BIN310 Selected Topics in Microbial Genomics

Credits (ECTS):10

Course responsible:Lars Gustav Snipen

Campus / Online:Taught campus Ås

Teaching language:Engelsk, norsk

Limits of class size:60

Course frequency:Odd years

Nominal workload:70 hours with instructor present: 180 hours self study

Teaching and exam period:This course starts in autumn parallel. This course has teaching/evaluation in autumn parallel.

About this course

Modern sequencing technology, shotgun and amplicon sequencing. Assembly and annotations of genomes and metagenomes. Comparative genomics and metagenomics. Analyses of data from microbial communities. Use of High Performance Computing facilities.

Competence in Bioinformatics contributes to all UN goals for sustainable development that involves biology (health, hood, nature).

Learning outcome

KNOWLEDGE: The ideas behind commonly used algorithms for processing and analysis of sequence data. Get to know a UNIX computing cluster and simple shell scripting. Know some common statistical methods for metagenome-studies and apply these on real data using R and R-packages.

SKILLS: Be able to apply elements from the topics above to solve project assignments in the course. Making simple shell-scripts and R-scripts and run heavier analyses on a UNIX computing cluster. Present material both orally and in a written report.

GENERAL COMPETENCE: Be able to make use of, and understand, the future computational pipelines of (meta)genome studies. Practical programming competence, directed toward analysis og bio-data.

Learning activities
The basis for the course is a series of written material as markdown documents as well as a number of screencasts. All physical teaching will be at weekly hours used for discussions and solutions to exercises as well as simple lectures over certain topics of special interest. There will also be oral presentations over some project reports.
Teaching support
Active use of Canvas.
Prerequisites
Introduction to bioinformatics (BIN210) - Some knowledge of modern sequencing - Basics on sequence alignments, pairwise, multiple, database search (BLAST) - Basics on phylogeni
Introduction to statistics (STAT100) - Familiar with stochastic variables and parameters - Basic calculus of probability, conditional probabilities
Introduction to programming (STIN100) - Be familiar with datatypes numeric, character, logical, factor - Be familiar with data structures vector (array), matrix, table (tibble/data.frame), list - Be familiar with concepts like loops and functions/methods
No advanced coding will be required, but we have no time for general teaching in coding. All coding will be in R and shell (bash).
Recommended prerequisites
Programming and statistics beyond the introductory level.
Assessment method
Portfolio asessment. Several project assignments will be given, and all must be passed in order to pass the course.

Portfolio Grading: Passed / Not Passed
Examiner scheme
External examiner will approve the projects and evaluate a selection of them
Notes
Students must bring their own laptop to computer-labs.
Teaching hours
6 hours teaching activities per week. This is a mixture of lectures, hand-on exercises and solving problems in Groups.
Preferential right
M-BIAS, M-BIOTEK, M-KB
Admission requirements
Special requirements in Science