Bioinformatics for Animal Genomics

Course content

Driven by the technological progress, biology continuously undergoes a revolution that started in the late 1990-es with the sequencing of the human genome. This revolution is on a scale that constantly redefines our basic understanding of biology and reaches deep into animal science, for example with respect to diseases, nutrition and production traits. Understanding the opportunities that come with the technological development is crucial for strategy planning in animal science in general.

The course will introduce tools to analyze the high-throughput next generation sequencing (NGS) data generated from a selected range of technologies with high relevance for animal science. To this end, basic concepts of sequence alignment and genome analysis will be introduced together with data of relevance for the genome structure (e.g. single nucleotide polymorphisms and chromosomal re-arrangements) and transcriptomes (e.g. gene expression and gene transcript variation) in animals. Furthermore, the functional context of genes will be addressed through the studies of biochemical pathways and networks, in which information from the NGS data will be employed to interpret the functional aspects of the genes.


MSc Programme in Animal Science

Learning outcome

The aim of the course is to make the participants able to analyze animal related NGS data, to assess the quality of the analysis, and conceptually to account for methodological principles underlying the bioinformatics methods presented in the course.


Overview of
  - key data types and sequence based data sets for animal genomics and from where to obtain them
  - bioinformatics tools applicable for analysis of a broad range of data from animal genomes and transcriptomes
  - bioinformatics tools for obtaining in silico driven predictions on animal genomes


  - Apply the relevant bioinformatics tools for the analysis of animal genome and transcriptome data
  - Assess the quality of a bioinformatic tools
  - Assses the quality of the data analysis


  - Acquire relevant data for a given problem
  - Assess the relevance of a given bioinformatics method for the data in question
  - Interpret the implications of a data analysis in broader context than the specific data analyzed


Lectures, computer (and occasionally theoretical) exercises. Cases based mini projects in team work resulting in group-wise presentations. Students will divided in teams which a in number of instances should meet and discuss the exercises and reading material before it takes place in class. In addition, there will be a number of guest lecturers.

Evaluation model: Survey-based model

The course material will mainly be research articles and also involve chapters from textbooks

Students should not expect to be able to follow the course unless a they have a bachelor level:

* Genetics, gene and genome structure
* Statistics
* Mathematics
* R

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Written assignment, 7 days
Written eksamination 7. days. 7 days before the exam paper is handed out.
A project report must be submitted.

The exam will consist of a take home task, including analysis of provided data.
The home assignment will take place in block week 8.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Intern censur. Flere bedømmere.
Criteria for exam assessment

To obtain the grade 12 the student must be able to:

- The student must be able to account for the choice of methods and program for the analysis of a particular biological problem
- The student should be able to account for the key assumptions in the methods and programs used to simplify them and what the implications is of this

- Explain the methodological and algorithmic principles used for construction of the tools
- Account for the choice of method(s) and strategy for data analysis and supporting bioinformatic predictions. In addition, the student should also account for the problems associated with this choice.

- Carry out analysis on the type of data presented in the course
- [Possibly how an R script is carried out]
- Place the results of the analysis in a broader context


  • Category
  • Hours
  • Lectures
  • 23
  • Practical exercises
  • 23
  • Project work
  • 9
  • Guidance
  • 7
  • Preparation
  • 90
  • Exam
  • 54
  • English
  • 206