Genetic Simulation Resources (original) (raw)
Basic Package Attributes
| Attribute | Value |
|---|---|
| Title | OncoSimulR |
| Short Description | BioConductor package for Forward Genetic Simulation of Cancer Progresion with Epistasis |
| Long Description | An R/BioConductor package that provides functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, and additive models) and plotting them. |
| Keywords | cancer, mutation, simulation, evolution, mutator, epistasis, fitness landscape, cancer progression models |
| Version | 2.13.1 |
| Project Started | 2015 |
| Last Release | 6 years, 4 months ago |
| Homepage | https://github.com/rdiaz02/OncoSimul |
| Citations | Diaz-Uriarte R, OncoSimulR: genetic simulation with arbitrary epistasis and mutator genes in asexual populations., Bioinformatics, June 15, 2017 [ Abstract, cited in PMC ] |
| GSR Certification |  ✔ Accessibility✔ Documentation✔ Application✔ Support |
| Last evaluated | Jan. 10, 2019 (2291 days ago) |
| Author verification | The basic description provided was derived from a website or publications by the GSR team and has not yet been verified by the simulation author. To modify this entry or add more information, propose changes to this simulator. |
Detailed Attributes
| Attribute Category | Attribute |
|---|---|
| Target | |
| Type of Simulated Data | Haploid DNA Sequence, |
| Variations | Biallelic Marker, Genotype or Sequencing Error, |
| Simulation Method | Forward-time, |
| Input | |
| Data Type | Ancestral Sequence, Other, |
| File format | Program Specific, |
| Output | |
| Data Type | Genotype or Sequence, Individual Relationship, Demographic, Mutation, Diversity Measures, Fitness, |
| Sequencing Reads | |
| File Format | Program Specific, |
| Sample Type | Random or Independent, Longitudinal, Other, |
| Phenotype | |
| Trait Type | |
| Determinants | |
| Evolutionary Features | |
| Demographic | |
| Population Size Changes | Exponential Growth or Decline, Logistic Growth, |
| Gene Flow | |
| Spatiality | |
| Life Cycle | Overlapping Generation, |
| Mating System | |
| Fecundity | Individually Determined, Influenced by Environment, |
| Natural Selection | |
| Determinant | Single-locus, Multi-locus, Fitness of Offspring, Environmental Factors, |
| Models | Directional Selection, Multi-locus models, Epistasis, Random Fitness Effects, |
| Recombination | |
| Mutation Models | Two-allele Mutation Model, |
| Events Allowed | Varying Genetic Features, |
| Other | |
| Interface | Command-line, Script-based, |
| Development | |
| Tested Platforms | Windows, Mac OS X, Linux and Unix, |
| Language | C or C++, R, |
| License | GNU Public License, |
| GSR Certification | Accessibility, Documentation, Application, Support, |
Number of Primary Citations: 1
Number of Non-Primary Citations: 3
The following 3 publications are selected examples of applications that used OncoSimulR.
2018
Diaz-Uriarte R, Cancer progression models and fitness landscapes: a many-to-many relationship., Bioinformatics, March 1, 2018[Abstract]
Schoen D, Schultz S, Somatic mutation and evolution in plants, Annual Review of Ecology, Evolution, and Systematics, vol. 50, in press, None
Diaz-Uriarte R, Vasallo C, Every which way? On predicting tumor evolution using cancer progression models, bioRxiv, None[Abstract]