Searching journal content for articles similar to Lemberg and Freeman 17 (11): 000.

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  1. Research: ACTB methylation regulates SMARCA4 genomic occupancy to promote translation and reduce adhesion in colorectal cancer cells
    • Elina Abaev-Schneiderman,
    • Linh Nguyen,
    • Raz Shalev,
    • Tzofit Elbaz Biton,
    • Anand Chopra,
    • Giritharan Jagadeesan,
    • Daniel Sevilla-Sanchez,
    • Nili Tickotsky Moskovitz,
    • Liron Levin,
    • Michal Feldman,
    • Capucine Van Rechem,
    • and Dan Levy
    Genome Res. April 2026 36: 684-694; Published in Advance March 25, 2026, doi:10.1101/gr.280534.125
    ...polymerization and smooth muscle contraction. Here, we show that the genomic distribution of ACTB is SETD3-dependent and that this regulation modulates the transcription of genes involved in cell adhesion and mRNA translation in colorectal cancer cells. Proteomic analyses reveal that ACTB and SETD3 interact...
  2. 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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  3. Method: Autoencoders for genomic variation analysis
    • Margarita Geleta,
    • Daniel Mas Montserrat,
    • Xavier Giro-i-Nieto,
    • and Alexander G. Ioannidis
    Genome Res. February 2026 36: 348-360; Published in Advance January 20, 2026, doi:10.1101/gr.280086.124
    ..., Barcelona 08034, Spain; 3Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, California 94720, USA; 4Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA; 5Institute for Computational and Mathematical Engineering...
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  4. Method: Automated interpretable artificial intelligence genomic prediction with AIGP
    • Lei Wei,
    • Ziqin Jiang,
    • Baoliang Fan,
    • Yidan Yan,
    • Zhenqiang Xu,
    • Xiaoxiang Hu,
    • and Yuzhe Wang
    Genome Res. April 2026 36: 814-826; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
    ....edu.cnAbstractPredicting phenotypes from genomic mutations remains a major genetic challenge. Traditional statistical methods (such as GBLUP and BayesR) have limitations, including reliance on artificial prior assumptions, and hard to capture epistatic effects. Machine learning (ML) has emerged as a powerful alternative for genomic...
  5. Research: Adaptation of centromeres to breakage through local genomic and epigenomic remodeling in wheat
    • Jingwei Zhou,
    • Yuhong Huang,
    • Huan Ma,
    • Yiqian Chen,
    • Chuanye Chen,
    • Fangpu Han,
    • and Handong Su
    Genome Res. November 2025 35: 2461-2471; Published in Advance September 30, 2025, doi:10.1101/gr.280913.125
    ...Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; 3State Key Laboratory of Seed Innovation, Institute of Genetics and Developmental Biology, Chinese Academy...
  6. Method: Enabling efficient and robust analysis of tandem repeats in genomic data using wavefront-based string decomposer
    • Junhai Qi,
    • Zhidong Yang,
    • Ting Yu,
    • and Guojun Li
    Genome Res. April 8, 2026 gr.281346.125; Published in Advance April 8, 2026, doi:10.1101/gr.281346.125
    ...computationally demanding. In this study, we introduce WSD10 (Wavefront-based StringDecomposer), a novel algorithm that enhances efficiency and accuracy in TRs decomposition.11 By integrating wavefront techniques, WSD significantly reduces computational and memory costs. Additionally, two12 adaptive strategies...
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  7. Mini-Review: A Hitchhiker's Guide to long-read genomic analysis
    • Medhat Mahmoud,
    • Daniel P. Agustinho,
    • and Fritz J. Sedlazeck
    Genome Res. April 2025 35: 545-558; doi:10.1101/gr.279975.124
    ..., scalability, and accuracy advancements have driven this evolution. Concurrently, novel analytical methods have emerged to harness the full potential of long reads. These advancements have enabled milestones such as the first fully completed human , enhanced identification and understanding of complex genomic...
  8. Research: The genomic consequences and persistence of sociality in spiders
    • Jilong Ma,
    • Jesper Bechsgaard,
    • Anne Aagaard,
    • Palle Villesen,
    • Trine Bilde,
    • and Mikkel Heide Schierup
    Genome Res. March 2025 35: 499-511; Published in Advance February 20, 2025, doi:10.1101/gr.279503.124
    ..., sociality is accompanied by a transition from outcrossing to inbreeding. In concert, these traits reduce effective population size, potentially rendering transitions to sociality “evolutionarily dead-ends.” We addressed this hypothesis in a comparative genomic study in spiders, in which sociality has...
  9. Method: spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
    • Tianyu Liu,
    • Tinglin Huang,
    • Wengong Jin,
    • Tinyi Chu,
    • Rex Ying,
    • and Hongyu Zhao
    Genome Res. April 2026 36: 754-768; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...transcriptomic data is hindered by high noise levels and missing gene measurements, challenges that are further compounded by the higher cost of spatial data compared to traditional single-cell data. To overcome this challenge, we introduce spRefine, a deep learning framework that leverages genomic language...
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  10. Method: A spectral component approach leveraging identity-by-descent graphs to address recent population structure in genomic analysis
    • Ruhollah Shemirani,
    • Gillian M. Belbin,
    • Sinead Cullina,
    • Christa Caggiano,
    • Christopher R. Gignoux,
    • Noah Zaitlen,
    • and Eimear E. Kenny
    Genome Res. March 2026 36: 534-546; Published in Advance December 23, 2025, doi:10.1101/gr.280659.125
    ...these approaches show promise, they currently have some limitations, such as implicating large genomic regions in variant association and requiring methodological improvements to LMM scalability. Future work could explore the use of PCs, ARGs, and SPCs in tandem as a powerful tool for addressing the challenges...
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