MOSim: bulk and single-cell multilayer regulatory network simulator

As multi-omics sequencing technologies advance, the need for simulation tools capable of generating realistic and diverse (bulk and single-cell) multi-omics datasets for method testing and benchmarking becomes increasingly important. We present MOSim, an R package that simulates both bulk (via mosim function) and single-cell (via sc_mosim function) multi-omics data. The mosim function generates bulk transcriptomics data (RNA-seq) and additional regulatory omics layers (ATAC-seq, miRNA-seq, ChIP-seq, Methyl-seq, and transcription factors), while sc_mosim simulates single-cell transcriptomics data (scRNA-seq) with scATAC-seq and transcription factors as regulatory layers. The tool supports various experimental designs, including simulation of gene co-expression patterns, biological replicates, and differential expression between conditions. MOSim enables users to generate quantification matrices for each simulated omics data type, capturing the heterogeneity and complexity of bulk and single-cell multi-omics datasets. Furthermore, MOSim provides differentially abundant features within each omics layer and elucidates the active regulatory relationships between regulatory omics and gene expression data at both bulk and single-cell levels. By leveraging MOSim, researchers will be able to generate realistic and customizable bulk and single-cell multi-omics datasets to benchmark and validate analytical methods specifically designed for the integrative analysis of diverse regulatory omics data.

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Publication Details

Journal
Briefings in Bioinformatics
Published
2025-03-01
DOI
https://doi.org/10.1093/bib/bbaf110
Citations
4
Primary Topic
Gene Regulatory Network Analysis
Type
article
Field-Weighted Citation Impact
3.22

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article

MOSim: bulk and single-cell multilayer regulatory network simulator

Ana Conesa, Ángeles Arzalluz-Luque, Maider Aguerralde‐Martin, Carolina Monzó et al.
4 citations
Briefings in Bioinformatics
Gene Regulatory Network Analysis
3.22
article

MOSim: bulk and single-cell multilayer regulatory network simulator

Ana Conesa, Ángeles Arzalluz-Luque, Maider Aguerralde‐Martin, Carolina Monzó, Carlos Martínez-Mira, Sonia Tarazona
article en
4 citations

Abstract

As multi-omics sequencing technologies advance, the need for simulation tools capable of generating realistic and diverse (bulk and single-cell) multi-omics datasets for method testing and benchmarking becomes increasingly important. We present MOSim, an R package that simulates both bulk (via mosim function) and single-cell (via sc_mosim function) multi-omics data. The mosim function generates bulk transcriptomics data (RNA-seq) and additional regulatory omics layers (ATAC-seq, miRNA-seq, ChIP-seq, Methyl-seq, and transcription factors), while sc_mosim simulates single-cell transcriptomics data (scRNA-seq) with scATAC-seq and transcription factors as regulatory layers. The tool supports various experimental designs, including simulation of gene co-expression patterns, biological replicates, and differential expression between conditions. MOSim enables users to generate quantification matrices for each simulated omics data type, capturing the heterogeneity and complexity of bulk and single-cell multi-omics datasets. Furthermore, MOSim provides differentially abundant features within each omics layer and elucidates the active regulatory relationships between regulatory omics and gene expression data at both bulk and single-cell levels. By leveraging MOSim, researchers will be able to generate realistic and customizable bulk and single-cell multi-omics datasets to benchmark and validate analytical methods specifically designed for the integrative analysis of diverse regulatory omics data.

Briefings in BioinformaticsVol. 26(2)
Universitat de València (ES), Biopolis (Spain) (ES), Institute for Integrative Systems Biology (ES), Universitat Politècnica de València (ES)
European Commission, Universitat de València, HORIZON EUROPE Framework Programme, Consejo Superior de Investigaciones Científicas, Instituto de Salud Carlos III, European Regional Development Fund
Openalex Percentile: Top 9%
Gene Regulatory Network Analysis
3.22
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