BIN315 Selected Topics in Functional Genomics
There may be changes to the course due to to corona restrictions. See Canvas and StudentWeb for info.
Showing course contents for the educational year 2021 - 2022 .
Course responsible: Torgeir Rhodén Hvidsten
Teachers: Lars Gustav Snipen
ECTS credits: 10
Faculty: Faculty of Chemistry, Biotechnology and Food Science
Teaching language: EN
Limits of class size:
Teaching exam periods:
This course starts in Fall parallel. This course has teaching/evaluation in Fall parallel.
Course frequency: Even years
First time: 2014H
Students who have the course compulsory as part of a study program at KBM will be given priority
The course will cover bioinformatics methods that are crucial components in all interdisciplinary projects that seek to describe and understand complex molecular biology systems. We will focus on analyzing data from functional genomics including transcriptomics, proteomics, metabolomics and epigenomics. The methods covered in this course are relevant for several of the UN sustainability goals where modern molecular biology is part of the solution, including goals pertaining to food- and bioenergy-production.
There will be weekly lectures/group discussions and supervised computer exercises. The lectures will primarily introduce the students to the theory behind the bioinformatics methods while the labs will show the students how the methods can be used in practice.
BIN315 complements the course BIN310, which covers bioinformatics analysis of biological sequences.
KNOWLEDGE: On completion of this course, the students will have general knowledge of the different data types generated within functional genomics ("omics"-data: transcriptomics, proteomics, metabolomics and epigenomics) and will be able to explain the theory behind the most common bioinformatics methods for analyzing such data. These methods include finding differentially expressed genes and gene sets, machine learning, clustering and network analysis, and methods for integrating "omics" data and biological knowledge in e.g. ontologies.
SKILLS: On completion of this course, the students will be able to analyze "omics" data using different methods and will also be able to understand and interpret the results produced by these methods. Given a data set and a biological question, the students should be able to asses which methods and tools to use in order to answer the question.
GENERAL COMPETENCE: The students will be able to perform reproducible analysis of data generated within functional genomics and be equipped to modify relevant methods when new datatypes emerge in the future.
One lecture/group discussion per week (two hours). Two supervised computer labs per week (four hours). Weekly hand-ins and a semester project.
Active use of Canvas.
Will be specified at the beginning of the course.
Introduction to bioinformatics equivalent to BIN210. Statistics equivalent to STAT100.
Programming and statistics beyond the introductory level.
Portfolio assessment including weekly assignments and a semester project. Everything must be passed. Pass/Fail.
Lectures: 20 hours
Computer labs: 56 hours
Individual study: 174 hours
Type of course:
1 lecture per week (two hours).
2 supervised computer lab per week (four hours).
Students must use their own laptop for computer-labs and the project.
An external examiner approves the evaluation arrangements for the course.
Examination details: Portfolio: Passed / Failed