Searching journal content for articles similar to Wagner 12 (2): 309.

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  1. Mini-Review: Revisiting models of enhancer–promoter communication in gene regulation
    • Gilad Barshad and
    • Charles G. Danko
    Genome Res. June 2025 35: 1277-1286; doi:10.1101/gr.278389.123
    ...in the expression of a target gene after enhancer–promoter loop formation (Kim et al. 2019), indicating that loops likely cause increased target gene expression.Such perturbation experiments, combined with newer and higher-resolution 3C variants, have shown that functionally active enhancer–promoter pairs have...
  2. Research: Oxygen-induced stress reveals context-specific gene regulatory effects in human brain organoids
    • Benjamin D. Umans and
    • Yoav Gilad
    Genome Res. August 2025 35: 1689-1700; Published in Advance June 2, 2025, doi:10.1101/gr.280219.124
    ...and Figure 1. (B) Enrichment of gene annotations among differentially expressed genes across cell types and conditions. The 39 MSigDB Hallmark gene sets with at least one significant enrichment are shown. (C) Fractional change in hypoxia-stressed fraction of each cell type after hypoxia exposure (coarse cell...
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  3. Method: Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets
    • Alexander Morin,
    • Eric Ching-Pan Chu,
    • Aman Sharma,
    • Alex Adrian-Hamazaki,
    • and Paul Pavlidis
    Genome Res. May 2023 33: 763-778; Published in Advance June 12, 2023, doi:10.1101/gr.277273.122
    ...IP-seq of TRs and experiments that perturb a TR and measure the differential abundance of gene transcripts are a primary means by which direct relationships are tested on a genomic scale. It has been reported that there is a poor overlap in the evidence across gene regulation strategies, emphasizing the need...
  4. Resource: Large-scale analysis of branchpoint usage across species and cell lines
    • Allison J. Taggart,
    • Chien-Ling Lin,
    • Barsha Shrestha,
    • Claire Heintzelman,
    • Seongwon Kim,
    • and William G. Fairbrother
    Genome Res. April 2017 27: 639-649; Published in Advance January 24, 2017, doi:10.1101/gr.202820.115
    ...University, Providence, Rhode Island 02912, USA; 3Hassenfeld Child Health Innovation Institute of Brown University, Providence, Rhode Island 02912, USA The coding sequence of each human pre-mRNA is interrupted, on average, by 11 introns that must be spliced out for proper gene expression. Each intron...
  5. 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
    ...) and introducing the key terminology used in NNet analysis. Coexpression network NNet estimates coexpression at the level of individual cells by combining dimensionality reduction with local regression. To measure cell-specific gene coexpression, NNet first uses principal component analysis (PCA) to embed gene...
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  6. Method: Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method
    • Quanyou Cai,
    • Lihui Lin,
    • Xin Liu,
    • and Jiekai Chen
    Genome Res. November 2025 35: 2513-2526; Published in Advance October 22, 2025, doi:10.1101/gr.280532.125
    ...in speed, and it is the only approach capable of integrating data sets containing millions of cells in a single run. As a result, Polyomino uncovers originally hidden gene expression patterns in brain sections and offers new insights into organogenesis and tumor microenvironments, all with exceptional...
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  7. Method: Quantifying pathological progression from single-cell transcriptomic data with scPSS
    • Samin Rahman Khan,
    • M Saifur Rahman,
    • M. Sohel Rahman,
    • and Md Abul Hassan Samee
    Genome Res. February 2026 36: 375-386; Published in Advance January 14, 2026, doi:10.1101/gr.280411.125
    ...-cell annotations. HiDDEN projects cells into a lower-dimensional space and trains a classifier on sample-level labels to produce per-cell perturbation scores, which are then clustered into affected and unaffected groups. MultiMIL also transforms the gene expressions into a lower dimension and then uses attention...
  8. Research: Spatial organization of chromosomes leads to heterogeneous chromatin motion and drives the liquid- or gel-like dynamical behavior of chromatin
    • Hossein Salari,
    • Marco Di Stefano,
    • and Daniel Jost
    Genome Res. January 2022 32: 28-43; Published in Advance December 28, 2021, doi:10.1101/gr.275827.121
    ...fundamental processes such as gene transcription and DNA repair. Experiments unveiled that chromatin motion is highly heterogeneous inside cell nuclei, ranging from a liquid-like, mobile state to a gel-like, rigid regime. Using polymer modeling, we investigate how these different physical states and dynamical...
  9. Method: Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching
    • Mengjie Chen,
    • Qi Zhan,
    • Zepeng Mu,
    • Lili Wang,
    • Zhaohui Zheng,
    • Jinlin Miao,
    • Ping Zhu,
    • and Yang I. Li
    Genome Res. April 2021 31: 698-712; Published in Advance March 19, 2021, doi:10.1101/gr.261115.120
    ...). Next, an external gene expression panel is used to identify anchor cells to estimate linear batch effects in the form of a rotation and a translation in PC space. Last, the data are corrected by rotating and translating the data points in PC space. The PC loadings are used to recover the aligned data...
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  10. Research: High-throughput allele-specific expression across 250 environmental conditions
    • Gregory A. Moyerbrailean,
    • Allison L. Richards,
    • Daniel Kurtz,
    • Cynthia A. Kalita,
    • Gordon O. Davis,
    • Chris T. Harvey,
    • Adnan Alazizi,
    • Donovan Watza,
    • Yoram Sorokin,
    • Nancy Hauff,
    • Xiang Zhou,
    • Xiaoquan Wen,
    • Roger Pique-Regi,
    • and Francesca Luca
    Genome Res. December 2016 26: 1627-1638; Published in Advance October 19, 2016, doi:10.1101/gr.209759.116
    ...response from the shallow sequencing data similarly have a small response when deep sequenced Colors next to treatment names represent treatments chosen for deep sequencing Gray indicates treatments that were not deep sequenced C Global coexpression network inferred using weighted gene correlation...
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