Genetic Simulation Resources: a website for the registration and discovery of genetic data simulators

Bo Peng,Eric J Feuer,Elizabeth Gillanders,Huann-Sheng Chen,John Clarke,Leah E Mechanic,Ben Racine,Lauren Clarke

doi:10.1093/bioinformatics/btt094

Bo Peng, Eric J Feuer + Show 6 more

Open Access

https://doi.org/10.1093/bioinformatics/btt094

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Abstract

Summary: Many simulation methods and programs have been developed to simulate genetic data of the human genome. These data have been widely used, for example, to predict properties of populations retrospectively or prospectively according to mathematically intractable genetic models, and to assist the validation, statistical inference and power analysis of a variety of statistical models. However, owing to the differences in type of genetic data of interest, simulation methods, evolutionary features, input and output formats, terminologies and assumptions for different applications, choosing the right tool for a particular study can be a resource-intensive process that usually involves searching, downloading and testing many different simulation programs. Genetic Simulation Resources (GSR) is a website provided by the National Cancer Institute (NCI) that aims to help researchers compare and choose the appropriate simulation tools for their studies. This website allows authors of simulation software to register their applications and describe them with well-defined attributes, thus allowing site users to search and compare simulators according to specified features.Availability: http://popmodels.cancercontrol.cancer.gov/gsr.Contact: gsr@mail.nih.gov

Highlights

Owing to the cost and availability of genetic samples, lack of knowledge of causal variants that contribute to observed phenotypes and mathematical intractability of complex evolutionary models, computer simulations have been widely used, among many applications, to predict outcomes under realistic genetic scenarios (e.g. Peng and Kimmel, 2007), to compare and verify analytical methods or tools (e.g. Spencer et al, 2009) and to estimate parameters of evolutionary models (e.g. Peter et al, 2010)
We excluded simulators without an accessible web page or download link and those that are designed for teaching purposes and are limited in their ability to simulate usable genetic data
The Genetic Simulation Resources (GSR) website currently provides an interface to a catalogue of 80 registered packages (Fig. 1), with a global search box, a list view of all software resources and interfaces to rank packages according to selected attributes and compare attributes of selected packages

Summary

INTRODUCTION

Owing to the cost and availability of genetic samples, lack of knowledge of causal variants that contribute to observed phenotypes and mathematical intractability of complex evolutionary models, computer simulations have been widely used, among many applications, to predict outcomes under realistic genetic scenarios (e.g. Peng and Kimmel, 2007), to compare and verify analytical methods or tools (e.g. Spencer et al, 2009) and to estimate parameters of evolutionary models (e.g. Peter et al, 2010). Owing to the cost and availability of genetic samples, lack of knowledge of causal variants that contribute to observed phenotypes and mathematical intractability of complex evolutionary models, computer simulations have been widely used, among many applications, to predict outcomes under realistic genetic scenarios Peng and Kimmel, 2007), to compare and verify analytical methods or tools (e.g. Spencer et al, 2009) and to estimate parameters of evolutionary models (e.g. Peter et al, 2010). With increasing power of personal computers and the availability of computer clusters, novel simulation methods and sophisticated simulation programs have been and continue to be

METHODS

DISCUSSION