Literature DB >> 25395236

Reproducible simulations of realistic samples for next-generation sequencing studies using Variant Simulation Tools.

Bo Peng1.   

Abstract

Computer simulations have been widely used to validate and evaluate the power of statistical methods for genetic epidemiological studies. Although a large number of simulation methods and software packages have been developed for genome-wide association studies, methodological and bioinformatics challenges have limited their applications in simulating datasets for whole-genome and whole-exome sequencing studies. With the development of more sophisticated statistical methods that make fuller use of available data and our knowledge of the human genome, there is a pressing need for genetic simulators that capture more features of empirical data (e.g., multiallele variants, indels, use of the Variant Call Format) and the human genome (e.g., functional annotations of genetic variants). This article introduces Variant Simulation Tools (VST), a module of Variant Tools for the simulation of genetic variants for sequencing-based genetic epidemiological studies. Although multiple simulation engines are provided, the core of VST is a novel forward-time simulation engine that simulates real nucleotide sequences of the human genome using DNA mutation models, fine-scale recombination maps, and a selection model based on amino acid changes of translated protein sequences. The design of VST allows users to easily create and distribute simulation methods and simulated datasets for a variety of applications and encourages fair comparison between statistical methods through the use of existing or reproduced simulated datasets.
© 2014 WILEY PERIODICALS, INC.

Entities:  

Keywords:  genetic variants; next-gen sequencing; rare variant association analysis; reproducible study; simulation

Mesh:

Year:  2014        PMID: 25395236      PMCID: PMC6432799          DOI: 10.1002/gepi.21867

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  3 in total

1.  Guidelines for Large-Scale Sequence-Based Complex Trait Association Studies: Lessons Learned from the NHLBI Exome Sequencing Project.

Authors:  Paul L Auer; Alex P Reiner; Gao Wang; Hyun Min Kang; Goncalo R Abecasis; David Altshuler; Michael J Bamshad; Deborah A Nickerson; Russell P Tracy; Stephen S Rich; Suzanne M Leal
Journal:  Am J Hum Genet       Date:  2016-09-22       Impact factor: 11.025

2.  VIPdb, a genetic Variant Impact Predictor Database.

Authors:  Zhiqiang Hu; Changhua Yu; Mabel Furutsuki; Gaia Andreoletti; Melissa Ly; Roger Hoskins; Aashish N Adhikari; Steven E Brenner
Journal:  Hum Mutat       Date:  2019-08-17       Impact factor: 4.878

3.  Genetic data simulators and their applications: an overview.

Authors:  Bo Peng; Huann-Sheng Chen; Leah E Mechanic; Ben Racine; John Clarke; Elizabeth Gillanders; Eric J Feuer
Journal:  Genet Epidemiol       Date:  2014-12-13       Impact factor: 2.135

  3 in total

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