Searching journal content for articles similar to Xue et al. 33 (9): 1609.

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  1. Method: Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN
    • Wenhao Zhang,
    • Lan Cao,
    • Xiaoxuan Gu,
    • Yongyu Long,
    • and Ying Wang
    Genome Res. January 2026 36: 142-158; Published in Advance October 9, 2025, doi:10.1101/gr.280757.125
    ...to comprehensively map GRNs (Badia-i-Mompel et al. 2023). High-throughput RNA sequencing (RNA-seq), which enables -wide analysis of the cellular transcriptome on bulk cells (Ozsolak and Milos 2011), has revolutionized GRN inference by enabling the computational derivation of regulatory networks from gene expression...
  2. Method: Deciphering context-specific gene programs from single-cell and spatial transcriptomics data with DeCEP
    • Lin Li,
    • Xianbin Su,
    • and Ze-Guang Han
    Genome Res. October 2025 35: 2300-2315; Published in Advance August 21, 2025, doi:10.1101/gr.279689.124
    .... Recent advancements in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies have empowered the comprehensive characterization of gene programs at both single-cell and spatial resolutions. Here, we present DeCEP, a computational framework designed to characterize context...
  3. Resource: Common and specific gene regulatory programs in zebrafish caudal fin regeneration at single-cell resolution
    • Yujie Chen,
    • Yiran Hou,
    • Qinglin Zeng,
    • Irene Wang,
    • Meiru Shang,
    • Kwangdeok Shin,
    • Christopher Hemauer,
    • Xiaoyun Xing,
    • Junsu Kang,
    • Guoyan Zhao,
    • and Ting Wang
    Genome Res. January 2025 35: 202-218; Published in Advance January 14, 2025, doi:10.1101/gr.279372.124
    ..., which is characterized by several waves of gene upregulation and downregulation. We identified and in vivo validated cell-type-specific and position-specific regeneration-responsive enhancers and constructed regulatory networks by cell type and stage. Our single-cell resolution transcriptomic...
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  4. Method: A gene regulatory network–aware graph learning method for cell identity annotation in single-cell RNA-seq data
    • Mengyuan Zhao,
    • Jiawei Li,
    • Xiaoyi Liu,
    • Ke Ma,
    • Jijun Tang,
    • and Fei Guo
    Genome Res. July 2024 34: 1036-1051; Published in Advance August 12, 2024, doi:10.1101/gr.278439.123
    ...an automated annotation tool for single-cell transcriptome data, named single-cell hybrid graph neural network with gene regulations (scHGR). Leveraging large-scale experimentally validated gene regulatory relationships from databases, scHGR employs a graph representation algorithm to assemble correlations...
  5. Resource: Cell type–specific gene regulatory atlas prioritizes drug targets and repurposable medicines in Alzheimer's disease
    • Yunxiao Ren,
    • Ming Hu,
    • Yang E. Li,
    • Andrew A. Pieper,
    • Jeffrey Cummings,
    • and Feixiong Cheng
    Genome Res. March 2026 36: 645-659; Published in Advance January 21, 2026, doi:10.1101/gr.280436.125
    ...–specific cis-regulatory DNA elements (CREs), we pinpoint 141 ADNC-associated genes. Using gene set enrichment analysis (GSEA) and network proximity analysis, we further identify nine candidate repurposable drugs that were associated with these ADNC-related genes. In summary, this cell type–specific multiomic...
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  6. Method: TFcomb identifies transcription factor combinations for cellular reprogramming based on single-cell multiomics data
    • Chen Li,
    • Sijie Chen,
    • Yixin Chen,
    • Haiyang Bian,
    • Minsheng Hao,
    • Lei Wei,
    • and Xuegong Zhang
    Genome Res. June 2025 35: 1429-1439; Published in Advance April 10, 2025, doi:10.1101/gr.279955.124
    ...of finding reprogramming TFs and their combinations as an inverse problem, and used Tikhonov regularization to guarantee the generalization ability of solutions. For the coefficient matrix of the model, we designed a graph attention network to augment gene regulatory networks built with single-cell RNA...
  7. Method: Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE
    • Yingxin Lin,
    • Tung-Yu Wu,
    • Xi Chen,
    • Sheng Wan,
    • Brian Chao,
    • Jingxue Xin,
    • Jean Y.H. Yang,
    • Wing H. Wong,
    • and Y.X. Rachel Wang
    Genome Res. January 2024 34: 119-133; Published in Advance December 15, 2023, doi:10.1101/gr.277960.123
    .... Corresponding authors: rachel.wang@sydney.edu.au, whwong@stanford.eduAbstractSingle-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and sc...
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  8. Method: Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics
    • Vishvak Raghavan,
    • Yumin Zheng,
    • Yue Li,
    • and Jun Ding
    Genome Res. July 2025 35: 1646-1663; Published in Advance May 2, 2025, doi:10.1101/gr.279771.124
    ...(Fig. 2A, top). Notably, CellAgentChat showed significantly better results than CellPhoneDB, NICHES, SingleCellSignalR, NATMI, and scSeqComm, further underscoring its consistency and accuracy in inferring communication networks (Fig. 2A, top). On examining individual data sets, Cell...
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  9. 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
    .... The direction of the arrows indicates the inferred regulatory direction between modules. (D) The case of cross-tissue regulatory network between Liver_ME1 and Shell gland_ME4. The red and blue dots stand for genes in Liver_ME1 and shell gland_ME4, respectively. The size of each point reflects the cross...
  10. 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
    ...regulatory genes (Narang et al. 2015), determine changes in regulatory mechanisms that are key to cellular identity (Wang et al. 2021), and prioritize genes that drive phenotypic variability (van Dam et al. 2017).Despite the utility of GCNs in understanding biological systems, network inference is still...
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