Searching journal content for articles similar to Carmona et al. 27 (3): 451.

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  1. 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
    ...). In particular, single-cell multi-omics technologies like SHARE-seq (Ma et al. 2020) can simultaneously profile transcriptomic and epigenomic data within individual cells, enabling the interrogation of cellular heterogeneity and molecular hierarchy (Cao et al. 2024). Consequently, numerous methods have emerged...
  2. Method: Single-cell Rapid Capture Hybridization sequencing reliably detects isoform usage and coding mutations in targeted genes
    • Hongke Peng,
    • Jafar S. Jabbari,
    • Luyi Tian,
    • Changqing Wang,
    • Yupei You,
    • Chong Chyn Chua,
    • Natasha S. Anstee,
    • Noorul Amin,
    • Andrew H. Wei,
    • Nadia M. Davidson,
    • Andrew W. Roberts,
    • David C.S. Huang,
    • Matthew E. Ritchie,
    • and Rachel Thijssen
    Genome Res. April 2025 35: 942-955; Published in Advance January 10, 2025, doi:10.1101/gr.279322.124
    .... While single-cell DNA sequencing, such as Mission Bio Tapestri, provides insight into clonality upon drug resistance (Thompson et al. 2022), it lacks transcriptome profiling per cell. In contrast, scRaCH-seq offers short-read whole transcriptomic data and mutation status if the mRNA of the targeted gene...
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  3. Resource: A single-cell transcriptome atlas of the maturing zebrafish telencephalon
    • Shristi Pandey,
    • Anna J. Moyer,
    • and Summer B. Thyme
    Genome Res. April 2023 33: 658-671; Published in Advance April 18, 2023, doi:10.1101/gr.277278.122
    ...signatures of the neuronal cell types in the telencephalon and the timeline of their emergence from larva to adult remain largely undescribed. Using an integrated analysis of single-cell transcriptomes of approximately 64,000 cells obtained from 6-day-postfertilization (dpf), 15-dpf, and adult telencephalon...
  4. Method: Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach
    • Fabien Wehbe,
    • Levi Adams,
    • Jordan Babadoudou,
    • Samantha Yuen,
    • Yoon-Seong Kim,
    • and Yoshiaki Tanaka
    Genome Res. January 2025 35: 135-146; Published in Advance December 2, 2024, doi:10.1101/gr.278812.123
    ...IDST to estimate disease status and drug response may assist in differential gene expression analysis of single-cell transcriptome data and may provide fascinating molecular insights of disease.MethodsSupervised and weakly supervised deep learningOur weakly supervised deep learning model is composed of three main...
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  5. Review: Advancements in prospective single-cell lineage barcoding and their applications in research
    • Xiaoli Zhang,
    • Yirui Huang,
    • Yajing Yang,
    • Qi-En Wang,
    • and Lang Li
    Genome Res. December 2024 34: 2147-2162; Published in Advance November 21, 2024, doi:10.1101/gr.278944.124
    ...the prospective tracking of millions of individual cells simultaneously, providing a unique opportunity to trace cellular lineages over time (Wagner and Klein 2020). The integration of single-cell lineage tracing (scLT) and single-cell transcriptomics presents a significant opportunity to explore clonal...
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  6. Review: Understanding isoform expression by pairing long-read sequencing with single-cell and spatial transcriptomics
    • Natan Belchikov,
    • Justine Hsu,
    • Xiang Jennie Li,
    • Julien Jarroux,
    • Wen Hu,
    • Anoushka Joglekar,
    • and Hagen U. Tilgner
    Genome Res. November 2024 34: 1735-1746; doi:10.1101/gr.279640.124
    ...of single-cell technology with the long-range information offered by long-read sequencing has resulted in new insights into the transcriptomes of disease-relevant systems with heterogeneous cell populations. In cancer, transcriptomic variation among cells can be layered on top of genomic variation. Short...
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  7. Resource: Dynamic dysregulation of retrotransposons in neurodegenerative diseases at the single-cell level
    • Wankun Deng,
    • Citu Citu,
    • Andi Liu,
    • and Zhongming Zhao
    Genome Res. October 2024 34: 1687-1699; Published in Advance October 18, 2024, doi:10.1101/gr.279363.124
    ...they are specifically involved in complex diseases is largely unknown. Here, we investigate the cellular heterogeneity of RTEs using 12 single-cell transcriptome profiles covering three neurodegenerative diseases, Alzheimer's disease (AD), Parkinson's disease, and multiple sclerosis. We identify cell type marker RTEs...
  8. Method: Dissecting and improving gene regulatory network inference using single-cell transcriptome data
    • Lingfeng Xue,
    • Yan Wu,
    • and Yihan Lin
    Genome Res. September 2023 33: 1609-1621; Published in Advance August 14, 2023, doi:10.1101/gr.277488.122
    ...expression data alone, as it would provide key guiding principles for improving GRN inference. In this work, we provided a quantitative framework for delineating the effects of gene-level and network-level factors on the accuracy of GRN inference using single-cell transcriptome data, identified fundamental...
  9. Method: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data
    • Cameron Park,
    • Shouvik Mani,
    • Nicolas Beltran-Velez,
    • Katie Maurer,
    • Teddy Huang,
    • Shuqiang Li,
    • Satyen Gohil,
    • Kenneth J. Livak,
    • David A. Knowles,
    • Catherine J. Wu,
    • and Elham Azizi
    Genome Res. September 2024 34: 1384-1396; Published in Advance September 5, 2024, doi:10.1101/gr.279126.124
    ...or activating cross talk.Here, we present Dynamic Intercellular Interactions in Single Cell transcriptOmics (DIISCO), an open-source tool (https://github.com/azizilab/DIISCO_public) for joint inference of cell type dynamics and communication patterns. DIISCO is a Bayesian framework that infers dynamic...
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  10. Research: Degrees of convergent evolution in rodent adaptations to arid environments
    • Domitille Chalopin,
    • Carine Rey,
    • Jeremy Ganofsky,
    • Juliana Blin,
    • Pascale Chevret,
    • Marion Mouginot,
    • Laurent Guéguen,
    • Bastien Boussau,
    • Sophie Pantalacci,
    • and Marie Sémon
    Genome Res. March 2026 36: 472-486; Published in Advance January 21, 2026, doi:10.1101/gr.280089.124
    ...of genomic and transcriptomic convergence.Here we studied the effects of timescale on repeated genomic and transcriptomic evolution associated with transition to arid environments (Fig. 1A). First, we quantified signals of repeated evolution at two timescales, corresponding to the order Rodentia...
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