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Project based learning in Bioinformatics
Vera van Noort
18 May 2016
Molecular Biology
Programming
Courses in Bioinformatics
Mathematics/ Statistics
Bioinformatics
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
Common package (3 stp)
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
Bioinformatics Practical computing for Bioinformatics (3 stp)
Semester 2, 3, 4 Common package (32 stp)
Bioinformatics (9 stp) Omics techniques and data analysis (5 stp) Management of large-scale omics data (4 stp)
Statistics (9 stp) Statistical Methods for Bioinformatics (5 stp) Dynamical systems (4 stp)
Biology (14 stp) Molecular interactions: theories and methods (4 stp) Biomolecular model building (5 stp) Model organisms (5 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)
Thesis work (26 stp)
Common package (4 stp)
Statistics (4 stp) Support vector machines: Methods and applications (4 stp)
Issue
After the course curriculum students did not have practical bioinformatics skills
Solution: Practical skill courses
Practical computing for bioinformatics
Statistics for bioinformatics
Omics techniques and data analysis
Comparative and regulatory genomics
Integrated Bioinformatics Project
F1 S1
F1 S2
F2 S1
Master’s Thesis
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.
Departments – Research groups Microbiology and Immunology
M2S- Microbial and Molecular Systems
ESAT (Electrical Engineering)
Biosystems
Human Genetics
Organization
Course coordinator
Coaches Assistants/professors
Student teams (3-4 students) Provide project ideas
Provide feedback
Provide guidance
Report progress Present results
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 Brainstorming, planning
According to needs
Students, coaches, coordinator
Poster presentation At the end of semester
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)
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
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
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
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
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.
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 email 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.
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
Database of plant peptides
Supervised by:
Vera van Noort
Rashmi Hazarika
Database of plant peptides (<40 amino acids)
Evolutionary conservation Functional annotation
0
0.2
0.4
0.6
0.8
1
1.2
5 10 15 20 25 30 35 40 45
pro
babili
ty
TMHMM posterior probabilities for WEBSEQUENCE
transmembrane inside outside
[GFTFSXP]
Previous state of the data
Team project
• Implement database in SQL
• Implement user interface
• Implement search options (Query and Sequence similarity)
• Implement data visualizations
Example project:
Presentation skills
• 5 intermediate presentations
• Poster presentation
– Software demo’s
• Scientific report
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)
Issue solved?
Questions?
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