Department of Bacic Animal and Veterinary Sciences
35 % Department of Veterinary Pathobiology 15 % Department of Natural Sciences 25 % Department of Plant Biology 25 % | |||||||||||||||||
Earliest Possible Year | MSc. 1 year to MSc. 2 year | ||||||||||||||||
Duration | One block | ||||||||||||||||
Credits | 7.5 (ECTS) | ||||||||||||||||
Course Level | MSc | ||||||||||||||||
Examination | Final Examination written examination All aids allowed Description of Examination: Evaluation of a report based on the students own data or data provided from one of the teachers. A report is turned in by the end of the course. Weight: Report 100% 13-point scale, internal examiner | ||||||||||||||||
Organisation of Teaching | Lectures, exercises | ||||||||||||||||
Block Placement | Block 2 Week Structure: B | ||||||||||||||||
Teaching Language | English | ||||||||||||||||
Optional Prerequisites | Bioinformatik1, Statistik, Matematik og Databehandling, Genetik ell. tilsvarende | ||||||||||||||||
Areas of Competence the Course Will Address | |||||||||||||||||
Scientific: The student should aquire knowledge not only on how to apply methods available on the internet, but also on how to integrate them in biological work. Technology and production: Furthermore the student should also be able to on their own to obtain knowledge of new methods and understand how to apply them. Ethics and values: To relate and communicate research results in a critical way. | |||||||||||||||||
Course Objectives | |||||||||||||||||
The goal of the course is obtain basic bioinformatics knowledge at a level which include machine learning methods and methods at a similar level. The course will together with bioinformatics1 complete introduction of basic concepts in bioinformatics. | |||||||||||||||||
Course Contents | |||||||||||||||||
Phylogeny and sequence evolution: Phylogenetic methods are based on multiple sequence alignments and can be used to group molecular sequences and to reconstruct evolutionary history. We introduce different models of molecular evolution and present principles and methods for phylogenetic analysis. The students will learn how to construct phylogenetic trees and to critically judge them, using knowledge about the limitations of phylogenetic analysis from a biological and from a statistical point of view. Neural networks (Machine learning part I): We demystify neural networks, examine their basic principles, and describe their application to prediction of protein secondary structure as well as some of their many other applications. The aim is that the student understands the basic principles and can determine when a meaningful and reliable prediction has been provided. Hidden Markov models (Machine learning part II): Hidden Markov models (HMMs) are also widely used in bioinformatics. We show their application to gene finding, promoter identification (motif recognition), modelling of families and prediction of protein secondary structure. The student should understand the principles and assumptions of hidden Markov models to critically judge the appropriateness of this class of models in a given situation and to evaluate their use in algorithms and software. Gene finding: A fundamental task in genome sequencing projects is gene finding, because knowledge of the genes is a prerequisite for downstream analyses such as function, structure, and metabolism. We present several strategies for gene finding in anonymous DNA sequences, e.g. ab initio strategies, similarity driven strategies, and EST-based strategies. Likewise, we present methods (e.g. neural networks, HMMs, statistics-based) that implement the strategies, and discuss their their strengths and weaknesses. The lectures will mainly be oriented towards the practical use of the methods, as several of them are poorly documented. Non-coding RNA genes and RNA structure (RNA informatics): Non-protein coding RNAs (ncRNAs) have turned out to be a highly abundant class of genes. They play a central role in regulation of protein coding genes and can be expressed at specific developmental stages or in specific tissues. Furthermore, ordinary mRNA can contain structural elements in their untranslated regions (UTRs) which may be involved in cis regulation of coding genes. Various examples of ncRNAs are introduced along with methods for predicting RNA structure and ncRNA genes. Microarrays (2): Microarrays play a steadily increasing role in simultaneously analyzing the expression of tens of thousands of genes in a series of biological and medical applications. We study topics from Bioinformatics 1 in more depth and introduce new analysis methods. We present the steps in image scan analysis, quality control and normalization methods, and we discuss experimental designs such as reference designs, loop designs and designs for time-course experiments. Statistical methods include finding significant genes, class prediction by discriminant analysis, class discovery by cluster algorithms and analysis of pathways. Metabolomics: Metabolomics deals with the analysis of metabolites and can be used to classify transgenic organisms and to investigate the effects of external impacts, such as drug delivery, at the metabolite level. We introduce techniques for metabolite analysis and statistical methods for calculating differences of chromatographic profiles as well as methods for analyzing the relationships between metabolite data and phenotype/genotype. Metabolic networks will also be covered. Details can be found at http://bioinf.kvl.dk/bioinformatics2 | |||||||||||||||||
Teaching And Learning Methods | |||||||||||||||||
Lectures and exercises with supervision as well as group work with exercises. Case work for example in relation to a scientific paper can also take place. | |||||||||||||||||
Course Coordinator | |||||||||||||||||
Jan Gorodkin, gorodkin@genome.ku.dk, Department of Basic Animal and Veternary Sciences/Genetics & Bioinformatics, Phone: 35333578 | |||||||||||||||||
Study Board | |||||||||||||||||
Study Committee NSN | |||||||||||||||||
Course Scope | |||||||||||||||||
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