GSR: Simulator - SFS_CODE
| Attribute | Value |
|---|---|
| Title | SFS_CODE |
| Short Description | SFS_CODE can perform forward population genetic simulations under a general Wright-Fisher model with arbitrary migration, demographic, selective, and mutational effects. |
| Long Description | SFS_CODE (Selection on Finite Sites under COmplex Demographic Events) performs forward population genetic simulations under a general Wright-Fisher model with arbitrary demographic, selective, and mutational effects. |
| Version | 20100203 |
| Project Started | 2009 |
| Last Release | 8 years, 9 months ago |
| Homepage | http://sfscode.sourceforge.net/SFS_CODE/index/index.html |
| Citations | Hernandez RD, A flexible forward simulator for populations subject to selection and demography., Bioinformatics, 12-01-2008 [ Abstract, cited in PMC ] |
| GSR Certification |  ✔ Accessibility |
| Last evaluated | 10-15-2015 (1912 days ago) |
| Attribute Category | Attribute |
|---|---|
| Target | |
| Type of Simulated Data | Diploid DNA Sequence, Haploid DNA Sequence, Sex Chromosomes, |
| Variations | Biallelic Marker, Multiallelic Marker, Single Nucleotide Variation, Insertion and Deletion, Other (Large-scale insertions and deletions), |
| Simulation Method | Forward-time, Other (Rejection sampling to simulate data conditional on terminal frequency.), |
| Input | |
| Data Type | Allele Frequencies, Other (Recombination rate variation), |
| File format | MS, STRUCTURE, Program Specific, Other (Fasta), |
| Output | |
| Data Type | Genotype or Sequence, Demographic, Mutation, Diversity Measures, Fitness, |
| Sequencing Reads | Other (Coding or noncoding, sex/autosomal), |
| File Format | Fasta or Fastq, MS, STRUCTURE, Program Specific, Other (Various), |
| Sample Type | Random or Independent, |
| Phenotype | |
| Trait Type | |
| Determinants | |
| Evolutionary Features | |
| Demographic | |
| Population Size Changes | Constant Size, Exponential Growth or Decline, Logistic Growth, Bottleneck, Carrying Capacity, User Defined, |
| Gene Flow | Stepping Stone Models, Island Models, Continent-Island Models, Sex or Age-Specific Migration Rates, Admixed Population, User-defined Matrix, Other (Various), |
| Spatiality | |
| Life Cycle | Discrete Generation Model, User-Defined transition matrices, |
| Mating System | Random Mating, Polygamous, Haplodiploid, Selfing, Other (Arbitrary poidy), |
| Fecundity | Constant Number, Individually Determined, |
| Natural Selection | |
| Determinant | Single-locus, Multi-locus, Codon-based, Fitness of Offspring, |
| Models | Directional Selection, Balancing Selection, Multi-locus models, Random Fitness Effects, Phenotype Threshold, Other (Various), |
| Recombination | Uniform, Varying Recombination Rates, Gene Conversion Allowed, |
| Mutation Models | Two-allele Mutation Model, Markov DNA Evolution Models, Infinite-allele Model, Codon and Amino Acid Models, Indels and Others, Heterogeneity among Sites, Others, |
| Events Allowed | Population Merge and Split, Varying Demographic Features, Population Events, Varying Genetic Features, Change of Mating Systems, Other (Various), |
| Other | |
| Interface | Command-line, Script-based, Web-based, |
| Development | |
| Tested Platforms | Windows, Mac OS X, Linux and Unix, Solaris, Others, |
| Language | C or C++, |
| License | GNU Public License, BSD, |
| GSR Certification | Accessibility, Documentation, Application, Support, |
The following 10 publications are selected examples of applications that used SFS_CODE.
2016
Subramanian S, The effects of sample size on population genomic analyses--implications for the tests of neutrality., BMC Genomics, 02-20-2016 [Abstract]
Lapierre M, Blin C, Lambert A, Achaz G, Rocha EP, The Impact of Selection, Gene Conversion, and Biased Sampling on the Assessment of Microbial Demography., Mol Biol Evol, 07-01-2016 [Abstract]
Kang L, Zheng HX, Zhang M, Yan S, Li L, Liu L, Liu K, Hu K, Chen F, Ma L, Qin Z, et al., MtDNA analysis reveals enriched pathogenic mutations in Tibetan highlanders., Sci Rep, 08-08-2016 [Abstract]
2015
Zhang Q, Tyler-Smith C, Long Q, An extended Tajima's D neutrality test incorporating SNP calling and imputation uncertainties., Stat Interface, 10-01-2015 [Abstract]
2014
He Z, O'Roak BJ, Smith JD, Wang G, Hooker S, Santos-Cortez RL, Li B, Kan M, Krumm N, Nickerson DA, Shendure J, et al., Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data., Am J Hum Genet, 01-02-2014 [Abstract]
Wilson Sayres MA, Lohmueller KE, Nielsen R, Natural selection reduced diversity on human y chromosomes., PLoS Genet, 01-01-2014 [Abstract]
Simkin AT, Bailey JA, Gao FB, Jensen JD, Inferring the evolutionary history of primate microRNA binding sites: overcoming motif counting biases., Mol Biol Evol, 07-01-2014 [Abstract]
Sjöstrand AE, Sjödin P, Jakobsson M, Private haplotypes can reveal local adaptation., BMC Genet, 05-22-2014 [Abstract]
Fu W, Gittelman RM, Bamshad MJ, Akey JM, Characteristics of neutral and deleterious protein-coding variation among individuals and populations., Am J Hum Genet, 10-02-2014 [Abstract]
2013
Crisci JL, Poh YP, Mahajan S, Jensen JD, The impact of equilibrium assumptions on tests of selection., Front Genet, 01-01-2013 [Abstract]
SFS_CODE uses a coalescent-like population-scaled parameterization system, which is very confusing for users who are not familiar with the coalescent theory or coalescent simulators such as ms. For example,
Reply to Bo1. Coalescent-based simulations can scale parameters by effective population size because they assume Wright-Fisher random mating with equal census and effective population size. This is usually not the case for forward-time simulations because they can simulate more complex non-random mating schemes with unequal census and effective population size. Scaled parameters work for SFS_CODE now because it only simulates WF processes, but might not work for the addition of simple features such as varying number of offspring.
2. Whereas coalescent usually has a single Ne, forward-time simulations can have multiple changing population sizes (e.g. starting and ending population sizes) so it is not entirely clear which size should be used to scale the parameters. (The answer is to use the initial (small) population size which corresponds to ancestral effective population size.)
3. Effective population size is not a fixed quantity, which can decrease with bottlenecks and increase with population expansion. Specifying a parameter (e.g. 4*Ne*mu) with a varying quantity is confusing because a user might think mu will decrease with increasing Ne.
Anyway, it can be argued that Ne is not important for such simulations so any (reasonable) Ne can be used to scale parameters. If this is the case, why cannot we use unscaled parameters, which is more or less equivalent to the use of a fixed Ne?