Searching journal content for articles similar to Nayak et al. 19 (11): 1953.

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  1. Research: Mapping multitissue regulatory variants reveals a liver-centric coexpression network associated with duck egg-laying performance
    • Yang Xi,
    • Jingjing Qi,
    • Zhao Yang,
    • Yutian Zeng,
    • Huicong Zhang,
    • Qiuyu Tao,
    • Mengru Xu,
    • Anqi Huang,
    • Shenqiang Hu,
    • Chunchun Han,
    • Lili Bai,
    • Jiwei Hu,
    • Jiwen Wang,
    • Liang Li,
    • Lingzhao Fang,
    • and Hehe Liu
    Genome Res. October 2025 35: 2211-2225; Published in Advance September 10, 2025, doi:10.1101/gr.280345.124
    ..., we performed weighted gene coexpression network analysis (WGCNA) across four tissues (Supplemental Fig. S8A). The analysis revealed that LOC101800576 and LOC101790890 were assigned to the shell gland ME3 module, GLP2R to the spleen ME7 module, and LOC119713219 to the ovary ME6 module (Supplemental...
  2. Method: Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet
    • Yidi Deng,
    • Jiadong Mao,
    • Jarny Choi,
    • and Kim-Anh Lê Cao
    Genome Res. March 5, 2026 ; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
    ...–TF–target) inferred based on the contextualized TF–target interactions. Cell-specific coexpression networks Genome Research 3 www..org C D E F G H B2 B3 B4 A2 A3 Coexpression Cell type Figure 2. Coexpression between transcriptional factors and targets provides robust evidence to active gene regulation. (A) (A1...
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  3. Method: Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks
    • Prashanthi Ravichandran,
    • Princy Parsana,
    • Rebecca Keener,
    • Kasper D. Hansen,
    • and Alexis Battle
    Genome Res. September 2025 35: 2087-2103; Published in Advance July 17, 2025, doi:10.1101/gr.280808.125
    ...of genes examined, a potential solution to increase statistical power by reducing network complexity has been to utilize methods such as WGCNA that infer modules or groups of coexpressed genes that are regulated by one or more transcription factors rather than individual gene interactions (Segal et al...
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  4. Method: Bayesian inference of sample-specific coexpression networks
    • Enakshi Saha,
    • Viola Fanfani,
    • Panagiotis Mandros,
    • Marouen Ben Guebila,
    • Jonas Fischer,
    • Katherine H. Shutta,
    • Dawn L. DeMeo,
    • Camila M. Lopes-Ramos,
    • and John Quackenbush
    Genome Res. September 2024 34: 1397-1410; Published in Advance August 12, 2024, doi:10.1101/gr.279117.124
    ...equally to this work. Corresponding author: johnq@hsph.harvard.eduAbstractGene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression...
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  5. Research: Developmental transcriptomics in Pristionchus reveals the environmental responsiveness of a plasticity gene-regulatory network
    • Shelley Reich,
    • Tobias Loschko,
    • Julie Jung,
    • Samantha Nestel,
    • Ralf J. Sommer,
    • and Michael S. Werner
    Genome Res. July 2025 35: 1560-1573; Published in Advance June 9, 2025, doi:10.1101/gr.279783.124
    .... 2023) and found copy-number variation of cis-regulatory elements underlying differences in eud-1 expression and mouth form. However, outside of this case study, it is unknown which nodes of the network are being acted on by evolution. To expand upon these findings, we compared gene expression between...
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  6. Method: Modeling gene interactions in polygenic prediction via geometric deep learning
    • Han Li,
    • Jianyang Zeng,
    • Michael P. Snyder,
    • and Sai Zhang
    Genome Res. January 2025 35: 178-187; Published in Advance November 19, 2024, doi:10.1101/gr.279694.124
    ....View larger version: In this window In a new window Figure 1. An illustrative diagram of PRS-Net. (A) The proposed framework is built upon GWAS summary statistics, including variants, risk alleles, P-values, and effect sizes. (B) A gene–gene interaction (GGI) network is constructed based on the protein...
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  7. Research: Landscape of microRNA and target expression variation and covariation in single mouse embryonic stem cells
    • Marcel Tarbier,
    • Sebastian D. Mackowiak,
    • Vaishnovi Sekar,
    • Franziska Bonath,
    • Etka Yapar,
    • Bastian Fromm,
    • Omid R. Faridani,
    • Inna Biryukova,
    • and Marc R. Friedländer
    Genome Res. February 2026 36: 291-302; Published in Advance January 12, 2026, doi:10.1101/gr.279914.124
    ...buffer gene expression variation at the protein level (Schmiedel et al. 2015). Evidence from miRNA overexpression studies indicate that miRNAs may increase variation at the RNA level (Gambardella et al. 2017; Rzepiela et al. 2018); however, transcriptome-wide evidence from natural cell conditions...
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  8. Method: Analysis of coding gene expression from small RNA sequencing
    • Aygun Azadova,
    • Anthonia Ekperuoh,
    • Greg N. Brooke,
    • and Antonio Marco
    Genome Res. March 2026 36: 611-618; Published in Advance February 10, 2026, doi:10.1101/gr.281364.125
    ...of miRNAs and compared the expression profile between all miRNA/coding gene pairs from the same sRNA-seq. Then, we identified those pairs with known interactions previously described in the literature as compiled in miRTarBase (see Methods). In this analysis, we used all the samples available for small...
  9. Method: Debiased personalized gene coexpression networks for population-scale scRNA-seq data
    • Shan Lu and
    • Sunduz Keles
    Genome Res. June 9, 2023 gr.277363.122; Published in Advance June 9, 2023, doi:10.1101/gr.277363.122
    ...QTL) across different cell types and in dynamic processes (Van Der Wijst et al., 2018; Soskic et al., 2022). A key opportunity unveiled by emerging scRNA-seq datasets is the construction of personalized gene coexpression networks which can be leveraged to link network-level properties to phenotypic variation...
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  10. Resource: Multiscale network modeling reveals the gene regulatory landscape driving cancer prognosis in 32 cancer types
    • Peng Xu and
    • Bin Zhang
    Genome Res. October 2023 33: 1806-1817; Published in Advance October 31, 2023, doi:10.1101/gr.278063.123
    ....zhang@mssm.eduAbstractCancer is a complex disease with diverse molecular mechanisms that affect patient prognosis. Network-based approaches are effective in revealing a holistic picture of cancer prognosis and gene interactions. However, a comprehensive landscape of coexpression networks and prognostic gene modules across multiple cancer...
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