Omics for the Life Sciences

Course goals

After this course, you will be able to:

1) explain technological principles of different omics approaches;
2) perform basic computational analyses of the omics data from quality control to statistical inferences;
3) design a multifactorial experiment utilizing omics technologies;
4) perform visualization of omics data in R.

Content

Introduction
In the course Omics in Life Sciences, students learn basics of how molecular omics technologies work, how they help discover and characterize phenomena in life sciences, and how they can be used to solve environmental and human health issues. The covered technologies include genomics, transcriptomics, epigenomics, proteomics, and partially, metabolomics. We will also discuss the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles of omics data management. The focus is not on a particular biological phenomenon but on the application of omics to research and diagnostics in life sciences.

Here are examples of questions we will cover. How can we derive conclusions from the large omics datasets? What factors shall we consider in the design of experiments involving omics approaches? What approaches and principles does one apply to analyse omics data?

Structure of the course 
In weeks 1-3, you will learn about genomics and apply this knowledge in the tutorial on genome-wide association mapping (GWAS). You will also learn about programming and data visualization in R, which will be used during tutorials and assignments in the remainder of the course. We will cover the basic use of the version control system in GitHub. In weeks 3-5, transcriptomics approaches are explained, and related data analyses and visualization methods will be practiced during tutorials. In week 6, you will learn about the principles and practicalities of computational epigenomic analyses. Weeks 7, 8, and 9 cover proteomics and metabolomics technologies. In week 9, you will also learn about principles of the omics data management. The course includes guest lectures with specialists working with omics data in industry and research institutions.
 
The course finishes with a final project where students present an omics workflow, discuss data management, and visualize results of an omics analysis from primary scientific articles.

Relation to other courses

This course extends on the knowledge acquired in:
MBLS: Mathematics and Programming (MBLS-102), Functional Biology (MBLS-107), Molecular Biology and Biochemical Techniques (MBLS-202), Genomes, Cells and Tissues (MBLS-204), Bioinformatics and Dynamical Modeling (MBLS-207)
Biology: Biological Models & Statistics (B-B1KWBI20), Molecular Genetic Research Techniques (B-B2MGOT14), Cells & Tissues (B-B3CEWE), Data Science & Biology (B-B2DSB18)

Entry requirements & recommended courses
You should have passed the following courses: 
MBLS: Mathematics & Programming (MBLS-102), Functional  Biology (MBLS-107), Molecular Biology & Biochemical Techniques (MBLS-202).
Biology: Biological Models & Statistics (B-B1KWBI20), Molecular Genetic Research Techniques (B-B2MGOT14)

Recommended courses are:
MBLS: Genomes, Cells & Tissues (MBLS-204) and Bioinformatics & Dynamical Modeling (MBLS-207)
Biology: Cells & Tissues (B-B3CEWE) and Data Science & Biology (B-B2DSB18)


Teaching methods & formats
For communication and sharing materials in this course, we will use Brightspace. Different teaching methods are used:

• Lectures
• Lectures with invited speakers
• Tutorials and hands-on sessions on real omics data by using data analysis software and programming tools
• Self-study, where the students can prepare before the lesson by reading articles and completing self-study assignments

You will perform short assignments in different weeks to repeat and better understand learned materials. Also, you will have short online tests before lectures and tutorials, which is aimed to help you prepare for learning in the classroom.

Estimation of each teaching method in this course:
Lectures 20%
Tutorials 40%
Self-study 40%

Assessment

1. Midterm exam: multiple choice and open questions exam covering the first 5 weeks (35%)
2. Final exam (wk9): multiple choice and open questions exam covering topics in weeks 6-9 (35%)
3. Final presentation on a multi-omics case (week 10) (10%)
4. Weekly assignments (20%)

 In order to pass:

• Each exam should be ≥5.0, and the average of the mid-term and final exam should be ≥5.5
• The average grade of the final project and weekly assignments should be ≥5.5
• Attendance at lectures, tutorials and seminars is mandatory (70% attendance is required)

Important note:

• Resit is possible for the mid-term and the final exam. If a student fails both exams, the resit includes material of both exams.