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SFS_CODE specifications

Information


Unique identifier OMICS_12972
Name SFS_CODE
Alternative name Simulating Finite Sites under COmplex Demographic Events
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages C, Python
License GNU General Public License version 3.0
Computer skills Advanced
Stability Stable
Maintained Yes

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Documentation


Publication for Simulating Finite Sites under COmplex Demographic Events

SFS_CODE citations

 (37)
library_books

Adaptation in a Fibronectin Binding Autolysin of Staphylococcus saprophyticus

2017
PMCID: 5705806
PMID: 29202045
DOI: 10.1128/mSphere.00511-17

[…] are known to confound the inference of bacterial demography (), so we used simulations to investigate their effects on our demographic inference performed for uropathogenic S. saprophyticus. We used SFS_CODE () to simulate positive selection (with a range of recombination rates) and to evaluate its effects on the accuracy of demographic inference with ∂a∂i. The method implemented in ∂a∂i relies o […]

library_books

Background selection as null hypothesis in population genomics: insights and challenges from Drosophila studies

2017
PMCID: 5698629
PMID: 29109230
DOI: 10.1098/rstb.2016.0471

[…] gion-specific temporal changes in Ne. Complementary studies could also take advantage of advances in optimizing forward simulations that can incorporate BGS and demographic events (e.g. SLiM [,,] and SFS_CODE [,]). Machine learning approaches to studying jointly demography, selection and linkage are similarly exciting avenues of research [–] and offer new opportunities to better evaluate the cause […]

library_books

A survey of methods and tools to detect recent and strong positive selection

2017
PMCID: 5385031
PMID: 28405579
DOI: 10.1186/s40709-017-0064-0

[…] , we have included execution results of SweepFinder, SweeD and SweepFinder2 using the average SFS instead of the regional SFS. We used Hudson’s ms for all simulations, whereas Crisci et al. have used sfs_code for the empirical simulated data. In general, our results are comparable to Crisci et al., but we report higher FPR than Crisci et al. A notable exception is the case of OmegaPlus in the seve […]

call_split

Genome wide standing variation facilitates long term response to bidirectional selection for antibody response in chickens

2017
BMC Genomics
PMCID: 5244587
PMID: 28100171
DOI: 10.1186/s12864-016-3414-7
call_split See protocol

[…] hereby genetic drift could be a prominent force affecting allele frequencies which would confound the identification of selective sweep signatures. Simulations were carried out for a 5 Mb locus using SFS_CODE [] taking into account mutation (the high mitochondrial mutation rate of 3.13 × 10-7 was implemented to promote high standing genetic variation in the ancestral population; []), recombination […]

call_split

MtDNA analysis reveals enriched pathogenic mutations in Tibetan highlanders

2016
Sci Rep
PMCID: 4976311
PMID: 27498855
DOI: 10.1038/srep31083
call_split See protocol

[…] synonymous and synonymous mutations were counted based on the branches of the phylogeny and the mutations on a branch were only counted once.According to the demographic models from BSPs, we employed SFS_CODE to conduct forward simulations with the effect of purifying selection. Generally, we simulated the protein coding regions with length of 11367 base pairs and sampled 300 individuals for each […]

library_books

The Impact of Selection, Gene Conversion, and Biased Sampling on the Assessment of Microbial Demography

2016
Mol Biol Evol
PMCID: 4915353
PMID: 26931140
DOI: 10.1093/molbev/msw048

[…] SFS were generated from random samples of 100 individuals. The mean SFS was calculated using 1,000 simulations. The exact ancestral state of each SNP was obtained using SFS_code. The SFS of the simulations were thus unfolded. For a better representation of the results, the SFS were transformed as follows. Let ξi denote the number of polymorphic sites at frequency in […]

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SFS_CODE institution(s)
Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, Institute for Quantitative Biosciences, University of California San Francisco, CA, USA
SFS_CODE funding source(s)
This work was supported by a grant from the National Institutes of Health (1R01HG007644) and a Sloan Foundation Research Fellowship.

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