I282 - Bioinformatics

Objectives

I282 is a graduate course primarily intended for students studying informatics. The aim is to give the students insight to problems in molecular biology, and how informatics can be utilised to solve some of these problems. No knowledge in biology is required, and a brief introduction to molecular biology will be given at the beginning of the course. We however assume preknowledge of algorithms corresponding to an advanced course, such as I234.
The different problems will be explained from the point of view of both biology and informatics, and an algorithmic approach will be used to explain the methods and techniques. In addition we will emphasize the theoretical and the biological foundation for the different methods. Therefore, statistics and biological significance will be stressed. Practical exersices using the different methods will be given.
There will be two lectures per week in thirteen weeks, one lecture is of 2 x 45 minutes. In addition there will be practical and theoretical exercises.

The contents (1997)

The contents of the course is listed below. The numbers in parentes are the planned number of lectures for each subject.

I. Introduction to molecular biology (4 lectures)

Cell, Molecule, Gene, Chromosom, DNA, RNA, Protein, Connection DNA-RNA-Protein, Protein structures, Protein functions, Bio-chemical properties of amino acids, Motif, Domain, Protein Families, Evolution, Similarity, Homology.

II. General search methods (1 lecture)

Means-Ends Analysis, Problem Reduction, Goal Tree, DepthFSearch, BreadthFSearch, BestFSearch, Optimal Search, Branch and Bound, Dynamic Programming Principle, (Minimax-procedure, Alpha-Beta pruning).

III. Statistical preliminaries (1 lecture)

Basic theory in probability and statistics, Sampling and sampling distributions, Estimation, Hypotheses testing.

IV. Scoring systems and comparison of two sequences (3 lectures)

PAM-Matrices (Dayhoff), BLOSUM (Henikoff and Henikoff), Scoring systems based on amino acid classifications.

V. Global multiple alignment (2 lectures)

Cost (measure) of multiple alignment, Dynamic programming, Progressive alignment (CLUSTAL), Use of local multiple alignment.

VI. Fragment assembly of DNA (2 lectures)

Only relative small parts of DNA may be sequenced at the time. Thus methods for assembling lot of DNA fragments in the right order into the original sequence is required. (Small scale).

VII. Physical mapping of DNA (2 lectures)

Methods for finding relative posistions of DNA sequences on a chromosome. (Large scale).

VIII. Construction of phylogenetic trees (2 lectures)

Construction by the use of character states, construction by the use of distance matrices.

IX. Search in biological databases (5 lectures)

Statistical significance, Dot matrix methods, Dynamic programming (MPSearch), FASTA, BLAST, Search with profiles.

X. Pattern discovery in set of sequences (2 lectures)

The PROSITE-database, sequence driven methods, pattern driven methods.

XI. Protein secondary structure prediction (1 lecture)

Introduction to the RNA secondary structure prediction and protein folding problems.

Main literature

I. Introduction to molecular biology

II. General search methods

III. Statistical preliminaries

IV. -XI. Rest of the themes

Links to course pages


Bioinformatics home page