Searching journal content for articles similar to Aevermann et al. 31 (10): 1767.

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  1. 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
    .... During each iteration, we only keep cells of a single cell type in R and Q.Integrating reference and query single-cell data setsThe principal components of the gene expression values of the cells in the combined data set R ∪ Q are found. Let PC: ℝg → ℝN be the PCA transformation that maps the gene...
  2. Method: Accurate fusion transcript identification from long- and short-read isoform sequencing at bulk or single-cell resolution
    • Qian Qin,
    • Victoria Popic,
    • Kirsty Wienand,
    • Houlin Yu,
    • Emily White,
    • Akanksha Khorgade,
    • Asa Shin,
    • Christophe Georgescu,
    • Catarina D. Campbell,
    • Arthur Dondi,
    • Niko Beerenwinkel,
    • Francisca Vazquez,
    • Aziz M. Al'Khafaji,
    • and Brian J. Haas
    Genome Res. April 2025 35: 967-986; Published in Advance March 14, 2025, doi:10.1101/gr.279200.124
    ...short-read sequences. Recent advances in long-read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single-cell samples. Here, we developed a new computational tool, CTAT-LR-Fusion, to detect fusion transcripts from long-read RNA-seq with or without...
    OPEN ACCESS ARTICLE
  3. Method: Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics
    • Vishvak Raghavan,
    • Yumin Zheng,
    • Yue Li,
    • and Jun Ding
    Genome Res. July 2025 35: 1646-1663; Published in Advance May 2, 2025, doi:10.1101/gr.279771.124
    .... This makes them insensitive in complex environments where the detailed dynamics of cell interactions matter. We introduce CellAgentChat, an agent-based model (ABM) designed to decipher CCIs from single-cell RNA sequencing and spatial transcriptomics data. This approach models biological systems...
    OPEN ACCESS ARTICLE
  4. Method: Long-read RNA sequencing reveals allele-specific N6-methyladenosine modifications
    • Dayea Park and
    • Can Cenik
    Genome Res. April 2025 35: 999-1011; Published in Advance October 29, 2024, doi:10.1101/gr.279270.124
    ...-methyladenosine (m6A) modifications in native mRNA. We used human and mouse cells with known genetic variants to assign the allelic origin of each mRNA molecule combined with a supervised machine learning model to detect read-level m6A modification ratios. Our analyses reveal the importance of sequences adjacent...
  5. Method: Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE
    • Yingxin Lin,
    • Tung-Yu Wu,
    • Xi Chen,
    • Sheng Wan,
    • Brian Chao,
    • Jingxue Xin,
    • Jean Y.H. Yang,
    • Wing H. Wong,
    • and Y.X. Rachel Wang
    Genome Res. January 2024 34: 119-133; Published in Advance December 15, 2023, doi:10.1101/gr.277960.123
    ...from data generated by assays of single-cell RNA sequencing (scRNA-seq) and single-cell transposase-accessible chromatin sequencing (scATAC-seq). Most of these methods infer the relationships between TFs and target genes by estimating their interactions with cis-regulatory elements (CREs...
    OPEN ACCESS ARTICLE
  6. Resource: Whole-genome sequencing of diverse 351 cultured prokaryotes including as-yet-unsequenced type strains
    • Shingo Kato,
    • Sachiko Masuda,
    • Arisa Shibata,
    • Takashi Itoh,
    • Mitsuo Sakamoto,
    • Ken Shirasu,
    • and Moriya Ohkuma
    Genome Res. April 2026 36: 875-884; Published in Advance April 7, 2026, doi:10.1101/gr.281026.125
    ...that the high values of contamination level observed in this strain and others were not caused by actual contamination but rather by a limitation of the checking ability in the software used in this study. Indeed, no 16S rRNA gene sequences from potential contaminants were detected in any of the assemblies...
  7. Editorial: New frontiers in single-cell genomics
    • Nicholas E. Navin,
    • Orit Rozenblatt-Rosen,
    • and Nancy R. Zhang
    Genome Res. October 2021 31: ix-x; doi:10.1101/gr.276129.121
    .... A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing. Genome Res (this issue) 31: 1767–1780. doi:10.1101/gr.275569.121 ↵Alghamdi N, Chang W, Dang P, Lu X, Wan C, Gampala S, Huang Z, Wang J, Ma Q, Zang Y, et al. 2021...
    OPEN ACCESS ARTICLE
  8. Method: Semisupervised adversarial neural networks for single-cell classification
    • Jacob C. Kimmel and
    • David R. Kelley
    Genome Res. October 2021 31: 1781-1793; Published in Advance February 24, 2021, doi:10.1101/gr.268581.120
    ...) and sequencing protocol annotations (B) in a UMAP projection of single-cell and nucleus RNA-seq profiles from the mouse kidney (Denisenko et al. 2020). Each protocol represents a unique training domain that captures technical variation. (C) Performance of scNym and baseline approaches on single-cell...
    OPEN ACCESS ARTICLE
  9. Method: Evaluation of strategies for evidence-driven genome annotation using long-read RNA-seq
    • Alejandro Paniagua,
    • Cristina Agustín-García,
    • Francisco J. Pardo-Palacios,
    • Thomas Brown,
    • Maite De Maria,
    • Nancy D. Denslow,
    • Camila J. Mazzoni,
    • and Ana Conesa
    Genome Res. April 2025 35: 1053-1064; Published in Advance December 23, 2024, doi:10.1101/gr.279864.124
    ...'s organisms, herald a new era of possibilities and requirements for annotation. Traditionally, annotation has relied on ab initio algorithms alone or combined with short-read sequencing and proteomics data to improve gene predictions. However, with the increased availability and throughput of lrRNA...
    OPEN ACCESS ARTICLE
  10. Research: Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis
    • Olga M. Sigalova,
    • Mattia Forneris,
    • Frosina Stojanovska,
    • Bingqing Zhao,
    • Rebecca R. Viales,
    • Adam Rabinowitz,
    • Fayrouz Hammal,
    • Benoît Ballester,
    • Judith B. Zaugg,
    • and Eileen E.M. Furlong
    Genome Res. May 2025 35: 1138-1153; Published in Advance April 15, 2025, doi:10.1101/gr.279652.124
    ...×–125× sequencing coverage across each line (Methods). Variants were called using the GATK4 best-practice pipeline (Poplin et al. 2018); the results were filtered to obtain a stringent variant set used in association tests (to minimize false discovery); and a lenient set was used to correct for mappability issues...
    OPEN ACCESS ARTICLE
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