Bio-informatics

Bioinformatics is the field where computer and information technology are used to collect, store, make accessible, analyse, interpret, and disseminate data from biomedical and/or biological research.

Key areas within this field include genomics, transcriptomics, proteomics (including protein modelling, structure, and function), metabolomics, and the integration of data from these areas. Bioinformaticians work in biological and biomedical research at scientific institutions, as well as in companies within the pharmaceutical, biotechnology, food, and plant breeding industries.

National Educational Programme

Body of Knowledge & Skills

Knowledge

Biology

• General biology, including organisms, tissues, evolution.
• Cell biology aspects including cell structure, energy supply, transport, cell-cell communication, mitosis, meiosis.
• Genetics: DNA structure, replication, coding, transcription, translation, mutations, inheritance, homology, population genetics, gene regulation, signal transduction pathways.

Chemistry

• General chemistry including molecules, reactions, kinetics.
• Biochemistry: building blocks, macromolecules (carbohydrates, lipids, proteins, enzymes), metabolism / metabolic pathways.

Skills

Informatics

• Programming in different programming languages. At least Python, but also at least two other languages such as R, Java, Bash, Javascript.
• Programming concepts: data types, data structures, control structures, functions, modular approach, object-oriented programming, regular expressions.
• Reproducible dependency management: e.g. pip, conda, Gradle, Maven.
• Version management: for example, with git
• Web application development: e.g. HTML, CSS, Javascript, Flask, Shiny, Django, Spring Boot, Panel.
• Databases: relational design, implementation, querying and programmatic interaction (SQL).
• Workflow design and implementation: e.g. Snakemake, Galaxy
• Manage own Linux environment: use of Shell commands

Bioinformatics

• ~omics and (NGS) sequencing technologies: nucleotides, proteins, metabolites, metagenomes, epigenetics.
• Application areas of NGS technologies: e.g., assembly, mapping, gene expression analysis with RNA-seq, phylogeny, variant analysis.
• Algorithmic aspects of sequence analysis: such as alignment, assembly, mapping, graphs, scoring matrices, annotation of sequences.
• Practical use of bioinformatics tools and databases: e.g. BLAST, Genome Browsers, Genbank, Uniprot, KEGG, etc.

Statistics & Data Analysis

• Experimental design: e.g., power analysis, sampling strategy, measurement accuracy, validity.
• Descriptive statistics: embedded in exploratory analysis. For example, mean, median, standard deviation, range, interquartile range.
• Visualizations: e.g. box-plot, histogram, scatterplots, etc.
• (Hypothesis) tests: e.g. t-test, ANOVA, non parametric, etc.
• Modeling: e.g. linear and nonlinear regression, multivariate, PCA.
• Machine learning: classification and unsupervised methods. For example UMAP, Clustering, Decision Trees, Naive Bayes, k-Nearest Neighbour, Neural Networks, SVM.

The Body of Knowledge and Skills is a summary of graduates’ basic knowledge and basic skills which has been prepared by the HBO-programmes in consultation with the professional field. These are obtained during the first two years of education.