Searching journal content for articles similar to Wan et al. 30 (2): 205.

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  1. Resource: Multitissue single-nucleus RNA-seq reveals cell type–specific regulatory patterns of alternative polyadenylation in pigs
    • Qiuhan Wen,
    • Zhen Wang,
    • Qi Bao,
    • Tianli Ding,
    • Haihan Zhang,
    • Jianbo Li,
    • Zhuang Liu,
    • Jieping Huang,
    • and Guoqiang Yi
    Genome Res. September 2025 35: 2116-2129; Published in Advance June 16, 2025, doi:10.1101/gr.280095.124
    ...polyadenylation (APA) plays a crucial role in gene regulation and phenotypic diversity. Whereas extensive studies have explored the global APA landscape using bulk RNA-seq data, in-depth analyses of APA events at the single-cell level remain limited—particularly in farm animals. In this study, we construct...
  2. Method: Simultaneous modeling of chromatin conformation changes from multiple single-cell interaction maps with ChromMovie
    • Krzysztof H. Banecki,
    • Haoxi Chai,
    • Yijun Ruan,
    • and Dariusz Plewczynski
    Genome Res. April 8, 2026 ; Published in Advance April 8, 2026, doi:10.1101/gr.280985.125
    ...within cellular genomics, we find significant advances in single-cell analysis. For instance, single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomics by offering advantages over traditional bulk analysis (Stegle et al. 2015; Bacher and Kendziorski 2016). Single-cell transcriptomics has...
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  3. Method: Tree-based differential testing using inferential uncertainty for RNA-seq
    • Noor Pratap Singh,
    • Euphy Y. Wu,
    • Jason Fan,
    • Michael I. Love,
    • and Rob Patro
    Genome Res. October 2025 35: 2326-2338; Published in Advance August 21, 2025, doi:10.1101/gr.279981.124
    ...also be explored in the future.Finally, it would be intriguing to explore the development of a TreeTerminus- and mehenDi-like approach in the context of single-cell RNA-seq analysis, where there are quite distinct but related opportunities for data-driven aggregation to increase the power...
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  4. Method: High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND
    • Kehan Lang,
    • Chenyang Jia,
    • Siyu Li,
    • Yi Guo,
    • Dingjun Hu,
    • and Shengquan Chen
    Genome Res. April 2026 36: 769-784; Published in Advance March 26, 2026, doi:10.1101/gr.281350.125
    ...BOND, a dual-aligned VAE framework that enables accurate and biologically meaningful translation between scRNA-seq and scDNAm data at the single-cell level. By integrating modality-specific encoders, MoE block, self-attention mechanism, and a feature recalibration module, scBOND effectively models complex...
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  5. Method: Quantifying pathological progression from single-cell transcriptomic data with scPSS
    • Samin Rahman Khan,
    • M Saifur Rahman,
    • M. Sohel Rahman,
    • and Md Abul Hassan Samee
    Genome Res. February 2026 36: 375-386; Published in Advance January 14, 2026, doi:10.1101/gr.280411.125
    ...technical artifacts remains a key problem in single-cell analysis, and scPSS is no exception. Batch effects, present in most data sets, can obscure changes that are disease-causing. scPSS employs Harmony for batch effect removal. However, this process may potentially remove some disease-related variation...
  6. 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
    ...and target genes (Gao et al. 2023).Single-cell RNA sequencing (scRNA-seq) enables gene expression profiling at the individual cell level, revealing cellular heterogeneity with single-cell resolution and significantly enhancing the understanding of cell type–specific gene regulation (Chen and Liu 2022; Kartha...
  7. 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
    ...sizes for reliable GCN inference. recount3, a data set with 316,443 processed human RNA-seq samples, provides an opportunity to improve network reconstruction. However, GCN inference from public data is challenged by confounders and inconsistent labeling. To address this, we develop a pipeline...
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  8. Research: Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments
    • John T. Chamberlin,
    • Younghee Lee,
    • Gabor T. Marth,
    • and Aaron R. Quinlan
    Genome Res. February 2024 34: 179-188; Published in Advance February 14, 2024, doi:10.1101/gr.278253.123
    ...analysis. Finally, we show that inclusion of pre-mRNA in bioinformatic processing can impart a larger effect than assay choice itself, which is pivotal to the effective reuse of existing data. These analyses advance our understanding of the sources of variation in single-cell and single-nucleus RNA-seq...
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  9. 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
    ...biologically meaningful structures in spatial transcriptomics data. Its performance underscores its potential for providing more accurate and biologically relevant insights into additional spatial transcriptomic and scRNA-seq data.geneCover improves resolution in single-cell and spatial transcriptomics...
  10. Method: Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method
    • Quanyou Cai,
    • Lihui Lin,
    • Xin Liu,
    • and Jiekai Chen
    Genome Res. November 2025 35: 2513-2526; Published in Advance October 22, 2025, doi:10.1101/gr.280532.125
    ...allocating with the algorithm.Mapping single-cell data to in situ sequencing spatial dataFor STARMAP data (Wang et al. 2018), we performed spatial mapping of processed Smart-seq2 snRNA-seq data (Tasic et al. 2018), consisting of 14,249 cells and 34,041 genes, onto 1523 spatial cells. Because STARMAP detected...
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