Master of Bioinformatics

Vera van Noort

What is bioinformatics?

Using informatics methods to study biology

Developing software for biologists

Developing analysis methods for new types of biological data

Biological sequence analysis

Molecular evolution

DNA sequencesProtein structures

ModelingDatabases

Ontologies

Genomics

Systems Biology

Introduction of Machine learning to Biology

New sequencing technologies

Require new analysis methods

Predicting new drugs

van Noort et al, Cancer Research 2014

New biochemical technologies

Vonkova et al, Cell 2015

Recent improvements with AI

DeepMind: AlphaFold

Bioinformatics and Data Science

What do you need to do bioinformatics

Computer Science

Molecular Biology Mathematics/Statistics

Bioinformatics

Objectives1. The student possesses a broad knowledge of the principles of genetics, biochemistry

and molecular and cellular biology that underlie the model systems, the experimental techniques, and the generation of data that are analyzed and modeled in bioinformatics.

2. Possesses a broad knowledge of the basic mathematical disciplines (linear algebra, calculus, dynamical systems) that underlie mathematical and statistical modeling in bioinformatics.

3. Masters the concepts and techniques from information technology (database management, structured and object-oriented programming, semantic web technology) for the management and analysis of large amounts of complex and distributed biological and biomedical data.

4. Masters the concepts and techniques from machine learning and frequentist and Bayesian statistics that are used to model complex omics data.

5. Has acquired knowledge of the core methods of computational biology (such as sequence analysis, phylogenetic analysis, quantitative genetics, protein modeling, array analysis).

Objectives6. Has advanced interdisciplinary skills to communicate with experts in life sciences,

applied mathematics, statistics, and computer science to formalize complex biological problems into appropriate data management and data analysis strategies.

7. Can - in collaboration with these experts - design complex omics experiments and analyze them independently.

8. Can independently collect and manage data from specialized literature and public databases and critically analyze and interpret this data to solve complex research questions, as well as develop tools to support these processes.

9. Investigates and understands interaction with other relevant science domains and integrate them within the context of more advanced ideas and practical applications and problem solving.

10. Demonstrates critical consideration of and reflection on known and new theories, models or interpretation within the specialty; and can efficiently adapt to the rapid evolution the life sciences, and especially in omics techniques, by quickly learning or developing new analysis strategies and incorporating them into the learned competences.

11. Presents personal research, thoughts, ideas, and opinions of proposals within professional activities in a suitable way, both written and orally, to peers and to a general

12. Develop and execute original scientific research and/or apply innovative ideas within research units.

Objectives13. Understands ethical, social and scientific integrity issues and responsibilities and is

able to analyse the local and global impact of bioinformatics and genomics on individuals, organizations and society.

Major Engineering14. Has a broad theoretical knowledge of methodology in computer science and can

apply this knowledge to design, implement and evaluate a computer-based system, process, component or programme to solve technical bioinformatics problems.

15. Has advanced skills in data analysis methodologies and can apply these skills to integrate data from multiple disciplines to solve bioinformatics problems in scientific, clinical or biotechnological environments.

Organization

Permanent Education Committee• Programme director (Vera van Noort)• Administrative assistant (Hanneke Deleu)• Representatives from Faculties of Science, Biomedical

Science, Engineering, Bioscience Engineering• Representative from industry• Representatives from assisting personnel• Student representatives (chosen)

Toledo community

Program structure

Common package (3 stp)

Reorientation package (14 stp)

Reorientation biology 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)

Semester 1

Bioinformatics Practical computing for Bioinformatics (3 stp)

Engineering (12 stp)Design of Softwre Systems(6 stp)Engineering choice course (6 stp)

ORErasmus exchange program

Semester 2, 3Common 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) Structural Bioinformatics (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)

Semester 4

Thesis work (26 stp)

Common package (4 stp)

Statistics (4 stp)Support vector machines: Methods and applications (4 stp)

Track Engineering (title ir)

ThesisUnder supervision of Faculty of

Engineering Science

Computer Science

Mathematics/ Statistics

Covered in Bachelor

Reorientation Biology.Design of software systemsEngineering choice course

Bioinformatics groups at KU LeuvenBioscience Engineering

van Noort Jelier

Engineering Science

Moreau J. Aerts De Moor

S. Aerts Raes

Lemey

Medicine

Vandamme

Voet

Lambrechts

Verstrepen

VermeeschSchymkowitz Rousseau

Science

+ …

Volckaert

Access to HPC at KU Leuven

Ethical implications of Bioinformatics and Genomics

CRISPR Babies

Medical Data SharingExchange of genomic data

Project presentations

Thesis projects• Main supervisor Faculty of Engineering Science

• Possibility to carry out thesis together with industry/abroad

Thesis presentations

What is a bioinformatician?• Data scientist

o Interdisciplinarityo Big Datao Applications to Life Sciences

• Job opportunitieso Pharmao Biotecho Hospitalso PhD o Data analysis and interdisciplinarity key in many areas

of industry

Even unexpected job opportunities

Key strengths of the Master of Bioinformatics

• Flexibility in admission

• Heterogeneity in learning community

• Diversity of teaching methods

• Embedding in strong research community

• Diversity in employability options

AdmissionBa BiologyBiochemistryMathematicsMedicine

Ba Bioscienceengineering

Ba EngineeringCS

Track Science

Track BioscienceEngineering

Track Engineering

Key strengths of the Master of Bioinformatics

• Flexibility in admission

• Heterogeneity in learning community

• Diversity of teaching methods

• Embedding in strong research community

• Diversity in employability options

Heterogenous learning communityNumber of students Difference with 2015-2016 Difference with 2011-2012

Gender Percentage international students

F

Figures MSc Bioinformatics

Difficulty

Gene technology

MachineLearning

MultivariateStatistics

BayesianModeling

Key strengths of the Master of Bioinformatics

• Flexibility in admission

• Heterogeneity in learning community

• Diversity of teaching methods

• Embedding in strong research community

• Diversity in employability options

Diversity of teaching methods• Theoretical lectures• Embedding of practical skills in the program, work at the computer

o Practical computing (M1, S1)o Omics techniques and data analysis (M1, S2)o Structural Bioinformatics (M1, S2)o Evolutionary and Quantitative Genetics (M2, S1)o Comparative and Regulatory Genomics (M2, S1)o Integrated Bioinformatics Project (M2, S1)o Thesis (M2, S1/S2)

• Access to KU Leuven HPC interactive nodes

• Debates

Key strengths of the Master of Bioinformatics

• Flexibility in admission

• Heterogeneity in learning community

• Diversity of teaching methods

• Embedding in strong research community

• Diversity in employability options

Key strengths of the Master of Bioinformatics

• Flexibility in admission

• Heterogeneity in learning community

• Diversity of teaching methods

• Embedding in strong research community

• Diversity in employability options

Employability

Master of Bioinformatics

PhD

R&D/Industry

50%

50%

Academic career10%

VDAB : 0% unemployed after 1 yearCW, BMT: 0%ET: 1.6%

Student satisfaction

in general, I am satisfied with the programme I followed

The programme challenged me to work hard during my studies

During the programme there is sufficient variation in teaching methods

The programme prepares for working in an international environment

During the programme I have learned to use scientific methods and techniques

The ICT infrastructure fits the needs

1: totally disagree 6: totally agree