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Review: The superpowers of imprinting control regions
- Bertille Montibus,
- Franck Court,
- and Philippe Arnaud
Genome Res. January 2026 36: 1-19; Published in Advance December 10, 2025, doi:10.1101/gr.281215.125
...of transcriptional regulation whereby approximately 200 mammalian genes are expressed monoallelically according to their parental origin. This crucial developmental process is primarily controlled by discrete cis-regulatory elements known as imprinting control regions (ICRs), which play essential roles in directing...
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Research: Developmental transcriptomics in Pristionchus reveals the environmental responsiveness of a plasticity gene-regulatory network
- Shelley Reich,
- Tobias Loschko,
- Julie Jung,
- Samantha Nestel,
- Ralf J. Sommer,
- and Michael S. Werner
Genome Res. July 2025 35: 1560-1573; Published in Advance June 9, 2025, doi:10.1101/gr.279783.124
...by adult P. pacificus with teeth colored for ease of visualization. (B) A gene-regulatory network for mouth-form development with St-promoting genes colored in blue and Eu-promoting genes in red. Genes in black may regulate the development of both morphs. (C) Experimental design for developmental...
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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
...models (HMMs) (Tang et al. 2006; Corbett-Detig and Nielsen 2017; Browning et al. 2018, 2023) become computationally expensive and slow when faced with biobank-sized data, neural networks can provide a powerful alternative.Genomic sequence compressionThe storage and transmission of high...
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Review: A systematic guide for identifying transcription factors that directly regulate the expression of a gene of interest
- Andrew D. Bates,
- Dawid Grzela,
- Maciej Studzian,
- Louise Brennan,
- Moli Williams,
- Conor Fawcett,
- Beth Hammond,
- Manreen Grewal,
- Marcin Ratajewski,
- Lukasz Pulaski,
- and Urszula L. McClurg
Genome Res. March 2026 36: 433-459; Published in Advance February 17, 2026, doi:10.1101/gr.281154.125
...-base resolution methylation levels for individual CpG sites across the . These resources also annotate hypomethylated regions commonly linked to gene promoters, as well as allele-specific methylation patterns relevant to genomic imprinting. Notable examples include MethBase (Song et al. 2013), MethBank (Zhang et...
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Method: Gene duplication is associated with gene diversification and potential neofunctionalization in lung cancer evolution
- Paul Ashford,
- Alexander M. Frankell,
- Zofia Piszka,
- Camilla S.M. Pang,
- Mahnaz Abbasian,
- Maise Al Bakir,
- Mariam Jamal-Hanjani,
- Nicholas McGranahan,
- Charles Swanton,
- and Christine A. Orengo
Genome Res. March 2026 36: 561-577; Published in Advance February 19, 2026, doi:10.1101/gr.278663.123
...magnitude and direction of selective pressure through changes in the extracellular matrix, nutrient availability, immune system cells, cytokines, and T cell regulators (Anari et al. 2018; Mansouri et al. 2022; Nakayama et al. 2022; Otohinoyi et al. 2022).In non-small-cell lung cancer (NSCLC), pronounced...
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Method: spRefine denoises and imputes spatial transcriptomics 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. February 3, 2026 gr.281001.125; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
...and missing19 gene measurements, challenges that are further compounded by the higher cost20 of spatial data compared to traditional single-cell data. To overcome this chal-21 lenge, we introduce spRefine, a deep learning framework that leverages genomic22 language models to jointly denoise and impute spatial...
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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
...associated with cCREs and AD GWAS data across diverse cellular populations.Cell type–specific TF regulatory network in AD progressionTo gain further insight into TF-mediated gene regulation across AD progression, we constructed cell type–specific positive TF regulatory networks. We identified candidate...
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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
...studying novel sequences or SVs that might affect gene structure or regulation. This process is still highly complicated (Salzberg 2019), often including either a de novo approach over RNA-seq or a liftover approach of a close relative available . Multiple methods have been suggested (e.g., liftoff...
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Method: Chromosome engineering to correct a complex rearrangement on Chromosome 8 reveals the effects of 8p syndrome on gene expression and neural differentiation
- Sophia N. Lee,
- Erin C. Banda,
- Lu Qiao,
- Sarah L. Thompson,
- Karan Singh,
- Ryan A. Hagenson,
- Teresa Davoli,
- Stefan F. Pinter,
- and Jason M. Sheltzer
Genome Res. March 2026 36: 547-560; Published in Advance January 12, 2026, doi:10.1101/gr.280425.125
...downregulated by both conditions included genes associated with cell proliferation (PCNA, MCM6), ribosome biogenesis (NOP9), and translation (DDX39A) (Fig. 5E). Gene Ontology (GO) analysis revealed a significant upregulation of transcripts associated with the endoplasmic reticulum and the extracellular matrix...
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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. March 5, 2026 gr.281006.125; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
...remains a major genetic challenge. 21 Traditional statistical methods (such as GBLUP and BayesR) have limitations, including 22 reliance on artificial prior assumptions, and hard to capture epistatic effects. Machine learning 23 (ML) has emerged as a powerful alternative for genomic prediction; however...
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