David Rasmussen's Projects
BEAST 2 package for fitting multi-type birth-death models to phylogenies conditional on mapped types.
Modified implementation of the BDMM multi-type birth-death model for BEAST 2.
Python scipts used to analyze TSWV deep sequence data.
EMSI SARS-CoV-2 Phylodynamics Team Project
Espalier is a Python package for working with discordant phylogenetic trees using maximum agreement forests.
Just an example repo for demonstration
A story example
Phylodynamic inference for stochastic epidemiological models using particle filtering.
BEAST 2 package for phylodynamics with adaptive molecular evolution using the Marginal Fitness Birth-Death model
A BEAST 2 package for phylodynamic inference using epidemiological models with multiple (nested) forms of population structure and time-varying parameters.
Phylodynamic simulations of influenza virus evolution with antigenic and deleterious mutations, implented in Java. This is a modified version of Trevor Bedford's program Antigen (http://bedford.io/projects/antigen/). For more information on the model, see: http://dx.doi.org/10.7554/eLife.07361
Phylodynamic network analysis using pairwise epidemic and coalescent models implemented in BEAST 2.
Phylodynamic inference using particle filtering and particle MCMC. Implemented in Matlab. See Rasmussen et al. (2011) for a full description of the algorithms: https://doi.org/10.1371/journal.pcbi.1002136
Efficient likelihood-based phylodynamic learning using birth-death models in TensorFlow.
Data from our meta-analysis of pleiotropic fitness effects of viral mutations
Simulation code and data for localizing recombination breakpoints in sequence data.
Test for hosting project docs on Read the Docs
Jupyter notebooks for SIR epidemic models hosted on binder.
Repository for lectures held at the 2023 TTB workshop in Squamish, British Columbia.
Slide deck introducing high school students to the diversity of viruses infecting bacteria, plants and invertebrates.