master of bioinformatics · sequence analysis, phylogenetic analysis, quantitative genetics,...
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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 sequences Protein structures
Modeling Databases
Ontologies
Genomics
Systems Biology
What do you need to do bioinformatics
Programming Mathematics/ Statistics
Molecular Biology
Bioinformatics
What is a bioinformatician? • Data scientist
o Interdisciplinarity o Big Data o Applications to Life Sciences
• Job opportunities o Pharma o Biotech o Hospitals o PhD o Data analysis and interdisciplinarity key in many areas
of industry
Evolutionary biology
Protein sequences DNA sequences Population genetics
Source: Wikipedia
Modeling the cell
Source: Wikipedia
Genomics Sequence analysis methods Measuring thousands of parameters simultaneously Big Data challenge Specialized statistical methods Linking mutations to disease
Source: Wikipedia
New sequencing technologies
Structural bioinformatics
Source: irbb barcelona
Predicting new drugs
van Noort et al, Cancer Research 2014
New biochemical technologies
Vonkova et al, Cell 2015
Bioinformatics as data mining
and/or experiments
Source: dwreview.com
Objectives 1. 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).
6. 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.
Objectives 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.
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
Admission Ba Biology Biochemistry Mathematics Medicine
Ba Bioscience engineering
Ba Engineering CS
Track Science
Track Biocience Engineering
Track Engineering
Programming Mathematics/ Statistics
Molecular Biology
Interdisciplinary program and flexibility • First semester = reorientation
o Biology o Mathematics and statistics o Information Technology
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
Semester 1
Bioinformatics Practical computing for Bioinformatics (3 stp)
Semester 2, 3 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)
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)
Thesis Under supervision of Faculty of
Engineering Science
Programming Mathematics/ Statistics
Covered in Bachelor Minimum 5 stp Engineering course from eg: Master of Engineering: - Computer Science - Nanotechnology - Biomedical technology
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 community Number of students Difference with 2014-2015 Difference with 2011-2012
Gender Percentage international students
F
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
o Practical computing (M1, S1) o Omics techniques and data analysis (M1, S2) o Integrated Bioinformatics Project (M2, S1) o Thesis (M2, S1/S2)
• Access to KU Leuven HPC interactive nodes
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
Bioinformatics groups at KU Leuven Bioscience Engineering
van Noort Jelier
Engineering Science
Moreau J. Aerts De Moor
S. Aerts Raes
Lemey
Medicine
Vandamme
Voet
Lambrechts
Verstrepen
Vermeesch Schymkowitz Rousseau De Maeyer
Science
+ …
Volckaert
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 career 10%
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)