PGsim: A Comprehensive and Highly Customizable Personal Genome Simulator

Juan, Liran; Wang, Yongtian; Jiang, Jingyi; Yang, Qi; Jiang, Qinghua; Wang, Yadong

doi:10.3389/fbioe.2020.00028

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…In the past, these methods have often focused on the simulation of an entire population and the coalescence of alleles within it (Balloux 2001; Tahmasbi and Keller 2016; Carvajal-Rodríguez 2008; Peng and Kimmel 2005). In recent years, teams have sought to use available data describing the frequency of mutations in populations and create novel samples by assigning these mutations in novel combinations (Juan et al 2020; Yue and Liti 2019; Mu et al 2015). One of the most recent projects of this type uses artificial intelligence methods, specifically generative approaches to determine appropriate combinations of mutations for sample generation (Yelmen et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Generating realistic artificial Human genomes using adversarial autoencoders

Burnard,

Mancheron,

Ritchie

2023

Preprint

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A publicly available human genome serves as both a valuable resource for researchers and a potential risk to the individual who provided the genome. Many actors with selfish intentions could exploit it to extract information about the donor’s health or that of their relatives. Recent efforts have employed artificial intelligence models to simulate genomic data, aiming to create synthetic datasets with scientific merit while preserving patient anonymity. However, a major challenge arises in dealing with the vast amount of data that constitutes a complete human genome and the resources required to process it.We have developed a dimension reduction method that combines artificial intelligence with our knowledge of in vivo mutation association mechanisms. This approach enables the processing of large amounts of data without significant computational resources. Our genome segmentation follows chromosomal recombination hotspots, closely resembling mutation transmission mechanisms. Training data is sourced from the 1000 Genomes Project, which catalogues over 2500 genomes from diverse ethnic groups. Variational autoencoders, utilising neural networks, serve as an extension to the generative model. Wasserstein Generative Adversarial Networks (WGAN) are a benchmark among generation methods for various data types.After optimisation of our data simulation strategy our pipeline allows the generation of a simulated population meeting several essential criteria. It demonstrates good diversity, closely resembling that found in the reference dataset. It is plausible, as newly generated combinations of mutations do not disrupt the linkage disequilibria found in humans. It also preserves donor anonymity by synthesising combinations of reference genomes that are distant from reference samples.

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Section: Introductionmentioning

confidence: 99%

Generating realistic artificial Human genomes using adversarial autoencoders

Burnard,

Mancheron,

Ritchie

2023

Preprint

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EpiGEN: an epistasis simulation pipeline

et al. 2020

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Summary Simulated data is crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes, or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns, and can generate both categorical and quantitative phenotypes. Availability and Implementation EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen. Supplementary information A detailed user guide for EpiGEN is available at Bioinformatics online.

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PGsim: A Comprehensive and Highly Customizable Personal Genome Simulator

Cited by 2 publications

References 23 publications

Generating realistic artificial Human genomes using adversarial autoencoders

Generating realistic artificial Human genomes using adversarial autoencoders

EpiGEN: an epistasis simulation pipeline

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