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iSG / indel-Seq-Gen
Simulates highly divergent DNA sequences and protein superfamilies. iSG is a method for simulating protein sequence evolution and generating realistic protein families. This is accomplished through the addition of subsequence length constraints and lineage- and site-specific evolution. iSG tracks insertion and deletion processes that occur during the simulation run. iSG records all evolutionary events and outputs the "true" multiple alignment of the sequences, and can generate a larger simulated sequence space by allowing the use of multiple related root sequences. iSG can be used to test the accuracy of multiple alignment methods, evolutionary hypotheses, ancestral protein reconstruction methods, and protein superfamily classification methods.
Provides statistical means to infer the strength of association between the rate of sequence evolution in a given genomic region and a phenotype of interest. TraitRateProp is a web server that allows testing the hypothesis of an association between the sequence evolutionary rate and the phenotypic trait. It then computes per-site predictions, where high scores indicate sequence sites whose rate is more likely to be in association with the phenotypic trait. In case where a 3D structure information of the protein is provided, the site-specific scores are mapped onto this structure, thus allowing the detection of putatively functional sequence sites that are spatially close to each other in 3D space.
RarePedSim / Rare-variant Pedigree-based Simulator
Simulates region/gene-level genotype and phenotype data for complex and Mendelian traits for any given pedigree structure. Using a genetic model, sequence variant data can be generated either conditionally or unconditionally on pedigree members' qualitative or quantitative phenotypes. Additionally, qualitative or quantitative traits can be generated conditional on variant data. Sequence data can either be simulated using realistic population demographic models or obtained from sequence-based studies. Variant sites can be annotated with positions, allele frequencies and functionality. For rare variants, RarePedSim is the only program that can efficiently generate both genotypes and phenotypes, regardless of pedigree structure. Data generated by RarePedSim are in standard linkage file (.ped) and variant call (.vcf) formats, ready to be used for a variety of purposes, including evaluation of type I error and power, for association methods including mixed models and linkage analysis methods.
SIMLA / SIMulation of Linkage and Association
Generates data sets of families for use in Linkage and Association studies. SIMLA allows the user flexibility in specifying marker and disease placement, locus heterogeneity, disequilibrium between markers and disease loci. It allows simulation of linkage and association for multiple markers in extended pedigrees, nuclear families or in sets of unrelated cases and controls. The program will be useful for studying and comparing existing statistical tests, for developing new genetic linkage and association statistics, planning sample sizes for new studies, and interpreting genetic analysis results.
Provides a flexible simulating tool for pathway-based genome-wide association studies using real genetic data from the HapMap project or users. Pathsimu can simultaneously simulate multiple quantitative phenotypes and genome-wide genotype data under users assigned parameters, such as simulation times, genetic models (additive & epistatic genetic models), names or sizes of causal pathways, numbers and genetic effects (main & interactive effects) of disease genes, minor allele frequency ranges of causal SNPs of disease genes. Pathsimu can be used to develop novel multiple gene association study approaches, for instance evaluating the impact of genetic parameters on the power of pathway-based association testing approaches, and comparing the performance of different approaches under various parameter settings. Pathsimu is designed to output data with PLINK file format and be easily extendable.
Simulates realistic samples for genome-wide association studies (GWAS). simuGWAS simulates populations that closely resemble the complex structure of the human genome, while allows the introduction of signals of natural selection. It can simulate realistic samples to evaluate the performance of a wide variety of statistical gene mapping methods for GWAS. Compared to other simulation methods, the tool simulates samples with existing genetic markers that resemble the human populations well in terms of marker allele frequency and linkage disequilibrium (LD) structure, with additional flexibility to simulate genomic regions with signals of natural selection.
A software tool to add a phenotype to genotypes generated in time-efficient coalescent simulations. Both qualitative and quantitative phenotypes can be generated and it is possible to partition phenotypic variation between additive effects and epistatic interactions between causal variants. The output formats of phenosim are directly usable as input for different GWAS tools. The applicability of phenosim is shown by simulating a genome-wide association study in Arabidopsis thaliana.
DHOEM / Densification of Haplotypes by lOEss and Maximum likelihood
A simulation software that does not belong to the three aforementioned approaches. DHOEM is a statistical procedure that simulates new markers, according to statistical modeling of local data characteristics, in real SNP marker data sets. The main objective of DHOEM is to increase the marker density in a marker data set for simulation studies. For each chromosome in a marker data set, the statistical procedure defined in DHOEM models the probability distribution generating the allele frequencies and the relation between LD and physical distance between consecutive markers. DHOEM is a user-friendly and useful tool for simulation and methodological studies in quantitative genetics and breeding. The software is written in R and runs on Windows operating systems (OS), although with a few modifications, it can be extended to Linux-like OS.
GAMETES / Genetic Architecture Model Emulator for Testing and Evaluating Software
Generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. It includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. The tool could be employed to pursue theoretical characterization of genetic models and epistasis.
Simulates phenotypes under different models including genetic variant effects and infinitesimal genetic effects, as well as correlated, non-genetic covariates and observational noise effects. PhenotypeSimulator is an R package that combines the phenotype components into a final phenotype while controlling for the proportion of variance explained by each of the components. Users can customize, for each effect component, the number of variables, their distribution and the design of their effect across traits.
ESPRESSO / Estimating Sample-size and Power in R by Exploring Simulated Study Outcomes
A simulation based tool, written in the R language that supports power and sample size calculations for stand-alone studies and analyses nested in cohort studies. ESPRESSO enables those designing large cohorts and biobanks to better estimate the sample size required to achieve adequate power. It can also be used to explore specific scientific questions relevant to the design and set up of large-scale association studies and biobanks.
Allows a considerable flexibility in disease models, potentially involving a large number of interacting loci. HAP-SAMPLE is a web application for simulating single nucleotide polymorphism (SNP) genotypes for case-control and affected-child trio studies by resampling from Phase I/II HapMap SNP data. It is a general simulation tool that is immediately available to the research community. The HapMap samples provide limited opportunity for specifying rare disease variants, because of selection bias in HapMap markers and sample size constraints.
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