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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
...of Mariculture Breeding, Xiamen University, Xiamen, Fujian 361000, China; 4Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision, Xiamen, Fujian 361005, China Corresponding author: wangying@xmu.edu.cnAbstractUnderstanding gene regulatory networks (GRNs) is crucial for deciphering cellular...
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Method: Spatial Cellular Networks from omics data with SpaCeNet
- Stefan Schrod,
- Niklas Lück,
- Robert Lohmayer,
- Stefan Solbrig,
- Dennis Völkl,
- Tina Wipfler,
- Katherine H. Shutta,
- Marouen Ben Guebila,
- Andreas Schäfer,
- Tim Beißbarth,
- Helena U. Zacharias,
- Peter J. Oefner,
- John Quackenbush,
- and Michael Altenbuchinger
Genome Res. September 2024 34: 1371-1383; Published in Advance September 4, 2024, doi:10.1101/gr.279125.124
...distribution, provide invaluable information about these mechanisms and the regulation of genes beyond processes occurring independently in each individual cell. “SpaCeNet” is a method designed to elucidate both the intracellular molecular networks (how molecular variables affect each other within the cell...
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Method: Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet
- Chuanyuan Wang and
- Zhi-Ping Liu
Genome Res. February 2025 35: 340-354; Published in Advance December 18, 2024, doi:10.1101/gr.279551.124
...of existing methodologies to decipher complex network structures. The architecture of GRNs, both globally or locally, is crucial in complex biological systems, revealing essential nodes (e.g., highly interconnected TFs) and regulatory modules and elucidating how GRNs adapt to intercellular variations...
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Research: Analyzing super-enhancer temporal dynamics reveals potential critical enhancers and their gene regulatory networks underlying skeletal muscle development
- Song Zhang,
- Chao Wang,
- Shenghua Qin,
- Choulin Chen,
- Yongzhou Bao,
- Yuanyuan Zhang,
- Lingna Xu,
- Qingyou Liu,
- Yunxiang Zhao,
- Kui Li,
- Zhonglin Tang,
- and Yuwen Liu
Genome Res. December 2024 34: 2190-2202; Published in Advance October 21, 2024, doi:10.1101/gr.278344.123
...2012). Understanding prenatal and postnatal PSM development is essential for elucidating the transcriptional regulatory networks that govern skeletal muscle growth and development.During skeletal muscle development, myogenic differentiation is driven by transcription factors (TFs) from the myogenic...
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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
...the phenotypes of interest (Lareau et al., 2015). Instead of focusing on high centrality genes within a single coexpression network, detecting genes with differential centrality between phenotypic groups can elucidate regulatory alterations. Savino et al. (2020) offer insights into the potential regulatory...
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Resource: Gene expression networks in the Drosophila Genetic Reference Panel
- Logan J. Everett,
- Wen Huang,
- Shanshan Zhou,
- Mary Anna Carbone,
- Richard F. Lyman,
- Gunjan H. Arya,
- Matthew S. Geisz,
- Junwu Ma,
- Fabio Morgante,
- Genevieve St. Armour,
- Lavanya Turlapati,
- Robert R.H. Anholt,
- and Trudy F.C. Mackay
Genome Res. March 2020 30: 485-496; Published in Advance March 6, 2020, doi:10.1101/gr.257592.119
...; Fagny et al. 2017). Elucidating such regulatory transcriptional networks will facilitate understanding how the effects of individual variants propagate through the network and how multiple variants together regulate gene expression and affect complex traits (Liu et al. 2008; Nicolae et al. 2010; Bryois...
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Method: Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease
- Jielin Xu,
- Pengyue Zhang,
- Yin Huang,
- Yadi Zhou,
- Yuan Hou,
- Lynn M. Bekris,
- Justin Lathia,
- Chien-Wei Chiang,
- Lang Li,
- Andrew A. Pieper,
- James B. Leverenz,
- Jeffrey Cummings,
- and Feixiong Cheng
Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
...'s disease (AD), we systematically identified molecular networks between DAM and DAA to uncover novel therapeutic targets for AD. Specifically, we develop a network-based methodology that leverages single-cell/nucleus RNA sequencing data from both transgenic mouse models and AD patient brains, as well...
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Research: Extensive cross-regulation of post-transcriptional regulatory networks in Drosophila
- Marcus H. Stoiber,
- Sara Olson,
- Gemma E. May,
- Michael O. Duff,
- Jan Manent,
- Robert Obar,
- K.G. Guruharsha,
- Peter J. Bickel,
- Spyros Artavanis-Tsakonas,
- James B. Brown,
- Brenton R. Graveley,
- and Susan E. Celniker
Genome Res. November 2015 25: 1692-1702; Published in Advance August 20, 2015, doi:10.1101/gr.182675.114
...reaction, an aspect of this study that is not possible in CLIP-seq or other crosslinking-dependent procedures. We utilize RIP to identify both the RNA and protein components of ribonuclear complexes. Analysis of the RNAs and proteins associated with these RBPs reveals a densely interconnected network...
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Method: Co-expression networks reveal the tissue-specific regulation of transcription and splicing
- Ashis Saha,
- Yungil Kim,
- Ariel D.H. Gewirtz,
- Brian Jo,
- Chuan Gao,
- Ian C. McDowell,
- The GTEx Consortium,
- Barbara E. Engelhardt,
- and Alexis Battle
Genome Res. November 2017 27: 1843-1858; Published in Advance October 11, 2017, doi:10.1101/gr.216721.116
...-expression networks are an essential framework for elucidating gene function and interactions, identifying sets of genes that respond in a coordinated way to environmental and disease conditions, and highlighting regulatory relationships (Penrod et al. 2011; Xiao et al. 2014; Yang et al. 2014). Each edge...
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Method: EnDeep4mC predicts DNA N⁴-methylcytosine sites using a dual-adaptive feature encoding framework in deep ensembles
- Shuyu Zhang,
- Quan Zou,
- Mengting Niu,
- Zhibin Lv,
- Antony Stalin,
- and Ximei Luo
Genome Res. March 2026 36: 589-599; Published in Advance February 17, 2026, doi:10.1101/gr.280977.125
...types and methylated base positions from bacteria using nanopore sequencing with multi-scale neural network. Bioinformatics 41: btaf397. doi:10.1093/bioinformatics/btaf397 ↵Cheng X. 1995. DNA modification by methyltransferases. Curr Opin Struct Biol 5: 4–10. doi:10.1016/0959-440x(95)80003-j ↵Deaton AM...
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