Course code BIN315

BIN315 Selected Topics in Functional Genomics

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Showing course contents for the educational year 2019 - 2020 .

Course responsible: Torgeir Rhodén Hvidsten
Teachers: Lars Gustav Snipen
ECTS credits: 10
Faculty: Faculty of Chemistry, Biotechnology and Food Science
Teaching language: EN, NO
(NO=norsk, EN=Engelsk)
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: Study year 2014-2015
Preferential right:



Course contents:

The course will cover bioinformatics analysis of "omics" data; transcriptomics, proteomics and metabolomics. It complements the course BIN310 that will cover bioinformatics analysis of biological sequences.

There will be weekly lectures and supervised exercises in the computer lab. 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.

Learning outcome:

KNOWLEDGE: On completion of this course, the students will have general knowledge of the different "omics"-technologies (transcriptomics, proteomics and metabolomics) and will be able to explain the  theory behind the most common bioinformatics methods for "omics" analysis. 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.

Learning activities:
One lecture per week (two hours). Two supervised computer lab per week (four hours). One project assignment with written report and/or oral presentation.
Teaching support:
Active use of Canvas.
Will be specified at the beginning of the course.
Introduction to bioinformatics equivalent to BIN210. Statistics equivalent to STAT100.
Recommended prerequisites:
Programming and statistics beyond the introductory level.
Mandatory activity:
There will be one project assignment where the students will present their findings in a written report and/or an oral presentation.
Written exam, 3.5 hrs.
Nominal workload:

Lectures: 20 hours

Computer labs: 56 hours

Individual study: 224 hours

Type of course:

1 lecture per week (two hours).

2 supervised computer lab per week (four hours). 

The exam will be approved by an external examiner. 25 selected exam paper papers will be evaluated by the external examiner.
Allowed examination aids: A1 No calculator, no other aids
Examination details: Continuous exam: Passed / Failed