Building 1520, DK-8000 Aarhus C
Phone: +45 8942 1111, studiekontor.nat@au.dk
Bioinformatics
Research Center - BiRC
University of Aarhus
Høegh Guldbergs Gade 10
DK-8000 Aarhus C
Phone +45 8942 3123
www.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 Programming
Introduction 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. A
nalysis of genome and
DNA chip data.
Molecular Population Genetics
Methods 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.
Genetics
Basic concepts in classical genetics.
Evolution and Diversity
Evolutionary science about life’s diversity.
Algorithms and Data Structures
Introduction 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 Bioinformatics
Introduction to efficient algorithms that solve specific bio
-
logical
problems, comprising comparison of two or more
biolo
gical 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 Bioinformatics
Introduction to Unix environment and scripting languages.
Focus on introductory bioinformatics problems, development
and test of smaller programs solving these problems.
Software Development in Bioinformatics
Focusses 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 Evolution
Use 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 Bioinformatics
Introduction to Markov chains and hidden Markov models often
used for modelling biological systems.
8
MSc Thesis
7
6
Algorithms in
Bioinformatics
Optional
Optional
5
Algorithms in
Bioinformatics
Stochastic Models in
Bioinformatics
4
Algorithms and Data
Structures
Software Development
in Bioinformatics
Molecular
Population Genetics
3
Algorithms and Data
Structures
Optional
Molecular Evolution
2
Programming in
Bioinformatics
Optional
Genome Analysis
1
Introduction to
Programming
Introduction to
Bioinformatics
8
MSc Thesis
7
6
Optional
Optional
Optional
5
Stochastic Models in
Bioinformatics
4
Evolution and
Diversity
Software Development
in Bioinformatics
Molecular
Population Genetics
3
Genetics
Optional
Molecular Evolution
2
Algorithms in
Bioinformatics
Programming in
Bioinformatics
Genome Analysis
1
Algorithms in
Bioinformatics
Introduction to
Molecular Biology
Introduction to
Bioinformatics