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  1. Method: A scalable computational framework for predicting gene expression from candidate cis-regulatory elements
    • Qinhu Zhang,
    • Siguo Wang,
    • Zhipeng Li,
    • Wenzheng Bao,
    • Wenjian Liu,
    • and De-Shuang Huang
    Genome Res. February 2026 36: 361-374; Published in Advance January 16, 2026, doi:10.1101/gr.281219.125
    ...effectively. Moreover, ScPGE greatly reduces computational demands for modeling by using discrete cCREs instead of the entire genomic region flanking target genes, facilitating rapid training and deployment for new cell types. To quantify its computational demand, we systematically evaluated the model...
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  2. Method: Dissecting multilayer cell–cell communications with signaling feedback loops from spatial transcriptomics data
    • Lulu Yan,
    • Jinyu Cheng,
    • Qing Nie,
    • and Xiaoqiang Sun
    Genome Res. June 2025 35: 1400-1414; Published in Advance April 22, 2025, doi:10.1101/gr.279857.124
    ...To benchmark stMLnet, we designed a benchmark framework to evaluate and compare its performance with other representative CCC inference methods in inferring intercellular and intracellular communications (Fig. 2A). Seven state-of-the-art CCC inference methods, including CellChatV2 (Jin et al. 2025), COMMOT...
  3. Method: QuadST identifies cell–cell interaction–changed genes in spatially resolved transcriptomics data
    • Xiaoyu Song,
    • Yuqing Shang,
    • Michelle E. Ehrlich,
    • Panos Roussos,
    • Guo-Cheng Yuan,
    • and Pei Wang
    Genome Res. August 2025 35: 1821-1831; Published in Advance June 23, 2025, doi:10.1101/gr.279859.124
    ...LF, Yoon S, Willis EF, Tran M, Lam PY, Raghubar A, et al. 2023. Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues. Nat Commun 14: 7739. doi:10.1038/s41467-023-43120-6 ↵R Core Team. 2024. R: a language and environment for statistical computing. R...
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  4. Method: Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics
    • Vishvak Raghavan,
    • Yumin Zheng,
    • Yue Li,
    • and Jun Ding
    Genome Res. July 2025 35: 1646-1663; Published in Advance May 2, 2025, doi:10.1101/gr.279771.124
    ...Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics Vishvak Raghavan1,2,3, Yumin Zheng2, Yue Li1,3 and Jun Ding1,2,3 1School of Computer Science, McGill University, Montreal, Quebec H3A 2A7, Canada; 2Meakins...
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  5. Method: Multicondition and multimodal temporal profile inference during mouse embryonic development
    • Ran Zhang,
    • Chengxiang Qiu,
    • Galina N. Filippova,
    • Gang Li,
    • Jay Shendure,
    • Jean-Philippe Vert,
    • Xinxian Deng,
    • William Stafford Noble,
    • and Christine M. Disteche
    Genome Res. October 2025 35: 2339-2351; Published in Advance August 14, 2025, doi:10.1101/gr.279997.124
    ...analysis and have not been evaluated in the single-cell setting. For example, Sagittarius performs cross-species and cross-cell-line inference of bulk transcriptomic profiles through a transformer model (Woicik et al. 2023), and chronODE integrates bulk multiomics time-series data to model the temporal...
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  6. 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
    ...identification, cell-cell communication inference, batch effect correction, and spatial ageing clock improvement. (C) spRefine as a pre-training framework for representing phenotype information. spRefine is capable of performing survival prediction, identifying spot-level phenotype information, and predicting...
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  7. 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
    ..., requires efficient computational detection methods due to experimental limitations. Although machine learning predictors have been proposed, their performance could be enhanced through systematic optimization of feature encoding schemes. Here, we propose EnDeep4mC, a dual-adaptive framework integrating...
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  8. Method: Pangenome-based genome inference using integer programming
    • Ghanshyam Chandra,
    • Md Helal Hossen,
    • Stephan Scholz,
    • Alexander T. Dilthey,
    • Daniel Gibney,
    • and Chirag Jain
    Genome Res. December 2025 35: 2661-2670; Published in Advance August 21, 2025, doi:10.1101/gr.280567.125
    ...Pan-based inference using integer programming Ghanshyam Chandra1, Md Helal Hossen2, Stephan Scholz3,4, Alexander T. Dilthey3,4, Daniel Gibney2 and Chirag Jain1 1Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka 560012, India; 2Department of Computer...
  9. Method: Exon Nomenclature And Classification of Transcripts (ENACT) provides a systematic framework to annotate exon attributes
    • Paras Verma,
    • Deeksha Thakur,
    • Deepanshi Awasthi,
    • and Shashi Bhushan Pandit
    Genome Res. June 2025 35: 1440-1455; Published in Advance May 7, 2025, doi:10.1101/gr.279878.124
    ....To achieve the integration of this distinct information, we developed the exon nomenclature and classification of transcripts (ENACT) framework. ENACT's systematic translation-focused, exon-centric design streamlines the computational tracking of exonic loci while facilitating the manual and automatic...
  10. Method: Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover
    • An Wang,
    • Stephanie Hicks,
    • Donald Geman,
    • and Laurent Younes
    Genome Res. December 2025 35: 2744-2755; Published in Advance November 12, 2025, doi:10.1101/gr.280539.125
    ...the robustness of geneCover's hyperparameter selection, evaluating the stability of our clustering results across different hyperparameter settings. Lastly, we introduce a generalized geneCover framework that enables marker gene selection across multiple samples or conditions, highlighting its ability...
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