ArticleBMC genomics2009
Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells.
Article in BMC genomics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
140 citing papers in PubMed, 1 synthesis or guideline pooled it, 269 citations in OpenAlex.
- Meta-analysis of drought-tolerant genotypes in Oryza sativa: A network-based approach.PloS one · 2019Pooled it
- Transcriptomic adaptation during skeletal muscle habituation to eccentric or concentric exercise training.Scientific reports · 2021Trial
- Comparative Transcriptomic Analysis Reveals Sugar Metabolism and Accumulation Mechanisms inLife (Basel, Switzerland) · 2026Article
- Identification of targets for high penicillin G production in an industrial strain of Penicillium rubens.Applied microbiology and biotechnology · 2026Article
- Informed spatially aware patterns for multiplexed immunofluorescence data.Scientific reports · 2026Article
- Identification of two genes associated with recurrence in Paget's disease and construction of a predictive model.Frontiers in genetics · 2026Article
- Warmer temperature accelerates senescence by modifying the aging-dependent changes in the mosquito transcriptome, altering immunity, metabolism, and DNA repair.Immunity & ageing : I & A · 2025Article
- Co-expression network analysis identifies AtrANS, a gene essential for anthocyanin pigmentation in Akebia trifoliata pericarp.BMC plant biology · 2025Article
- Decoding mechanosensitive genes in cardiac fibroblasts via 3D hydrogel models of fibrosis.Scientific reports · 2025Article
- DiffCoRank: a comprehensive framework for discovering hub genes and differential gene co-expression in brain implant-associated tissue responses.BMC bioinformatics · 2025Article
- ACT2.6: Global Gene Coexpression Network inGenes · 2025Article
- Analysis of key lncRNA related to Parkinson's disease based on gene co-expression weight networks.Neurosciences (Riyadh, Saudi Arabia) · 2025Article
- Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning.Frontiers in oncology · 2025Article
- A twin analysis to estimate genetic and environmental factors contributing to variation in weighted gene co-expression network module eigengenes.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025Article
- Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections.Cancers · 2024Article
- Reassessing the modularity of gene co-expression networks using the Stochastic Block Model.PLoS computational biology · 2024Article
- Reassessing the modularity of gene co-expression networks using the Stochastic Block Model.bioRxiv : the preprint server for biology · 2024Article
- Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections.bioRxiv : the preprint server for biology · 2024Article
- Transcriptome organization of white blood cells through gene co-expression network analysis in a large RNA-seq dataset.Frontiers in immunology · 2024Article
- Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning.Frontiers in microbiology · 2024Article
80 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundRecent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes.
resultsWe show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epigenetic modifications (histone modifications and promoter CpG methylation status), which are known to play a role in controlling gene expression during ES cell self-renewal and differentiation.
conclusionOur systems biologic re-analysis of gene expression, transcription factor binding, epigenetic and gene ontology data provides a novel integrative view of ES cell biology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.