Searching journal content for articles similar to Lee et al. 34 (3): 484.

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  1. Method: scSHEFT enables multiomics label transfer from scRNA-seq to scATAC-seq through dual alignment
    • Zhitao Huang,
    • Ruiqing Zheng,
    • Pengzhen Jia,
    • Xuhua Yan,
    • Jinmiao Chen,
    • and Min Li
    Genome Res. February 2026 36: 387-396; Published in Advance January 20, 2026, doi:10.1101/gr.280410.125
    ...labels are transferred from well-annotated scRNA-seq data to less-annotated omics data, such as scATAC-seq. This approach leverages the gene expression profiles available in scRNA-seq to help annotate common cell types and even novel cell types for other omics data. However, the heterogeneous features...
  2. Method: Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet
    • Chuanyuan Wang and
    • Zhi-Ping Liu
    Genome Res. February 2025 35: 340-354; Published in Advance December 18, 2024, doi:10.1101/gr.279551.124
    ...and genetic contexts. Here, we propose a discrete diffusion generation model, called DigNet, capable of generating corresponding GRNs from high-throughput single-cell RNA sequencing (scRNA-seq) data. DigNet embeds the network generation process into a multistep recovery procedure with Markov properties. Each...
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  3. Method: Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN
    • Wenhao Zhang,
    • Lan Cao,
    • Xiaoxuan Gu,
    • Yongyu Long,
    • and Ying Wang
    Genome Res. January 2026 36: 142-158; Published in Advance October 9, 2025, doi:10.1101/gr.280757.125
    ...heterogeneity and the mechanisms underlying development and disease. However, current GRN inference methods fail to utilize multi-omics data and prior knowledge from a biologically interpretable insight. Therefore, we propose PRISM-GRN, a Bayesian model that seamlessly incorporates known GRNs, along with scRNA-seq...
  4. Method: Benchmarking bulk and single-cell variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries
    • Matthew Wiens,
    • Hossein Farahani,
    • R. Wilder Scott,
    • T. Michael Underhill,
    • and Ali Bashashati
    Genome Res. August 2024 34: 1196-1210; Published in Advance August 15, 2024, doi:10.1101/gr.277066.122
    ...applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level...
  5. Method: Debiased personalized gene coexpression networks for population-scale scRNA-seq data
    • Shan Lu and
    • Sunduz Keles
    Genome Res. June 9, 2023 gr.277363.122; Published in Advance June 9, 2023, doi:10.1101/gr.277363.122
    ...for materials should be addressed to Su¨ndu¨z Keles¸ (e-mail: keles@stat.wisc.edu; Telephone: 608-265-4384). Running title Debiased personalized coexpression networks Abstract Population-scale single-cell RNA-seq (scRNA-seq) datasets create unique opportunities for quantifying expression variation across...
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  6. Method: Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies
    • Jianing Yao,
    • Jinglun Yu,
    • Brian Caffo,
    • Stephanie C. Page,
    • Keri Martinowich,
    • and Stephanie C. Hicks
    Genome Res. July 2025 35: 1621-1632; Published in Advance May 20, 2025, doi:10.1101/gr.279380.124
    ...in Healthcare, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: shicks19@jhu.eduAbstractRecent advances in spatially resolved single-omic and multi-omics technologies have led to the emergence of computational tools to detect and predict spatial domains. Additionally, histological...
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  7. Research: Multi-omics analyses demonstrate a critical role for EHMT1 methyltransferase in transcriptional repression during oogenesis
    • Hannah Demond,
    • Courtney W. Hanna,
    • Juan Castillo-Fernandez,
    • Fátima Santos,
    • Evangelia K. Papachristou,
    • Anne Segonds-Pichon,
    • Kamal Kishore,
    • Simon Andrews,
    • Clive S. D'Santos,
    • and Gavin Kelsey
    Genome Res. January 2023 33: 18-31; Published in Advance January 23, 2023, doi:10.1101/gr.277046.122
    ...imaging, multi-omics sequencing, and mass spectrometry (MS)–based proteome analyses in cKO oocytes. Although H3K9me1 was depleted only upon loss of EHMT1, H3K9me2 was decreased, and H3K9me2-enriched domains were eliminated equally upon loss of EHMT1 or EHMT2. Furthermore, there were more significant...
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  8. 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
    ...sufficient. Combining tools with awareness of their limitations allows researchers to design experiments with high-confidence hypotheses. However, one of the major steps when trying to identify functional regions of a gene involves systematic perturbation of its regulatory DNA through deletion, linker...
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  9. Research: Single-cell multi-omics of human preimplantation embryos shows susceptibility to glucocorticoids
    • Cheng Zhao,
    • Savana Biondic,
    • Katherine Vandal,
    • Åsa K. Björklund,
    • Michael Hagemann-Jensen,
    • Theresa Maria Sommer,
    • Jesica Canizo,
    • Stephen Clark,
    • Pascal Raymond,
    • Daniel R. Zenklusen,
    • Nicolas Rivron,
    • Wolf Reik,
    • and Sophie Petropoulos
    Genome Res. September 2022 32: 1627-1641; Published in Advance August 10, 2022, doi:10.1101/gr.276665.122
    ...of transcriptome and methylation and using Small-seq to measure small RNA biotypes. (B) The number of cells retained after quality control for scRNA-seq data. (C) UMAP showing individual cells from all control and glucocorticoid-exposed embryos. Cells are colored by lineages (left) and treatments (right...
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  10. Method: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data
    • Krishan Gupta,
    • Manan Lalit,
    • Aditya Biswas,
    • Chad D. Sanada,
    • Cassandra Greene,
    • Kyle Hukari,
    • Ujjwal Maulik,
    • Sanghamitra Bandyopadhyay,
    • Naveen Ramalingam,
    • Gaurav Ahuja,
    • Abhik Ghosh,
    • and Debarka Sengupta
    Genome Res. April 2021 31: 689-697; Published in Advance March 5, 2021, doi:10.1101/gr.267070.120
    ...be noted that ROSeq is not inbuilt with any batch correction method. As such, it expects the user to input an scRNA-seq data set that is not only library size normalized but also free of other covariates as applicable.We systematically compared the performance of ROSeq with some of the existing best...
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