ArticlePloS one2021
Emergence of co-expression in gene regulatory networks.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 73 citations in OpenAlex.
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- Insights into glandular trichome biology from analysis of organ-specific gene expression programmes in cannabis, hop and tomato.The Plant journal : for cell and molecular biology · 2026Article
- Targeted therapies reshape extracellular matrix remodeling and microenvironmental regulation in pediatric acute myeloid leukemia.Discover oncology · 2026Article
- Homophily within and across groups.Nature communications · 2025Article
- Organ-level gene-regulatory networks inferred from transcriptomic data reveal context-specific regulation and highlight novel regulators of ripening and ABA-mediated responses in tomato.Plant communications · 2025Article
- Role of PRC2 in the stochastic expression of Aire target genes and development of mimetic cells in the thymus.The Journal of experimental medicine · 2025Article
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- Differential Transcriptional Programs Reveal Modular Network Rearrangements Associated with Late-Onset Alzheimer's Disease.International journal of molecular sciences · 2025Article
- Article
- Molecular models of bidirectional promoter regulation.Current opinion in structural biology · 2024Review
- Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome.Genome biology · 2024Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- lncRNA-mRNA Co-Expression and Regulation Analysis in Lung Fibroblasts from Idiopathic Pulmonary Fibrosis.Non-coding RNA · 2024Article
- Sex Differences in Cochlear Transcriptomes in Horseshoe Bats.Animals : an open access journal from MDPI · 2024Article
- Complex regulatory networks influence pluripotent cell state transitions in human iPSCs.Nature communications · 2024Article
- Cell type evolution reconstruction across species through cell phylogenies of single-cell RNA sequencing data.Nature ecology & evolution · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes.International journal of molecular sciences · 2023Article
- Oncoinformatic screening of the gene clusters involved in the HER2-positive breast cancer formation along with the in silico pharmacodynamic profiling of selective long-chain omega-3 fatty acids as the metastatic antagonists.Molecular diversity · 2023Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcriptomes are known to organize themselves into gene co-expression clusters or modules where groups of genes display distinct patterns of coordinated or synchronous expression across independent biological samples. The functional significance of these co-expression clusters is suggested by the fact that highly coexpressed groups of genes tend to be enriched in genes involved in common functions and biological processes. While gene co-expression is widely assumed to reflect close regulatory proximity, the validity of this assumption remains unclear. Here we use a simple synthetic gene regulatory network (GRN) model and contrast the resulting co-expression structure produced by these networks with their known regulatory architecture and with the co-expression structure measured in available human expression data. Using randomization tests, we found that the levels of co-expression observed in simulated expression data were, just as with empirical data, significantly higher than expected by chance. When examining the source of correlated expression, we found that individual regulators, both in simulated and experimental data, fail, on average, to display correlated expression with their immediate targets. However, highly correlated gene pairs tend to share at least one common regulator, while most gene pairs sharing common regulators do not necessarily display correlated expression. Our results demonstrate that widespread co-expression naturally emerges in regulatory networks, and that it is a reliable and direct indicator of active co-regulation in a given cellular context.
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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.