SFS_CODE

Basic Package Attributes
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	6 years, 4 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, Dec. 1, 2008 [Abstract, cited in PMC ]
GSR Certification	✔Accessibility ✔Documentation ✔Application ✔Support
Last certified	Oct. 15, 2015 (1029 days ago)

Detailed Attributes
Attribute Category	Attribute
Target
Type of Simulated Data	Diploid DNA Sequence, Haploid DNA Sequence, Sex Chromosomes,
Sequencing Reads
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),
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, Feb. 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, July 1, 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, Aug. 8, 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, Oct. 1, 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, Jan. 2, 2014 [Abstract]
Wilson Sayres MA, Lohmueller KE, Nielsen R, Natural selection reduced diversity on human y chromosomes., PLoS Genet, Jan. 1, 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, July 1, 2014 [Abstract]
Sjöstrand AE, Sjödin P, Jakobsson M, Private haplotypes can reveal local adaptation., BMC Genet, May 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, Oct. 2, 2014 [Abstract]

2013

Crisci JL, Poh YP, Mahajan S, Jensen JD, The impact of equilibrium assumptions on tests of selection., Front Genet, Jan. 1, 2013 [Abstract]

Please take note of the commenting policy.

Bo Peng says: (4 years, 3 months ago)
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,

1. 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?

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