Course idea and teaching goals of Case studies in Bioinformatics
The general idea of the course "Case studies in Bioinformatics" is to expose the students to a varied set of published bioinformatics analyses that will be re-examined critically in a hands-on fashion. Specifically, we will consider four individual cases, corresponding to four teaching modules. The teacher of each module will introduce a paper and its context, and then guide the students to study in detail one or several of its bioinformatics analysis with the goal to reproduce their results. This will typically involve some programming, where the students can apply and develop their skills in using Python and/or R programming.
The rationale of our course "Case studies in Bioinformatics" as an integral part of the “mention Bioinformatics” in the life science Master is three fold:
- It is quite common that Master or PhD projects build on existing work and it is usually a good start to critically examine some of the key results. This course will train students in how to validate bioinformatics analyses.
- Focusing on published analyses provides a good focus, because usually the analysis pipelines are well described and the data are readily available.
- On some occasions students might find minor or even major mistakes in published analyses. The bioinformatics community would profit from revealing such issues and we will motivate our students to think about how they can share their “question marks” (e.g. on this wiki).
Through the individual modules the course aims to teach key concepts of bioinformatics, including:
- data normalization
- similarity measures
- data (over-)fitting
- cross validation
Students will be asked to provide a written report for one of the modules, detailing their code and explaining their results. The course grade will be based on this report.
In summary this course puts emphasis on developing the analysis and programming skills of UNIL Master students and is a requirement for those studying towards a mention in bioinformatics. By dissecting (rather than reviewing) papers they will really understand how the analyses were done, preparing them for their own future projects.