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?