Bioinformatics (MTAT.03.239)

Course info

• The first meeting will take place on February 10.
• Starting from this year, we will be using team-based learning (TBL). This means that there will no formal lectures. Instead, we will have regular structured meeting to discuss reading assignments and solve problems in teams.
• Meetings/Practicals: Mondays 14:15 - 18:00 in Narva street 18-1022
• We have the seminar room booked for 4 hours, but on most weeks we'll probably finish by 16:00.
• Parallel course: Special Course in Machine Learning: Deep Learning in Genomics

Contacts

• Assignment submissions: Moodle
• Course materials: https://github.com/kauralasoo/MTAT.03.239_Bioinformatics
• Lecturers: Kaur Alasoo (kaur.alasoo [at] ut.ee), Erik Abner, Priit Adler, Ralf Tambets

Pre-requisites

Although the course has no official pre-requisites, basic programming and data analysis skills are required to successfully complete the homework and project assignments.

Learning objectives

• You will learn how to transfer the theoretical skills that you have learned in Data Mining, Neural Networks and Algorithmics courses to practical data analysis problems, with a particular focus on biology.
• You will know how to assess the quality of data via exploratory analysis (clustering, PCA) and know how to detect and when to remove outlier samples.
• You will understand the basic concepts of information flow in biological systems (genetic variant -> TF binding -> gene expression -> protein expression -> cellular phenotypes/imaging -> organismal phenotypes ) and what are the main biological and computational approaches to analyse it.

Grading and requirements

The grade is calculated from the total number of points (max 100). The points can be earned as follows:

• Assignments (50 points): there will be ~10 assignments, each worth ~5 points;
• "Team project" (50 points): active participation in the team-based learning sessions will count towards the project score (i.e. participating in all sessions can give you up to 50 points. ;
• Additional points can be earned from bonus tasks within assignments;
• Other arrangements are possible only in exceptional circumstances.

In order to pass the course, the student must get at least 50% from assignments (threshold 25 points), at least 50% from the project (threshold 25 points).