Phylodynamic analysis of SARS-CoV-2
Title: Phylodynamic analysis of SARS-CoV-2
Lecturer: Prabhav Kalaghatgi
Maximum number of participants: 10
Period/preliminary appointment: tba
Location: Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, PC-Pool
Short description of content:
Over 10 million SARS-CoV-2 genomes have been sampled during the ongoing pandemic making available an unprecedented amount of data to study virus evolution. Phylogenetic trees are tree-structured models of evolutionary relationships. Phylogenetic trees of fast-evolving pathogens contain information about how quickly the pathogen is growing in number.
The software practical will introduce students to the principles of phylogeny inference and discuss tools that are being used during the pandemic such as UShER, nextstrain, and beast2. You will be introduced to the theory and practice of Markov chain Monte Carlo with an emphasis on the use of epidemiological models. Phylodynamics makes use of phylogenetic trees along with sampling time of sequences in order to characterize epidemiological dynamics using effective reproductive number (R0) which is used to characterize the rate at which an epidemic is growing. An R0 greater than 1 indicates an exponentially increasing growth curve whereas an R0 less than 1 indicates that the epidemic is declining.
Students will work in teams of two. At the end of the course each team will present their project and deliver a short report
Quantitative allocation (in %):
Practical programming work: 60 %
Soft skills: 40%
Programming language(s) used: Python
Difficulty level (Eight stars distributed over three areas):
C Projekt management:***
Required previous knowledge: Basics of probability theory and Markov chains. These are covered in the course Algorithmische Bioinformatik.
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