- CanEvolve.jl - generating synthetic VAF distributions representative of tumour evolution
- TumE - fast evolutionary inferences in sequenced tumour biopsies using synthetic supervised learning
Other Software References
- MOBSTER - a mixture model for subclone detection that properly accounts for neutral dynamics in tumour populations
- CancerSeqSim.jl - a tumour evolution simulator based on stochastic branching process
- sciClone - a variational Bayesian mixture model for subclone detection that does not take into account neutral dynamics in tumour populations
- TEMULATOR - a tumour evolution simulator based on stochastic branching process (similar to CancerSeqSim.jl) but with faster implementation
Paper references
- Caravagna et al. 2020 Subclonal reconstruction of tumour populations using machine learning and population genetics. Nature Genetics
- Fay and Wu 2000 Hitchhiking under positive darwinian selection. Genetics
- Miller et al. 2014 SciClone: Inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLOS Computational Biology
- Tajima 1989 Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics
- Werner et al. 2020 Measuring single cell divisions in human tissues from multi-region sequencing data. Nature Communications
- Williams et al. 2016 Identification of neutral tumour evolution across cancer types. Nature Genetics
- Williams et al. 2018 Quantification of subclonal selection in cancer from bulk sequencing data. Nature Genetics