The course covers techniques for analysis of genomic data. The first part focus on properties of genomic sequences. Methods for determining GC-content, gene finding and sequence alignment are discussed. In this context Hidden Markov methods play a vital role. The second part covers various aspects of genetic variation; genetic variation within and between species. Evolution and reconstruction of evolutionary mechanisms are studied. Jukes-Cantor and Kimura 2-parameter models, Ka/Ks ratio and phylogenetic trees are some tools that are discussed. In the final part analysis of microarray data, both cDNA and short-oligos arrays, are studied. The course uses real biological examples to illustrate the discussed techniques. Data are analyzed using different software (e.g. BLAST and bioconductor). After the course the students should be familiar with the software.
In a degree, this course may not be included together with another course with a similar content. If unsure, students should ask the Director of Studies in Mathematics and Mathematical Statistics. The course can also be included in the subject area of computational science and engineering.
