Searching journal content for articles similar to Hvidsten et al. 15 (6): 856.

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  1. Research: Functional genomics analysis of developing zebrafish and human endoderm reveals highly conserved cis-regulatory modules acting during vertebrate organogenesis
    • Daniela M. Riley,
    • Randa Elsayed,
    • Mark D. Walsh,
    • Simaran Johal,
    • Ying Lin,
    • Harry Walton,
    • Till Bretschneider,
    • Sascha Ott,
    • and Andrew C. Nelson
    Genome Res. April 2026 36: 695-712; Published in Advance March 4, 2026, doi:10.1101/gr.280838.125
    ...outcomes, there must also be consistent regulation of such genes by cis-regulatory modules (CRMs). CRMs consist of clusters of transcription factor binding sites (TFBSs) that regulate the expression of target genes through collective or competitive binding of operative transcription factors (TFs). However...
  2. Method: Iterative improvement of deep learning models using synthetic regulatory genomics
    • André M. Ribeiro-dos-Santos and
    • Matthew T. Maurano
    Genome Res. November 2025 35: 2539-2549; Published in Advance October 22, 2025, doi:10.1101/gr.280540.125
    ..., synthetic regulatory genomics is not limited to derivatization of the reference sequence but can deliver a nearly unlimited set of sequences.Here, we explore the application of deep learning models to predict regulatory features at loci engineered through synthetic regulatory genomics. We evaluate...
  3. Research: Genomic regulatory landscape underlying the antiviral response of Atlantic salmon
    • Shahmir Naseer,
    • Thomas C Clark,
    • Bertrand Collet,
    • Pooran Dewari,
    • Damir Baranasic,
    • Daniel J Macqueen,
    • Pierre Boudinot,
    • and Samuel A.M. Martin
    Genome Res. October 22, 2025 gr.280579.125; Published in Advance October 22, 2025, doi:10.1101/gr.280579.125
    ...provides evidence for conserved transcription factors 26 (TFs) driving the interferon response by binding ISG promoters, including IRF8, IRF9, 27 STAT1, and STAT2. Regulatory elements within differentially expressed genes were 28 enriched for predicted binding sites for STAT6, PRDM1, IRF6, JDP2, NR2E1...
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  4. 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. April 2026 36: 785-801; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
    ...the response or predictor dimension. When applied to TFs, each soft cluster represents a TF module, which is a group of TFs that act together to regulate a common transcriptional program. Aggregating TFs according to their learned module yields meta-TF–target CSNs with only a few meta-TFs.Signaling inference...
    OPEN ACCESS ARTICLE
  5. 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
    .... Nevertheless, although these methods successfully utilize prior data and infer cell type–specific GRNs, they tend to overlook chromatin-level regulatory mechanisms, such as chromatin accessibility (Kim et al. 2023).Chromatin accessibility highlights potential TF binding sites for target genes (Isbel et al...
  6. 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
    ...al. 2018; Tatulian 2022; Li et al. 2023). Given the magnitude of the problem, innovative strategies for discovering treatments for AD are essential.In this study, we develop a cell type–specific multiomic regulatory atlas to model AD progression and explore therapeutic opportunities. Using single...
    OPEN ACCESS ARTICLE
  7. Method: A scalable computational framework for predicting gene expression from candidate cis-regulatory elements
    • Qinhu Zhang,
    • Siguo Wang,
    • Zhipeng Li,
    • Wenzheng Bao,
    • Wenjian Liu,
    • and De-Shuang Huang
    Genome Res. February 2026 36: 361-374; Published in Advance January 16, 2026, doi:10.1101/gr.281219.125
    ...PGE accurately discovers some crucial TF motifs within prioritized cCREs and reveals the different regulatory types of these cCREs.Cell type–specific gene expression patterns are primarily determined through complex interactions between cis-regulatory elements (CREs) and transcriptional factors, playing...
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  8. Method: Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes
    • Rachel M. Petersen,
    • Christopher M. Vockley,
    • and Amanda J. Lea
    Genome Res. August 2025 35: 1781-1793; Published in Advance July 14, 2025, doi:10.1101/gr.279957.124
    ...elements is modulated by methylation, which is generally associated with decreased transcription. Within our set of regulatory elements, we use allele-specific expression analyses to identify 8020 sites with genetic effects on gene regulation; further, we find that 42.3% of these genetic effects vary...
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  9. 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
    ....ResultsOverview of scHGRTo summarize, we use a deep learning framework based on (HGNNs) to transfer labels from reference data sets to newly sequenced cells based on gene regulatory relationships, which are prevalent within the same species. As inputs, scRNA-seq expression profiles and cell-level labels are provided...
  10. Method: Incorporating valuable prior knowledge to improve deep learning prediction of genetic perturbation responses
    • Xiuhao Fu,
    • Chao Yang,
    • Chunyan Cui,
    • Aoyun Geng,
    • Yidi Sun,
    • Chao Zha,
    • Feifei Cui,
    • Leyi Wei,
    • Quan Zou,
    • Xin Gao,
    • and Zilong Zhang
    Genome Res. March 26, 2026 gr.281523.125; Published in Advance March 26, 2026, doi:10.1101/gr.281523.125
    ...perturbed genes. In parallel, 88 we incorporate a gene co-expression network to encode genes, allowing PRIM to 89 capture regulatory and expression-coupled relationships. Furthermore, by introducing 90 a cell encoding module based on control-cell expression profiles, PRIM learns 91 heterogeneous cellular...
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