Evidence map›Paper›PMID 40116657›Full record

ArticleBriefings in bioinformatics2025

MOSim: bulk and single-cell multilayer regulatory network simulator.

Carolina Monzó, Maider Aguerralde-Martin, Carlos Martínez-Mira, Ángeles Arzalluz-Luque, Ana Conesa, Sonia Tarazona

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Carolina MonzóGenomics of Gene Expression Lab, Institute for Integrative Systems Biology, Spanish National Research Council (CSIC-UV), C/ Catedràtic Agustín Escardino Benlloch, Paterna 46980, Spain.
Maider Aguerralde-MartinApplied Statistics, Operational Research and Quality Department, Universitat Politècnica de València, Camí de Vera s/n, València 46022, Spain.
Carlos Martínez-MiraBiobam Bioinformatics S.L., Marina de Valencia Base 5, BioHub, C/ de la Travesía, s/n, Sector Puerto 14 E, València 46024, Spain.
Ángeles Arzalluz-LuqueGenomics of Gene Expression Lab, Institute for Integrative Systems Biology, Spanish National Research Council (CSIC-UV), C/ Catedràtic Agustín Escardino Benlloch, Paterna 46980, Spain.
Ana ConesaGenomics of Gene Expression Lab, Institute for Integrative Systems Biology, Spanish National Research Council (CSIC-UV), C/ Catedràtic Agustín Escardino Benlloch, Paterna 46980, Spain.
Sonia TarazonaApplied Statistics, Operational Research and Quality Department, Universitat Politècnica de València, Camí de Vera s/n, València 46022, Spain.ORCID 0000-0001-5346-1407

Funding

Instituto de Salud Carlos III AC22/00058Scientific Foundation of the Spanish Association Against Cancer PERME224336TARA
6 · The paper itself

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.

Indexed as

Computational BiologyGene Regulatory NetworksSingle-Cell AnalysisSoftwareComputer SimulationGene Expression ProfilingHumansTranscriptomebulkmulti-omic simulatorsingle celltranscriptomics

Identifiers

PMID40116657
PMCPMC11926980

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.