Bioinformatics Seminar (MTAT.03.242)
- The first meeting will take place on February 6.
- Seminars: Monday 10:15 - 12:00, (J.Liivi 2-512)
- Contacts: Anna Ufliand (anna.ufliand [at] ut.ee), Sulev Reisberg (sulev.reisberg [at] ut.ee)
- BIIT (Bioinformatics, Algorithmics, and Data Mining group) group website : http://biit.cs.ut.ee/
Bioinformatics is an interdisciplinary field that combines computer science, biology, statistics and mathematics for understanding biological data.
Aims of the course (the student that has passed the course ...):
- Knows the basics of the Bioinformatics field
- Has a brief overview of what are the current trends in Bioinformatics
- Has improved his/her presentation and teaching skills
Look at the submenu on the left.
- You have to take part of the seminars. If you do not miss more than 4 seminars, you’ll get 30 points. Every further missed seminar gives (takes) 5 points less (total can go to minus).
- You have to make two presentations on selected bioinformatics topics (15 p each) + prepare relevant homework for the others and grade their submissions (10p). The presentations topics have to be selected in time.
- You have to do the homeworks by the deadlines (20 p).
- All deadlines are strict
- At some point in the middle of the semester we will have a journal club (maybe several of them). Participation in journal club (or writing a summary of the discussed articles) (10 p)
Prerequisites for the grading:
- Student has presented his topics in the lectures
- Student has given the homework for the others
- Student has graded the homeworks of the others
Student has passed the course if prerequisites are satisfied and the calculated score is >50 points:
Score = attending the seminars (max 30p) + given two presentations (15p both) + given homework and given grades (10p) + doing the homeworks (max 20p) + participation in journal club (10p).
The resulting grade (out of 100 points) will be mapped to a grade between A and F using the standard University scale:
- 91-100 points: A
- 81-90 points: B
- 71-80 points: C
- 61-70 points: D
- 51-60 points: E
- 0-50 points: F
Current state of the points and corresponding grades can be seen here: https://docs.google.com/spreadsheets/d/1u0dndZThcE3Mse2G7ANAX4JPec-wPWErYPXaCkfdC5o/edit?usp=sharing
The aim of every presentation is to cover the chosen topic and improve your teaching skills.
The topics will be assigned to the students randomly from the proposed data types list. If you need an advice for preparing the presentation, ask Sulev or Anna.
The length of each presentation is expected to be 20 min.
Every presentation does not have to be just slides + talk only. Make it interactive, include discussions, "hands on" opportunities, surveys, etc.
Questions to be answered in the first presentation
- What is the biological origin and “knowledge” of this kind of data?
- What is the technology to extract the data from sample?
- How do you get the “numbers” from there?
- How the data looks like (formats, examples)?
- What are the public data sources (databases, tools) to store it?
- What information can you get from this data (relation to phenotype)?
- What is the metadata here?
Questions to be answered in the second presentation
- What was the goal of the analysis?
- What kind of additional data they used (relation to phenotype)?
- What is the computational challenge here (algorithms, big data, statistics)?
- What analysis methods and pipeline did they used?
- What did they find?
Homeworks (the number of homeworks depend on the number of registered students)
Additionally you have to prepare a homework for the others based on the first presentation topic.
Expected amount of effort for doing a homework is approx. 0.5-1.0 hour.
The homework have to be presented in the end of your presentation.
Homeworks are sent directly to you via e-mail in 1 week. You have to grade all the submitted homeworks in additional 1 week and send the grades together with the correct solution to Anna or Sulev.
We have called several guest lecturers to give you the broad overview of the state-of-the-art research of the bioinformatics field.