Searching journal content for articles similar to Zhao et al. 33 (6): 988.

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  1. Method: Iterative improvement of deep learning models using synthetic regulatory genomics
    • André M. Ribeiro-dos-Santos and
    • Matthew T. Maurano
    Genome Res. November 2025 35: 2539-2549; Published in Advance October 22, 2025, doi:10.1101/gr.280540.125
    ...performance for other tracks. Our results show that current deep learning models perform poorly when presented with novel sequences diverging in certain critical features from their training set. Thus, an iterative approach incorporating profiling of synthetic constructs can improve model generalizability...
  2. Method: Fast and flexible bacterial genomic epidemiology with PopPUNK
    • John A. Lees,
    • Simon R. Harris,
    • Gerry Tonkin-Hill,
    • Rebecca A. Gladstone,
    • Stephanie W. Lo,
    • Jeffrey N. Weiser,
    • Jukka Corander,
    • Stephen D. Bentley,
    • and Nicholas J. Croucher
    Genome Res. February 2019 29: 304-316; Published in Advance January 24, 2019, doi:10.1101/gr.241455.118
    ...-time surveillance with consistent cluster naming between studies and allows for outbreak detection using hundreds of s in minutes. Interactive visualization and online publication is streamlined through the automatic output of results to multiple platforms. PopPUNK has been designed as a flexible platform...
    OPEN ACCESS ARTICLE
  3. Method: Pangenome-based genome inference using integer programming
    • Ghanshyam Chandra,
    • Md Helal Hossen,
    • Stephan Scholz,
    • Alexander T. Dilthey,
    • Daniel Gibney,
    • and Chirag Jain
    Genome Res. December 2025 35: 2661-2670; Published in Advance August 21, 2025, doi:10.1101/gr.280567.125
    ...are consecutive vertices in aj.h, we achieve an inferred path satisfying the conditions stated in the lemma after at most n iterations.Next, we present a definition of the expanded graph in which we will consider only the useful recombinations. For technical reasons, we preprocess each edge in E, splitting...
  4. Method: Inferring ancestry with the hierarchical soft clustering approach tangleGen
    • Klara Elisabeth Burger,
    • Solveig Klepper,
    • Ulrike von Luxburg,
    • and Franz Baumdicker
    Genome Res. December 2024 34: 2244-2255; Published in Advance October 21, 2024, doi:10.1101/gr.279399.124
    ...inferred ancestral relationships from the tangles tree, tangleGen accounts for the reliability of the cuts. To do so, the soft clustering incorporates an estimate of the number of misclassified individuals according to an assumed HWE.The Tangles framework has the flexibility to explore different research...
  5. Method: ScisTree2 enables large-scale inference of cell lineage trees and genotype calling using efficient local search
    • Haotian Zhang,
    • Yiming Zhang,
    • Teng Gao,
    • and Yufeng Wu
    Genome Res. December 2025 35: 2781-2791; Published in Advance September 3, 2025, doi:10.1101/gr.280542.125
    ..., such as development, aging, and immunology. ScisTree2 provides an alternative way of reconstructing trees directly from large numbers of cells without the need of clustering and can potentially avoid clustering errors. As shown in the Results section, the inferred cell lineage tree can provide the clustering of cells...
    OPEN ACCESS ARTICLE
  6. 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
    ...practices for GCN inference by leveraging publicly available RNA-seq data, and a set of consensus and context-specific networks that will aid the scientific community in achieving the full potential of GCN inference in biomedical research.ResultsManual annotation and clustering of RNA-seq data from recount3...
    OPEN ACCESS ARTICLE
  7. 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
    ...the resilience of specific consensus strategies in maintaining clustering accuracy despite variations in the number of clusters, reinforcing their applicability in complex ST data sets.Finally, we visualized the clustering results and performed trajectory inference. By taking the BZ5 slice as an example, all...
  8. Method: Multicondition and multimodal temporal profile inference during mouse embryonic development
    • Ran Zhang,
    • Chengxiang Qiu,
    • Galina N. Filippova,
    • Gang Li,
    • Jay Shendure,
    • Jean-Philippe Vert,
    • Xinxian Deng,
    • William Stafford Noble,
    • and Christine M. Disteche
    Genome Res. October 2025 35: 2339-2351; Published in Advance August 14, 2025, doi:10.1101/gr.279997.124
    ...to be immensely valuable, they suffer from a fundamental limitation: Because sequencing measurements are usually disruptive, it is impossible to trace a given cell's behavior across time. In addition, single-cell profile inference suffers from heterogeneity of biological samples (e.g., female vs. male, disease vs...
    OPEN ACCESS ARTICLE
  9. Method: ERC2.0 evolutionary rate covariation update improves inference of functional interactions across large phylogenies
    • Jordan H. Little,
    • Guillermo Hoffmann Meyer,
    • Aakash Grover,
    • Alex Michael Francette,
    • Raghavendran Partha,
    • Karen M. Arndt,
    • Martin Smith,
    • Nathan Clark,
    • and Maria Chikina
    Genome Res. September 2025 35: 2041-2051; Published in Advance August 7, 2025, doi:10.1101/gr.280586.125
    ...with hundreds of species and tens of thousands of genes. ERC2.0 improves upon previous iterations of ERC in algorithm speed, normalizing for heteroskedasticity, and normalizing correlations via Fisher transformations. These improvements have resulted in greater statistical power to predict biological function...
  10. Method: Bayesian inference of sample-specific coexpression networks
    • Enakshi Saha,
    • Viola Fanfani,
    • Panagiotis Mandros,
    • Marouen Ben Guebila,
    • Jonas Fischer,
    • Katherine H. Shutta,
    • Dawn L. DeMeo,
    • Camila M. Lopes-Ramos,
    • and John Quackenbush
    Genome Res. September 2024 34: 1397-1410; Published in Advance August 12, 2024, doi:10.1101/gr.279117.124
    ...computed both sparse and dense networks, then computed pairwise correlations and distances between all the networks derived for each sample (Fig. 3A), and used a number of clustering methods (k-means, spectral, and agglomeration clustering) to determine how well correlation matrices inferred using each...
    OPEN ACCESS ARTICLE
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