Searching journal content for articles similar to Huang et al. 36 (2): 387.

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  1. 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
    ...to preferential visibility of one chromosome in scRNA-seq and scATAC-seq, thus improving variant detection. The major question is whether they will transfer to the low read coverage per cell of Chromium-based sequencing. To verify this viability, we benchmark SCcaller, which among the caller options has a varying...
  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...
    OPEN ACCESS ARTICLE
  3. Method: Systematic mapping of TF-mediated cell fate changes by a pooled induction coupled with scRNA-seq and multi-omics approaches
    • Muyoung Lee,
    • Qingqing Guo,
    • Mijeong Kim,
    • Joonhyuk Choi,
    • Alia Segura,
    • Alper Genceroglu,
    • Lucy LeBlanc,
    • Nereida Ramirez,
    • Yu Jin Jang,
    • Yeejin Jang,
    • Bum-Kyu Lee,
    • Edward M. Marcotte,
    • and Jonghwan Kim
    Genome Res. March 2024 34: 484-497; Published in Advance April 5, 2024, doi:10.1101/gr.277926.123
    ...of TF induction (Parekh et al. 2018; Joung et al. 2023). Furthermore, none of the studies investigated the dynamic changes in the transcriptome or the action mechanisms of potent TFs during cell fate conversion.The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding...
    OPEN ACCESS ARTICLE
  4. 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
    ...provided unparalleled insights into the transcriptional programs of cell types and cellular stages (Zeisel et al., 2015; Chen et al., 2017; Travaglini et al., 2020). Emerging population-scale scRNA-seq datasets are enabling investigations of population-level phenotypic variability as a function...
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  5. 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
    ...-specific feature expression, as evidenced by NCP selectivity in A. thaliana and CKSNAP selectivity in G. subterraneus, through co-optimization of species-specific feature selectivity and model architectural characteristics. Collectively, these findings confirm that the proposed dual-adaptive framework enables...
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  6. Method: TFcomb identifies transcription factor combinations for cellular reprogramming based on single-cell multiomics data
    • Chen Li,
    • Sijie Chen,
    • Yixin Chen,
    • Haiyang Bian,
    • Minsheng Hao,
    • Lei Wei,
    • and Xuegong Zhang
    Genome Res. June 2025 35: 1429-1439; Published in Advance April 10, 2025, doi:10.1101/gr.279955.124
    ...and scATAC-seq data as inputs. The scRNA-seq data should contain cells of both the source and target states. The scATAC-seq data are not required to be simultaneously sequenced in the same cells of the scRNA-seq data but are best derived from similar tissues to guarantee the reliability. These data...
  7. Method: Efficient integration of spatial omics data for joint domain detection, matching, and alignment with stMSA
    • Han Shu,
    • Jing Chen,
    • Chang Xu,
    • Jialu Hu,
    • Yongtian Wang,
    • Jiajie Peng,
    • Qinghua Jiang,
    • Xuequn Shang,
    • and Tao Wang
    Genome Res. October 2025 35: 2285-2299; Published in Advance August 14, 2025, doi:10.1101/gr.280584.125
    ..., and multislice alignment for 3D tissue reconstruction. By unifying these capabilities, stMSA facilitates integrative analysis of spatially resolved multiomics data, advancing insights into complex tissue architectures.ResultsOverview of stMSAThis study introduces stMSA, a novel deep graph representation learning...
  8. 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
    .... We evaluate the performance of geneCover across various scRNA-seq and spatial transcriptomics data sets, comparing it to other label-free algorithms to highlight its utility and potential in diverse biological contexts.The identification of marker genes plays a critical role in advancing our...
  9. Method: Fast sequence alignment for centromeres with RaMA
    • Pinglu Zhang,
    • Yanming Wei,
    • Qinzhong Tian,
    • Quan Zou,
    • and Yansu Wang
    Genome Res. May 2025 35: 1209-1218; Published in Advance February 12, 2025, doi:10.1101/gr.279763.124
    ...only shares a small portion of the alignment path with RaMA, with the majority of its alignment results deviating substantially from RaMA's. We also applied the WFA with a dual-affine gap penalty using different parameter settings to align both the simulated and real data sets (see Supplemental Fig. S1...
  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
    .... An immediate future extension of ROSeq therefore would be enabling multigroup (≥2) comparisons.MethodsDescription of data setsWe used four publicly available scRNA-seq data sets for the various analyses. For better readability, we name the data sets after the first investigators’ surnames. Among these...
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