Searching journal content for articles similar to Bauer et al. 4 (2): S97.

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  1. Method: Recalibrating differential gene expression by genetic dosage variance prioritizes functionally relevant genes
    • Philipp Rentzsch,
    • Aaron Kollotzek,
    • Kaushik Ram Ganapathy,
    • Pejman Mohammadi,
    • and Tuuli Lappalainen
    Genome Res. October 2025 35: 2316-2325; Published in Advance September 17, 2025, doi:10.1101/gr.280360.124
    ...effect sizes in differential expression analysis provides a more biologically useful ranked gene list. Genome Biol 20: 67. doi:10.1186/s13059-019-1674-7 ↵Ignatiadis N, Klaus B, Zaugg JB, Huber W. 2016. Data-driven hypothesis weighting increases detection power in -scale multiple testing. Nat Methods 13...
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  2. Research: Distinct classes of lamina-associated domains are defined by differential patterns of repressive histone methylation
    • Caden J. Martin,
    • Elizabeth A. Oser,
    • Prabakaran Nagarajan,
    • Liudmila V. Popova,
    • Benjamin D. Sunkel,
    • Benjamin Z. Stanton,
    • and Mark R. Parthun
    Genome Res. September 2025 35: 1959-1974; Published in Advance August 5, 2025, doi:10.1101/gr.280380.124
    ...and regulation. One is the large degree of redundancy between tethering mechanisms and the diversity in peripheral proteins which can interact with chromatin, many of which are differentially expressed between different tissues and stages of development. Added to this is the variability of LADs themselves...
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  3. Method: Statistically rigorous and computationally efficient chromatin stripe detection with Quagga
    • Fan Feng,
    • Sean Moran,
    • Anders S. Hansen,
    • Xiaotian Zhang,
    • and Jie Liu
    Genome Res. January 2026 36: 159-168; Published in Advance November 12, 2025, doi:10.1101/gr.280132.124
    ...not accurately assess the length of stripes, always calculating the stripe starting position as directly on the main diagonal. StripeDiff (Gupta et al. 2022) detects differential stripes between experiments and reveals the connection between changes of chromatin stripe and chromatin modification, transcriptional...
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  4. 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
    ...-aware differential transcript/gene expression methods. Our method detects inner nodes that show a strong signal for differential expression, which would have been overlooked when analyzing the transcripts alone.RNA-seq has become the de facto technology for measuring the expression profiles of different genomic...
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  5. 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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  6. Method: STCC enhances spatial domain detection through consensus clustering of spatial transcriptomics data
    • Congcong Hu,
    • Nana Wei,
    • Jiyuan Yang,
    • Hua-Jun Wu,
    • and Xiaoqi Zheng
    Genome Res. June 2025 35: 1415-1428; Published in Advance May 12, 2025, doi:10.1101/gr.280031.124
    ...technologies has yielded substantial spatial transcriptomics data. Deriving biological insights from these data poses nontrivial computational and analysis challenges, of which the most fundamental step is spatial domain detection (or spatial clustering). Although a number of tools for spatial domain detection...
  7. Method: De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data
    • Arthur Dondi,
    • Nico Borgsmüller,
    • Pedro F. Ferreira,
    • Brian J. Haas,
    • Francis Jacob,
    • Viola Heinzelmann-Schwarz,
    • Tumor Profiler Consortium,
    • and Niko Beerenwinkel
    Genome Res. April 2025 35: 900-913; Published in Advance March 19, 2025, doi:10.1101/gr.279281.124
    ....beerenwinkel@bsse.ethz.chAbstractIn cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging...
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  8. Research: T2T-CHM13 improves read mapping and detection of clinically relevant genetic variation in the Swedish population
    • Daniel Schmitz,
    • Adam Ameur,
    • and Åsa Johansson
    Genome Res. November 2025 35: 2377-2388; Published in Advance September 16, 2025, doi:10.1101/gr.279320.124
    ...for the detection of many novel variants, especially in the previously unresolved regions. They also found that their novel reference reduces the number of false-positive calls mainly in protein-coding genes, and they highlight the increased sensitivity for detection of rare variants and singletons (Aganezov et al...
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  9. Method: Accurate estimation of pathway activity in single cells for clustering and differential analysis
    • Daniel Davis,
    • Avishai Wizel,
    • and Yotam Drier
    Genome Res. June 2024 34: 925-936; Published in Advance July 9, 2024, doi:10.1101/gr.278431.123
    ...with findings reported in previous studies in many cases, but SiPSiC also reveals the differential activity of novel pathways, enabling us to suggest new mechanisms underlying the pathophysiology of these diseases and demonstrating SiPSiC's high accuracy and sensitivity in detecting biological function...
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  10. 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
    ...framework for multiple ST analysis tasks, including spatial domain detection and cell cycle identification. Although these methods have successfully analyzed individual slices, they remain limited to two-dimensional (2D) analyses owing to their single-slice focus.As the availability of SO data derived from...
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