Statistics in Genetics

In the course, methods for analysing different types of genomic data are studied. The first part covers methods for annotating genomic sequences in order to describe the properties and structures of a genome. Here, methods for finding genes and deciding the equality of sequences, are treated. Multinomia models, Markov models, hidden Markov models and Monte Carlo simulation constitute the base of the described methods. The second part covers methods for studying genetic variation within and between species, evolution and reconstruction of evolutionary mechanisms. The third part of the course covers methods for treating the analysis of high-dimensional genomic expression data (e.g. microarray data), including basic normalization, identification of affected variables and clustering of variables. The course is built around a couple of complex biological problems, where the students identify and formulate specific hypotheses, choose suitable analysis methods, test hypotheses using various software (e.g. BLAST och Bioconductor), and interpret the results. An important part of the course is to make the students well acquainted with the analysis process for complex biological problems.