MSc in Computer Science - Specialization in Bioinformatics

Algorithms and Programming - Evolutionary Thinking

Course content

The course will introduce the students to a wide range of methods for reconstructing phylogenetic trees and using these trees to infer evolutionary patterns including natural selection. They will also learn basic concepts in population genetics including both prospective and retrospective approaches and how these can be inferred to understand the evolutionary processes in population including population demography, recombination and selection. These two fields are then integrated into models that consider both intra and inter-specific variation. Application areas will include inferring human evolution, primate evolutionary history, virus evolution, the complete tree of life, and inferring selection for new functions. This will also be through reading and discussion of primary scientific publications and students will get familiar with the main programs of phylogenetic analysis and with exercises in population genetics analysis using R.

Description of qualifications

Objectives of the course:

The participants will after the course have detailed knowledge of the theoretical basis for population genetics and phylogenetic methods as two aspects of the evolutionary process, and will get hands-on experience with solving population genetics problems, constructing phylogenetic trees and inferring evolutionary processes from phylogenetic trees and from species variation data.

Algorithms in Bioinformatics

Course content

The course covers central algorithmic problems and computational techniques for the analysis of biological sequences, phylogenetic trees, and molecular structures. The class introduces the underlying biological questions and models, but concentrates on algorithmic problems and computational techniques about the comparison of two or more biological sequences; reconstruction and comparison of phylogenetic trees; RNA secondary structure prediction with and without pseudoknots; and protein structure prediction in simple models.

Description of qualifications

Objectives of the course

The participants will after the course have detailed knowledge of algorithms for the comparison and analysis of biological sequences (DNA, RNA and protein), construction and comparison of evolutionary trees (phylogenetics), prediction and analysis of molecular structures (RNA and proteins), and practical experience with implementation of these algorithms. The working method of the course will also train the participants to plan and complete projects and to communicate professional issues.

Data Science in Bioinformatics

Course content

This course focuses on understanding core concepts in basic probability theory and inferential statistics using real-life questions and examples from the areas of biology, molecular biology, and genomics. The course will train students in using R to perform data summarisation, visualisation and statistical analysis of cases arising from various areas of biology and genomics.

Description of qualifications

Objectives of the course:

The participants will after the course have detailed knowledge and skills for sieving through and understanding bioinformatics and genomics data.

The emphasis is on using probability and statistical concepts underlying the analysis of datasets but also on data handling and efficient analysis.

Statistics and Data - Data Science in Bioinformatics

Course content

This course focuses on understanding core concepts in basic probability theory and inferential statistics using real-life questions and examples from the areas of biology, molecular biology, and genomics. The course will train students in using R to perform data summarisation, visualisation and statistical analysis of cases arising from various areas of biology and genomics.

Description of qualifications

Objectives of the course:

The participants will after the course have detailed knowledge and skills for sieving through and understanding bioinformatics and genomics data.

The emphasis is on using probability and statistical concepts underlying the analysis of datasets but also on data handling and efficient analysis.

Statistical and Machine Learning in Bioinformatics

Course content

This course covers a broad set of methods with a focus on regression and classification methods. This will include classic regression methods such as linear, logistic and polynomial regression as well as model selection and regularization. We will use computer-intensive methods such as cross-validation, and bootstrap, as well as unsupervised learning methods. Throughout the course, we will discuss and analyze datasets from bioinformatics studies.

Description of qualifications

Objectives of the course

The participants will after the course have the necessary theoretical and practical background to engage in modelling and understanding various types of genomic datasets with a focus on prediction and classification. The participants will develop an intuition for the basis of detailed knowledge of current methods in statistical learning that are particularly tailored to (large) genomic datasets.

Evolutionary Thinking

Course content

The course will introduce the students to a wide range of methods for reconstructing phylogenetic trees and using these trees to infer evolutionary patterns including natural selection. They will also learn basic concepts in population genetics including both prospective and retrospective approaches and how these can be inferred to understand the evolutionary processes in population including population demography, recombination and selection. These two fields are then integrated into models that consider both intra and inter-specific variation. Application areas will include inferring human evolution, primate evolutionary history, virus evolution, the complete tree of life, and inferring selection for new functions. This will also be through reading and discussion of primary scientific publications and students will get familiar with the main programs of phylogenetic analysis and with exercises in population genetics analysis using R.

Description of qualifications

Objectives of the course:

The participants will after the course have detailed knowledge of the theoretical basis for population genetics and phylogenetic methods as two aspects of the evolutionary process, and will get hands-on experience with solving population genetics problems, constructing phylogenetic trees and inferring evolutionary processes from phylogenetic trees and from species variation data.