Programming in the Life Sciences
Full course description
In the life sciences the physical interactions between chemical and biological entities, like genes, RNA, proteins, metabolites, and drugs, is of key interest to human health. Not only do these interactions play an important role in the regulation of gene expression, inhibition of proteins, and they basically define all cellular processes and therefore life itself. For example, pharmacology studies the action of drugs on protein, metabolism depends on the interactions of small molecule substrates with enzymes, and coronaviruses reorganise the normal function of cells after entry into the cell.
With the increasing amount of knowledge and data in the life sciences, automation becomes
increasingly important. The data, whether large or small and complex, have challenges to integrate data from different experiments and data sources. Many core life sciences databases, such as WikiData have made their data available in RDF (resource data framework), and provide SPARQL endpoints to their knowledge. In this course, you will learn to use how to interact with SPARQL endpoints with JavaScript and visualise the results graphically with a library like d3.js. Additionally, you will learn to store and share your code through Github.
Course objectives
- To have the ability to recognize various classes of biological or medical entities and to understand how they link to human health;
- To know the programming concepts related to data processing and web services;
- To be familiar with technologies for web services and querying resources in the life sciences;
- To obtain experience in using such web services with a programming language;
- To be able to select web services for a particular biological or medical research question;
- To be familiar with modern software development practices;
- To have the ability to visualise data retrieved from web services.
Prerequisites
-
MAT2007 or
-
PRA2003
Corequisites
- None
Recommended reading
- “JavaScript & jQuery: The Missing Manual” by D.S. McFarland (O'Reilly, 2nd edition, 2011);
- “Wikidata as a knowledge graph for the life sciences” by A. Waagmeester et al. eLife, 2020, https://doi.org/10.7554/eLife.52614;
- “WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research” by D. Slenter et al. NAR, 2018, https://doi.org/10.1093/NAR/GKX1064;
- “Semantic Web programming” by J. Hebeler. 2009. UB Library. https://maastrichtuniversity.on.worldcat.org/oclc/428142652;
- “Semantic Web for the working ontologist: effective modeling in RDFS and OWL” by D. Allemang, J.A. Hendler. 2011. UB Library. https://maastrichtuniversity.on.worldcat.org/oclc/733936673;
- “Git from the Bottom Up” by J. Wiegley. https://jwiegley.github.io/git-from-the-bottom-up/.
- E.L. Willighagen
- R.R.R. Fijten