Difference between revisions of "BCB410"
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Revision as of 20:24, 21 September 2012
BCB410 2012
Contents
Objectives and Participants
The "Applied Bioinformatics" course is offered as a part of the BCB curriculum to ensure that our students know enough about application issues in the field to be able to put their knowledge into practice in a research lab setting. This is to support the Specialist Program goal: to prepare students for graduate studies in the discipline.
As a required course in the BCB curriculum, BCB410 assumes the prerequisites and goals of fourth-year students in the BCB Specialist Program. Other students may participate but they may need to catch up on prerequisites in computer science or life-science courses that BCB students have taken at this point. They may also need to consider whether their objectives match the course objectives well. Generally speaking, this is an advanced course that presupposes familiarity with programming principles, algorithm analysis, and methods of modern systems biology, as well as introductory knowledge of linear algebra, graph theory, information theory, statistics as well as molecular–, structural– and cellular biology.
Organization
Dates and Location
Classes meet Wednesdays between 10:00 and 12:00 in MS 2394 throughout the Fall Term.
Contact
Contact within the class is easiest via the Google Group that you have been subscribed to.
Marking
| Activity | Weight | |
| Design and coordination of your unit | 20 marks | |
| Delivery and contents of presentation | 20 marks | |
| Quality of exercises/assignments | 30 marks | |
| Participation | 10 marks | |
| Final exam | 20 marks | |
| Total | 100 marks | |
Contents
A syllabus of learning units
Working from a general collection of topics in the field, we identify learning units that are of the greatest interest and greatest relevance for the students in the class. We jointly select the most suitable topics. Every student in class will take responsibility for development and delivery of one of the learning units.
Unit contents and delivery
The detailed contents for each unit is to be be discussed with the coordinator. Each student will to lead a two hour session on their topic.
Presenter's responsibilities include[1]:
- Outline of the unit contents, to be completed at least three weeks in advance; This is to include:
- a detailed lecture outline that includes an introduction, discussion of algorithms, presentation of examples, exposition of practical- and implementation issues and an outlook on future developments in the field;
- suitable pre-reading material;
- an outline of exercises for the class;
- Iteration of the unit contents with the coordinator, to be completed at least two weeks in advance.
- Developing a set of exercises (iterated with the coordinator) around the implementation of the topics , at least one week in advance;
- Communication of pre-reading materials to your classmates, at least one week in advance;
- Delivery of your lecture at a sufficiently technical level to be appropriate for an advanced fourth-year course and engaging the class in discussion;
- Communication of exercise materials to the class, at or directly after the lecture;
- Drafting a final-exam question that tests the successful completion of the exercises, at the latest one week after the lecture.
Audience responsibilities include:
- Pre-reading before class;
- Active participation in the discussion;
- Feedback on the exercises and completion in due time.
Schedule
This schedule exceeds the available dates of the term and additional time slots will be agreed on, as required, during the second half of the term. Students may swap their presentation dates among themselves but the coordinator must be informed of swaps.
| Week | Presenter | Topic |
| 1 | Neda Raji | High-throughput sequencing |
| 2 | Andrei Soltan | unix tools* |
| 3 | Inna Dimenshtein | R programming* |
| 4 | Andrew Lugowski | Text mining |
| 5 | Lorenz Breu | Network metrics |
| 6 | Samuel Law | BioPython |
| 7 | Kyle Kim | Correlation discovery in large datasets* |
| 8 | Dylan Bethune-Waddell | Pattern discovery* |
| 9 | Taras Gordiyenko | High performance computing |
| 10 | Chiho Kwon | Clustering* |
| 11 | Harun Mustafa | Cluster quality metrics |
* Older material and/or previous lectures on these topics are available. Coordinate with me ...
Notes
- ↑ Details may vary as required, by mutual agreement.
