SASK21001U Bioinformatics analysis of gene expression data

Volume 2021/2022
Education

MSc Programme in Animal Science

Content

Driven by the technological progress, biology continuously undergoes a revolution that started in the late 1990s 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 human and animal science, for example with respect to diseases and nutrition. Understanding the opportunities that come with the technological development is crucial for strategy planning human and 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 human and animal science. To this end, basic concepts of sequence alignment and genome analysis will be introduced with special focus on transcriptomics (e.g. gene expression and gene transcript variation). 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.

Major software tools introduced and applied during the course are the web-based platform Galaxy for running data intensive bioinformatic analysis pipelines, the statistical software R for statistical computing, and Cytoscape for visualization and interpretation of biological networks.

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.

Knowledge:

Overview of
  - key data types and sequence based data sets for transcriptomics
  - bioinformatics tools applicable for analysis of a broad range of data from animal transcriptomes
  - bioinformatics tools for obtaining in silico driven predictions on animal transcriptomes

Skills:
  - Analyze RNA sequencing data by applying relevant bioinformatic tools
  - Assses the quality of sequence data
  - Assess the quality of bioinformatic tools
  - Apply the statistical software R for data manipulation and analysis

Competences:
  - 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

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 they have a bachelor level in one of the following subjects:

* Genetics, gene and genome structure
* Statistics
* Bioinformatics

Previous experience with the software R are recommended but not required.
The course combines theoretical lectures, tutorials, and computer exercises. Students will be divided in study groups which should meet and discuss the exercises and reading material. Cases based mini projects will be performed in these study groups resulting in group-wise presentations. In addition, there will be a number of guest lecturers.
  • Category
  • Hours
  • Lectures
  • 23
  • Preparation
  • 90
  • Practical exercises
  • 23
  • Project work
  • 9
  • Guidance
  • 7
  • Exam
  • 54
  • Total
  • 206
Written
Oral
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Written assignment, 7 days
Written examination 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.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Intern censur. Flere bedømmere.
Exam period

 

 

Criteria for exam assesment

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

Knowledge:
- 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

Skills:
- 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.

Competences:
- Carry out analysis on the type of data presented in the course
- Write R script to analyze count table in transcriptomics
- Place the results of the analysis in a broader context