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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
...) introduces the concept of cis-regulatory potential to infer subpopulation-specific GRNs, achieving significantly higher accuracy compared with correlation-based methods. CellOracle (Kamimoto et al. 2023) enables GRN inference by integrating single-cell multi-omics data with prior regulatory knowledge...
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Resource: Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise
- Aliza B. Rubenstein,
- Gregory R. Smith,
- Zidong Zhang,
- Xi Chen,
- Toby L. Chambers,
- Frederique Ruf-Zamojski,
- Natalia Mendelev,
- Wan Sze Cheng,
- Michel Zamojski,
- Mary Anne S. Amper,
- Venugopalan D. Nair,
- Andrew R. Marderstein,
- Stephen B. Montgomery,
- Olga G. Troyanskaya,
- Elena Zaslavsky,
- Todd Trappe,
- Scott Trappe,
- and Stuart C. Sealfon
Genome Res. July 2025 35: 1664-1677; Published in Advance May 20, 2025, doi:10.1101/gr.280051.124
...)-expressing fibro-adipogenic progenitor cells. Single-cell regulatory circuit triad reconstruction (transcription factor, chromatin interaction site, regulated gene) also identifies largely distinct gene regulatory circuits modulated by exercise in the three muscle fiber types and LUM-expressing fibro...
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Method: Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies
- Jianing Yao,
- Jinglun Yu,
- Brian Caffo,
- Stephanie C. Page,
- Keri Martinowich,
- and Stephanie C. Hicks
Genome Res. July 2025 35: 1621-1632; Published in Advance May 20, 2025, doi:10.1101/gr.279380.124
...in Healthcare, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: shicks19@jhu.eduAbstractRecent advances in spatially resolved single-omic and multi-omics technologies have led to the emergence of computational tools to detect and predict spatial domains. Additionally, histological...
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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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Research: Single-cell multi-omics of human preimplantation embryos shows susceptibility to glucocorticoids
- Cheng Zhao,
- Savana Biondic,
- Katherine Vandal,
- Åsa K. Björklund,
- Michael Hagemann-Jensen,
- Theresa Maria Sommer,
- Jesica Canizo,
- Stephen Clark,
- Pascal Raymond,
- Daniel R. Zenklusen,
- Nicolas Rivron,
- Wolf Reik,
- and Sophie Petropoulos
Genome Res. September 2022 32: 1627-1641; Published in Advance August 10, 2022, doi:10.1101/gr.276665.122
...in dysregulation of metabolic pathways. We examined the direct effect of glucocorticoid exposure, low-dose dexamethasone (DEX), from embryonic day (E) 4 (before lineage specification) until E7 (blastocyst stage after mural–polar specification has initiated). On E7, we used a single-cell multi-omics approach...
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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...
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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 novel insights into the genomic regulatory landscape underlying antiviral 33 immunity in a farmed fish with a complex . 34 Introduction 35 The innate immune response to viral infection, which is mainly based on the type I interferon 36 (type I IFN) pathway is crucial to both disease progression...
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Method: A unified analysis of atlas single-cell data
- Hao Chen,
- Nam D. Nguyen,
- Matthew Ruffalo,
- and Ziv Bar-Joseph
Genome Res. May 2025 35: 1219-1233; Published in Advance February 18, 2025, doi:10.1101/gr.279631.124
..., Illinois 60607, USA; 3Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA Corresponding author: zivbj@cs.cmu.eduAbstractRecent efforts to generate atlas-scale single-cell data provide opportunities for joint analysis across tissues...
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Research: Regeneration alters open chromatin and cis-regulatory landscape of erythroid precursors
- Yichao Zhou,
- Venkatasai Rahul Dogiparthi,
- Hannah L. Harris,
- Suhita Ray,
- Avik Choudhuri,
- Song Yang,
- Yi Zhou,
- Leonard I. Zon,
- M. Jordan Rowley,
- and Kyle J. Hewitt
Genome Res. July 2025 35: 1518-1529; Published in Advance June 2, 2025, doi:10.1101/gr.279949.124
...-activating factors driving stress erythropoiesis. To infer AP-1-dependent activities in the transcriptome after PHZ treatment, we performed single-cell regulatory network inference and clustering (SCENIC) analysis. “Regulon” scores of JUN, FOS, and FOSB increased in HSPCs and erythroid clusters 3 and 4, specifically...
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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
..., highlighting the extensive gene interactions within organisms. Therefore, we propose scHGR, an automated annotation tool designed to leverage gene regulatory relationships in constructing gene-mediated cell communication graphs for single-cell transcriptome data. This strategy helps reduce noise from diverse...

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