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  1. Research: Landscape of microRNA and target expression variation and covariation in single mouse embryonic stem cells
    • Marcel Tarbier,
    • Sebastian D. Mackowiak,
    • Vaishnovi Sekar,
    • Franziska Bonath,
    • Etka Yapar,
    • Bastian Fromm,
    • Omid R. Faridani,
    • Inna Biryukova,
    • and Marc R. Friedländer
    Genome Res. February 2026 36: 291-302; Published in Advance January 12, 2026, doi:10.1101/gr.279914.124
    ...assembly and folding (Tarbier et al. 2020; Gutiérrez-Pérez et al. 2021). However, studying these alternative functions require single-cell methods because variation and covariation across individual cells must be measured.There is evidence for both of these alternative miRNA functions. Almost 10 years ago...
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  2. Method: Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks
    • Prashanthi Ravichandran,
    • Princy Parsana,
    • Rebecca Keener,
    • Kasper D. Hansen,
    • and Alexis Battle
    Genome Res. September 2025 35: 2087-2103; Published in Advance July 17, 2025, doi:10.1101/gr.280808.125
    ...of direct gene interactions remains challenging in the presence of unknown technical and biological confounders (Supplemental Fig. S15A; Kernfeld et al. 2024).We inferred consensus GCNs across diverse tissues by weighted aggregation of covariance matrices estimated from residual expression and graphical...
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  3. Research: Epigenetic characterization of pseudogenes across human tissues
    • Yunzhe Jiang,
    • Beatrice Borsari,
    • and Mark Gerstein
    Genome Res. April 15, 2026 gr.280768.125; Published in Advance April 15, 2026, doi:10.1101/gr.280768.125
    ...of pseudogenes across 26 adult human tissues using matched EN-TEx transcriptomic, epigenomic, and three-dimensional chromatin interaction data, complemented by annotations of sequence features, evolutionary conservation, and population genetic variation. We constructed a comprehensive promoter catalog...
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  4. Resource: Lignature provides a curated resource of ligand induced transcriptomic signatures for signaling inference
    • Ying Xin,
    • Md Amanullah,
    • Cheng Qian,
    • Chingmo Zhou,
    • and Jiang Qian
    Genome Res. April 8, 2026 gr.281287.125; Published in Advance April 8, 2026, doi:10.1101/gr.281287.125
    ...processes. Ligand-induced transcriptomic signatures can be used to infer ligand activity; however, the absence of a comprehensive set of ligand-response signatures has limited their practical application in predicting ligand-receptor interactions. To bridge this gap, we develop Lignature, a curated database...
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  5. Method: Bayesian inference of sample-specific coexpression networks
    • Enakshi Saha,
    • Viola Fanfani,
    • Panagiotis Mandros,
    • Marouen Ben Guebila,
    • Jonas Fischer,
    • Katherine H. Shutta,
    • Dawn L. DeMeo,
    • Camila M. Lopes-Ramos,
    • and John Quackenbush
    Genome Res. September 2024 34: 1397-1410; Published in Advance August 12, 2024, doi:10.1101/gr.279117.124
    ...equally to this work. Corresponding author: johnq@hsph.harvard.eduAbstractGene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression...
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  6. Method: Unified integration of spatial transcriptomics across platforms with LLOKI
    • Ellie Haber,
    • Ajinkya Deshpande,
    • Jian Ma,
    • and Spencer Krieger
    Genome Res. December 2025 35: 2722-2733; Published in Advance November 13, 2025, doi:10.1101/gr.280803.125
    ..., Pittsburgh, Pennsylvania 15213, USA Corresponding authors: jianma@cs.cmu.edu, skrieger@andrew.cmu.eduAbstractSpatial transcriptomics (ST) has transformed our understanding of tissue architecture and cellular interactions, but integrating ST data across platforms remains challenging due to differences in gene...
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  7. Method: Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms
    • Hongyu Zheng,
    • Hirak Sarkar,
    • and Benjamin J. Raphael
    Genome Res. December 2025 35: 2734-2743; Published in Advance November 17, 2025, doi:10.1101/gr.280555.125
    ...Inference (Eq. 2) is updated to PoiLoss(AX; diag(N)PQX) + PoiLoss(AV; MTPQdiag(ϕ)).Entropy regularizationTo assign the spots in X into distinct cell types, we optionally add an entropy regularization term of to the loss function (2) with increasing weight ω = exp(k/λ) across training epochs, where k...
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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
    ...in the human MHC. Int J Biochem Cell Biol 131: 105882. doi:10.1016/j.biocel.2020.105882 ↵Dilthey A, Cox C, Iqbal Z, Nelson MR, McVean G. 2015. Improved inference in the MHC using a population reference graph. Nat Genet 47: 682–688. doi:10.1038/ng.3257 ↵Ebert P, Audano PA, Zhu Q, Rodriguez-Martin B, Porubsky D...
  9. 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
    ...that the interaction effect among cell, time, and condition should not vary based on the data type a cell is measured in.Once the model is trained, we can continuously vary the time factor to infer smooth trajectories in cellular profiles across time. Furthermore, because Sunbear can be jointly optimized on multimodal...
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  10. Method: ScisTree2 enables large-scale inference of cell lineage trees and genotype calling using efficient local search
    • Haotian Zhang,
    • Yiming Zhang,
    • Teng Gao,
    • and Yufeng Wu
    Genome Res. December 2025 35: 2781-2791; Published in Advance September 3, 2025, doi:10.1101/gr.280542.125
    ...for most settings in these data. Moreover, our results indicate that ScisTree2 is robust to the ADO rate settings, and an appropriately chosen prior improves overall inference accuracy (Supplemental Fig. S3). Therefore, we recommend using genotype priors computed from allele frequencies as the default...
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