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:

  • Be familiar with a range of tools used in microbial genomics, and the ideas behind the most commonly used algorithms behind these.
  • 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.

  • Syllabus

    Will be specified at the beginning of the course.

  • 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 assessment. Several project assignments will be given, and all must be passed in order to pass the course.

    Portfolio Karakterregel: Passed / Not Passed

  • About use of AI

    K3: We encourage the use of AI as a tool for improving your own learning, like a discussion partner, improving written language or suggesting R code. However, handed in material must be a product of your own efforts. Direct copying of AI generated material without considerable personal contribution, like editing, commenting or discussion, is not allowed. If you use AI you must declare exactly what type of AI and for what it was used.

    Descriptions of AI-category codes.

  • Examiner scheme

    • Internal Examiner.
    • An external examiner must approve the evaluation arrangements for the course.

  • 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, M-GS

  • Admission requirements

    Special requirements in Science