Project based learning in Bioinformatics. Vera van Noort 18 May 2016

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1 Project based learning in Bioinformatics Vera van Noort 18 May 2016

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3 Courses in Bioinformatics Programming Mathematics/ Statistics Bioinformatics Molecular Biology

4 Semester 1 Vooropleiding: Bachelor in de bio-ingenieurswetenschappen Bachelor in de biochemie en de biotechnologie Bachelor in de biologie Bachelor in de biomedische wetenschappen Bachelor in de chemie Bachelor in de fysica Bachelor in de geneeskunde Bachelor in de geografie Bachelor in de geologie Bachelor in de ingenieurswetenschappen Bachelor in de wiskunde Reorientation package (26 stp) Reorientation biology (21 stp) Basics of Biological Chemistry (4 stp) Basic Concepts of Cell Biology (5 stp) Structure, Synthesis and Cellular Function of Macromolecules (3 stp) Introduction to Genetics (5 stp) Gene Technology (4 stp) Reorientation statistics (5 stp) Univariate data and modelling (5 stp) Reorientation mathematics (12 stp) Linear Algebra (7 stp) Calculus (5 stp) Reorientation information technology (14 stp) Basic Programming (4 stp) Object Oriented Programming (4 stp) Database Management (6 stp) Complementary reorientation (up to 26 stp) Optional courses Common package (3 stp) Bioinformatics Practical computing for Bioinformatics (3 stp)

5 Semester 2, 3, 4 Common package (32 stp) Biology (14 stp) Molecular interactions: theories and methods (4 stp) Biomolecular model building (5 stp) Model organisms (5 stp) Statistics (9 stp) Statistical Methods for Bioinformatics (5 stp) Dynamical systems (4 stp) Common package (4 stp) Statistics (4 stp) Support vector machines: Methods and applications (4 stp) Thesis work (26 stp) Bioinformatics (9 stp) Omics techniques and data analysis (5 stp) Management of large-scale omics data (4 stp) Common package (25 stp) Statistics (9 stp) Machine learning and inductive inference (4 stp) Applied multivariate statistical analysis (5 stp) Bioinformatics (16 stp) Bayesian modelling for biological data analysis (4 stp) Evolutionary and quantitative genetics (4 stp) Comparative and regulatory genomics (4 stp) Integrated bioinformatics project (4 stp) Thesis work (4 stp)

6 Issue After the course curriculum students did not have practical bioinformatics skills

7 Solution: Practical skill courses Practical computing for bioinformatics F1 S1 Statistics for bioinformatics Omics techniques and data analysis F1 S2 Comparative and regulatory genomics Integrated Bioinformatics Project F2 S1 Master s Thesis

8 Aims - Design and implementation of a bioinformatics solution. Translating a biological problem first into a data analysis strategy and then into a practical implementation. - Integration of skills from the courses of the bioinformatics module: bio-molecular model building, high-throughput analysis, omics data management, comparative and regulatory genomics, evolutionary and quantitative genetics, Bayesian modeling for biological data analysis. - Teamwork and communication skills.

9 Departments Research groups Microbiology and Immunology Human Genetics M 2 ESAT (Electrical Engineering) S - M i c r o B i o s y s t e m s

10 Organization Course coordinator Provide feedback Provide project ideas Coaches Assistants/professors Provide guidance Student teams (3-4 students) Report progress Present results

11 Activities Who What When Students, Coordinator, (Coaches) Feedback session 6 x two hours during the first semester Student teams Team work 4 hours per week Students, Coaches Students, coaches, coordinator Brainstorming, planning Poster presentation According to needs At the end of semester

12 Feedback sessions 1. Presentation of projects (student- + teaching-team-initiated). Set-up teams (coordinator) 2. Presentation of relevant literature and available resources, project planning and solution design. 3. Presentation of data structures, programming languages, analysis pipelines, first results 4. Presentation of implementation (focus on problems for feedback) 5. Presentation of implementation and interpretation of results (focus on problems for feedback) 6. Presentation of implementation and interpretation of results (focus on problems for feedback)

13 Activities Who What When Students, Coordinator, Coaches Feedback session 6 x two hours during the semester Student teams Team work 4 hours per week Students, Coaches Brainstorming, planning According to needs

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15 Practicalities Teams of 3-4 students Mix expertises Mix nationalities! Elements from at least 3 courses (interdisciplinarity) Contact hours every two weeks Monday (mandatory) PC-room available Tuesday 9-13 (Time management) Final presentation Paper (max 5 pages) Poster session, software demo Be creative! Include the whole team

16 Facilities Accounts for all students <2 hours compute jobs After motivation VSC account for 1,000 credits ICTS PC Classrooms Facilities of individual research groups

17 Evaluation Permanent evaluation Participation in discussion during feedback sessions Progress during the semester Evaluation by team coach Jury at the poster session Answer questions individually Peer/self evaluation

18 Fair evaluation How much work did the students do? a) Less than I expected b) Exactly the amount I expected c) More than I expected d) A lot more than I expected (at least twice as much) What was the quality of the team work? a) bad. Eg, results were irreproducable by my own people. Sloppy work. b) ok. I would still let my own postdocs/phd students redo the analysis/reimplement the bioinformatics solution before would publish this/make this available to collaborators. c) Good. With some additional quality checks, this work is publication quality. d) Excellent. The work is comparable to the highest standards in my lab. How independent did the students carry out their project? a) I had to explain every step in the analysis (twice) b) They needed some explanation and/or help with problem-solving and carried out some work independently c) The majority of the work was carried out independently d) The students worked almost completely independent.

19 Fair evaluation How much input did the students give themselves? a) none, they only did what they were supposed to b) a little bit. They had some ideas for the implementation themselves c) quite a lot. The students had extra ideas for the project d) Once given the data and general idea, the students made their own plan for data analysis/implementation. How easy was it to communicate with the students? a) hard, eg I had to them several times before they answered. They came to meetings unprepared. b) Ok, I had to maintain contact most of the time. They brought results to the meetings that were not always entirely clearly presented. c) Good, the students contacted me and I contacted them for updates. They had clearly presented results to meetings. d) Excellent, the students were active in their communication, sent new updates all the time and were well prepared for meetings.

20 Peer/self evaluation Reflection on contribution Size of contribution to different parts for all team members Grade on scale of 1-10 Result in -1 or +1 on final evaluation

21 Database of plant peptides Supervised by: Vera van Noort Rashmi Hazarika

22 probability Database of plant peptides (<40 amino acids) Evolutionary conservation Functional annotation 1.2 TMHMM posterior probabilities for WEBSEQUENCE transmembrane inside outside

23 Previous state of the data

24 Team project Implement database in SQL Implement user interface Implement search options (Query and Sequence similarity) Implement data visualizations

25 Example project:

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28 Presentation skills 5 intermediate presentations Poster presentation Software demo s Scientific report

29 Advantages Pitfalls Students need to fully acquire skills in order to apply them High motivation (ownership) Both specialist knowledge (one research team) and broad overview (presentations of all teams) Obtain feedback from peers, coaches and coordinator. Project management skills. Interdisciplinary teams. Discussions about Open Source, Open Science, Scientific Integrity, good research practices. Presentation and reporting skills. Some students hide behind good peers Time management sometimes problematic (different course schedules) Commitment of coaches variable Difficulty of projects variable Size of group limited (max 8-10 teams)

30 Issue solved?

31 Questions?