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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
MVE360 - Bioinformatics
 
Syllabus adopted 2011-02-20 by Head of Programme (or corresponding)
Owner: MPBIO
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Bioengineering, Information Technology, Mathematics
Department: 11 - MATHEMATICAL SCIENCES


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0111 Examination 7,5 c Grading: TH   7,5 c   03 Mar 2012 pm V

In programs

MPBIO BIOTECHNOLOGY, MSC PROGR, Year 1 (compulsory elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (elective)

Examiner:

Bitr professor  Graham Kemp


Course evaluation:

http://document.chalmers.se/doc/1971388971


  Go to Course Homepage

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

Basic courses in molecular biology and statistics (e.g. KBB032, TMS145).

Aim

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 a protein. The sequence of amino acids in turn determines the three-dimensional shape and biological function of the protein. As DNA sequencing technology has been dramatically improved recently we are facing a vast amount of data in basic science as well as in areas such as clinical medicine.


Analysis of DNA, RNA and protein sequences will therefore play an essential role in coming years. The aims for such analysis include genome analysis, structural and functional prediction, elucidation of the molecular basis for human disease, understanding fundamental biological systems and reconstruction of evolutionary history.


The course illustrates how bioinformatics solutions are applied in addressing biological problems, and the theoretical principles behind these solutions are discussed.

Learning outcomes (after completion of the course the student should be able to)


  • understand the use of bioinformatics in addressing a range of biological questions
  • describe how bioinformatics methods can be used to relate sequence, structure and function
  • discuss the technologies for modern high-throughput DNA sequencing and their applications
  • use and understand some central bioinformatics data and information resources
  • know principles and algorithms of pairwise and multiple alignments, and sequence database searching
  • perform pattern matching in biomolecular sequences
  • describe how evolutionary relationships can be inferred from sequences (phylogenetics)
  • understand the most important principles in gene prediction methods
  • know basic principles of hidden Markov models and their application in sequence analysis
  • understand and implement solutions to basic bioinformatics problems

Content

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, RNA bioinformatics, gene prediction methods and principles for molecular phylogeny. The course includes modern high-throughput sequencing techniques and their applications, as well as molecular biology databases and different systems to query such databases. The course considers theoretical principles as well as how existing programs are being used by bioinformaticians.

Literature

See separate list.

Examination

There will be 5-8 obligatory computer practicals. The student is examined by these computer exercises and a written exam.



Page manager Published: Thu 04 Feb 2021.