MeSCoT: the tool for quantitative trait simulation through the mechanistic modeling of genes’ regulatory interactions

Milkevych, Viktor; Karaman, Emre; Sahana, Goutam; Janss, Luc; Cai, Zexi; Lund, Mogens Sandø

doi:10.1093/g3journal/jkab133

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…Splatter implements six different simulation models ranging from a simple negative Binomial model to a more sophisticated gamma-Poisson hierarchical model, however, it assumes no correlation in expression among different genes. Finally, MeSCoT was released recently which is a synthetic data generator developed in MATLAB for the detailed simulation of genes' regulatory interactions for variable genomic architectures which can also produce a complete set of transcriptional and translational data together with simulated quantitative trait values [41]. So, while there are several in silico methods available for simulating gene expression data, currently no method produces synthetic scRNA-seq data with realistic expression statistics as expected by stochastic gene expression and scRNA-seq protocols.…”

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Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

2022

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Background Single cell RNA-sequencing (scRNA-seq) has very rapidly become the new workhorse of modern biology providing an unprecedented global view on cellular diversity and heterogeneity. In particular, the structure of gene-gene expression correlation contains information on the underlying gene regulatory networks. However, interpretation of scRNA-seq data is challenging due to specific experimental error and biases that are unique to this kind of data including drop-out (or technical zeros). Methods To deal with this problem several methods for imputation of zeros for scRNA-seq have been developed. However, it is not clear how these processing steps affect inference of genetic networks from single cell data. Here, we introduce Biomodelling.jl, a tool for generation of synthetic scRNA-seq data using multiscale modelling of stochastic gene regulatory networks in growing and dividing cells. Results Our tool produces realistic transcription data with a known ground truth network topology that can be used to benchmark different approaches for gene regulatory network inference. Using this tool we investigate the impact of different imputation methods on the performance of several network inference algorithms. Conclusions Biomodelling.jl provides a versatile and useful tool for future development and benchmarking of network inference approaches using scRNA-seq data.

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Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

2022

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Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

Lasri

Shahrezaei

Sturrock

2021

Preprint

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Single cell RNA-sequencing (scRNA-seq) has very rapidly become the new workhorse of modern biology providing an unprecedented global view on cellular diversity and heterogeneity. In particular, the structure of gene-gene expression correlation contains information on the underlying gene regulatory networks. However, interpretation of scRNA-seq data is challenging due to specific experimental error and biases that are unique to this kind of data including drop-out (or technical zeros). To deal with this problem several methods for imputation of zeros for scRNA-seq have been developed. However, it is not clear how these processing steps affect inference of genetic networks from single cell data. Here, we introduce Biomodelling.jl, a tool for generation of synthetic scRNA-seq data using multiscale modelling of stochastic gene regulatory networks in growing and dividing cells. Our tool produces realistic transcription data with a known ground truth network topology that can be used to benchmark different approaches for gene regulatory network inference. Using this tool we investigate the impact of different imputation methods on the performance of several network inference algorithms. Biomodelling.jl provides a versatile and useful tool for future development and benchmarking of network inference approaches using scRNA-seq data.

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351. Quantitative trait simulation using MeSCoT software

Milkevych

Karaman

Sahana

et al. 2022

Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)

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This work represents MeSCoT, a software dedicated to complex trait simulation through the mechanistic modelling of genes interactions. The software provides an innovative framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions. It closely imitates the basic in vivo mechanisms of complex trait realization and allows a detailed simulation of genes' regulatory interactions for variable genomic architectures. The MeSCoT software is scalable and well documented (https://genetics.ghpc.au.dk/vimi/mescot/).

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MeSCoT: the tool for quantitative trait simulation through the mechanistic modeling of genes’ regulatory interactions

Cited by 3 publications

References 47 publications

Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

351. Quantitative trait simulation using MeSCoT software

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