General:
Learning goals:
Knowledge and insight.
After the course, the student can:
explain the bioinformatic steps (the "pipeline") of analyzes that lie between the generation of sequencing data and the biological interpretation, as this takes place in many original articles within the Life Sciences.
Skills
The student can:
Apply the Python programming language;
Understand primary genomics literature and especially the "pipelines" described in so-called "supplemental methods" of articles;
Program simple "pipelines" to move from sequencing data to biological interpretation;
Apply the above bioinformatics skill to
- map transcription regulation networks and their disruptions;
- Detect DNA variations, such as mutations and chromosome variations, such as those found in the genome of tumours, Predict protein function through data integration in networks and comparative genomics betwen species
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Content:
In this course, attention is paid to understanding and working with large amounts of data as has been obtained in recent years with genetic and molecular research. These technological developments require new skills and concepts to be able to understand and conduct life science research. Successively work with mutations and sequencing data will be used. The regulatory network is studied and how the effect of mutations in proteins can be better explained through evolution. The Python programming language is used throughout the course, for scripting.
Keywords: (cancer) genomics, reguloom, transcriptome, proteome, genome and protein evolution, SNP / CNV calling.
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