| Department of Basic Animal and Veterinary Sciences
35 % Department of Veterinary Disease Biology 15 % Department of Basic Science and Environment 25 % Department of Plant Biology and Biotechnology 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. A plan for the report work (based on the content of the course and chosen topic) is worked out early in the course and adjusted as the course progresses. Weight: Report 100% 7-point scale, internal examiner | ||||||||||||||||
| Organisation of Teaching | Lectures, exercises | ||||||||||||||||
| Block Placement | Block 2 Week Structure: A | ||||||||||||||||
| Teaching Language | English | ||||||||||||||||
| Optional Prerequisites | Bioinformatik1, Statistik, Matematik og Databehandling, Genetik ell. tilsvarende | ||||||||||||||||
| Restrictions | None | ||||||||||||||||
| Course Contents | |||||||||||||||||
| Concepts of phylogeny along with models of molecular evolution are presented and principles and methods for phylogenetic analysis will be introduced. The students will learn how to construct phylogenetic trees and to critically judge them. Machine learning is introduced in two parts. In the first neural networks are demystified and their basic principles exmined. Their applications to prediction of protein secondary structure as well as some of their many other applications are presented. In part two Hidden Markov models (HMMs) are presented and their application to a variety of applications shown (some overlapping with neural networks): gene finding, promoter identification (motif recognition), modelling of families and prediction of protein secondary structure. Furthermore support vector machine will also be touched upon. Gene finding is also introduced in two part. The first concern protein coding gene finding and is a prerequisite for downstream analyses such as function, structure, and metabolism. Several (machine learning) strategies for gene finding in anonymous DNA sequences, e.g. ab initio strategies, similarity driven strategies, and EST-based strategies. The second concern non-coding RNA (ncRNA) genes and RNA structure (also referred to as RNA informatics). The ncRNAs have turned out to be a highly abundant class of genes which play a central roles in regulation of protein coding genes and are expressed at specific developmental stages or in specific tissues. Various examples of ncRNAs are introduced along with methods for predicting RNA structure and ncRNA genes. Microarrays topics from introductory level (eg, Bioinformatics 1) are presented in more depth along with new analysis methods. This include steps in image scan analysis, quality control and normalization methods, and experimental designs such as reference designs, loop designs and designs for time-course experiments are presented. Statistical methods include finding significant genes, class prediction by discriminant analysis, class discovery by cluster algorithms and analysis of pathways. An introduction to systems biology is given. It include the analysis on how the molecular interactions in the cell are described as a whole and involve eg. protein-protein interactions and their relation to phenotypes. It also include the analysis of metabolites (metabolics) and can be used to classify transgenic organisms and to investigate the effects of external impacts, such as drug delivery, at the metabolite level. Methods for analyzing these types of data will be described. Details can be found at http://genome.ku.dk/courses/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. | |||||||||||||||||
| Learning Outcome | |||||||||||||||||
| Knowledge and understanding: To obtain basic knowledge from both a practical and theoretical viewpoint of concepts in bioinformatics that exceeds a pure introductory level. To acquire an overview of machine learning methods applied in bioinformatics. To point to which bioinformatics method which is suitable for analysis of a various types of data. Skills: To be able to decide which methods are suitable which given type of data. To reason and account for strengths and weaknesses of methods suitability for particular types of data analysis. Then to apply the methods in their respective contexts. To help (eg. team) collaborators identify the relevant methodologies to be applied for a given problem. Competences: To apply the methods on types of problems and data not presented explicitly in the course and thereby to evalutate the principles, strengths and weaknesses of higher level bioinformatics method on known and novel types of data. To apply the methods in practical work and work independently with them, eg, within a team. | |||||||||||||||||
| Course Litterature | |||||||||||||||||
| A compendium and other litterature will be available prior to course start | |||||||||||||||||
| 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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