|UMF017 - Sequence information
5,0 Credits (ECTS 7,5)
|Grading: TH - Five, Four, Three, Not passed
Department: 0345 - MEDICINSK OCH FYSIOLOGISK KEMI GU
Teaching language: English
15 Dec 2005 pm V
TITEA SOFTWARE ENGINEERING - Software development and management, Year 4 (elective)
TITEA SOFTWARE ENGINEERING - Data communication, Year 4 (elective)
TITEA SOFTWARE ENGINEERING - Embedded systems, Year 4 (elective)
TITEA SOFTWARE ENGINEERING - Interaction design, Year 4 (elective)
TITEA SOFTWARE ENGINEERING - Interactive simulations, Year 4 (elective)
TITEA SOFTWARE ENGINEERING - Bioinformatics, Year 4 (compulsory)
TKBIA BIOENGINEERING, Year 4 (elective)
BIMAS MSc PROGRAMME IN BIOINFORMATICS, Year 1 (compulsory)
Professor Tore Samuelsson
Eligibility:For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.
Course specific prerequisites
The course Introduction to Bioinformatics, or similar.
Basics in biology, or similar.
A basic course in computer programming and in statistics.
Genetic information is stored in the DNA molecule as a linear sequence
of bases. In the course of gene expression this sequence is translated
into a sequence of amino acids in proteins. The sequence of amino acids
in proteins in turn determines the three-dimensional shape and
biological function of the protein. Analysis of DNA and protein
sequences therefore plays an essential role in bioinformatics. Such
analysis is aimed at for instance structural and functional prediction,
classification of protein molecules, elucidation of the molecular basis
for human genetic disease, or reconstruction of evolutionary history.
The course presents the most important theoretical principles, as well
as the applications and tools available in sequence analysis.
The course covers basic methods used in sequence analysis such as
pairwise and multiple alignment, searching databases for sequence
similarity, profiles, pattern matching, hidden Markov
models, transformational grammars, RNA bioinformatics
phylogenetic analysis, and gene prediction methods. The course considers
theoretical principles as well as how existing programs are being used
At the end of the course, students should:
- know principles and algorithms of pairwise and multiple alignments and
of methods that search databases for sequence similarity,
- know principles and benefits of position specific matrices
- be familiar with regular expressions and their applications
- know the basics of transformational grammars and how they are used in
- understand what problems are involved in RNA bioinformatics,
- understand the most important principles in gene prediction methods,
- be able to use existing programs for the topics listed above,
- know basic principles of hidden Markov models and their use in
biological sequence analysis (continued from Introduction to Bioinformatics).
- be able to understand and write simple code in the perl programing
language for bioinformatics (continued from Introduction to Bioinformatics).
Lectures, exercises and supervised computer laborations.
Biological Sequence Analysis, by Durbin, Eddy, Krogh and Mitchison.
Computer laborations and a written exam.