Searching journal content for articles similar to Han and Molloy 33 (7): 1042.

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  1. Method: A scalable computational framework for predicting gene expression from candidate cis-regulatory elements
    • Qinhu Zhang,
    • Siguo Wang,
    • Zhipeng Li,
    • Wenzheng Bao,
    • Wenjian Liu,
    • and De-Shuang Huang
    Genome Res. February 2026 36: 361-374; Published in Advance January 16, 2026, doi:10.1101/gr.281219.125
    ...@eitech.edu.cnAbstractDeciphering the relationships between cis-regulatory elements (CREs) and target gene expression has been a long-standing unsolved problem in molecular biology, and the dynamics of CREs in different cell types make this problem more challenging. To address this challenge, we propose a scalable computational framework...
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  2. Method: Balancing gene ontology annotation specificity in protein function prediction based on the protein sequence large graph
    • Jiangyi Shao,
    • Shutao Chen,
    • Ziwen Wang,
    • Zixu Chen,
    • and Bin Liu
    Genome Res. April 15, 2026 gr.280816.125; Published in Advance April 15, 2026, doi:10.1101/gr.280816.125
    ...206 computational approach is required to effectively bridge this gap and improve the 207 accuracy of GO term prediction across all specificity levels. 208 209 To address the imbalance across GO specificity levels, we construct the protein 210 sequence large graph (PSL), which employs an imbalance...
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  3. Method: Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet
    • Yidi Deng,
    • Jiadong Mao,
    • Jarny Choi,
    • and Kim-Anh Lê Cao
    Genome Res. April 2026 36: 785-801; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
    ...) graph that defines each cell’s local neighborhood in the PC space is constructed. (B) Neighborhood regression: For each cell, NNet performs regression within the cell’s KNN using the LRA-derived gene expression as the response and PCs as predictors. This process quantifies coexpression between genes...
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  4. Method: Graph-based deep reinforcement learning for haplotype assembly with Ralphi
    • Enzo Battistella,
    • Anant Maheshwari,
    • Barış Ekim,
    • Bonnie Berger,
    • and Victoria Popic
    Genome Res. December 2025 35: 2617-2625; Published in Advance November 14, 2025, doi:10.1101/gr.280569.125
    ..., while maintaining high phasing block lengths across several standard benchmark data sets from both short and long read sequencing platforms and different coverage regimes.ResultsOverview of RalphiAt a high level, Ralphi consists of two key modules (Fig. 1): (1) fragment graph construction from read...
  5. Method: Partitioned multi-MUM finding for scalable pangenomics with MumemtoM
    • Vikram S. Shivakumar and
    • Ben Langmead
    Genome Res. February 2026 36: 397-404; Published in Advance November 7, 2025, doi:10.1101/gr.280940.125
    ....0: scalable core- alignment for massive microbial datasets. Bioinformatics 40: btae311. doi:10.1093/bioinformatics/btae311 ↵Li H. 2025. A collection of high-quality human assemblies [Data set]. In BWT construction and search at the terabase scale (3.0, Vol. 40, Number 12, p. btae717). Zenodo. https...
  6. 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
    ...of the initial trees constructed by the neighbor joining algorithm (Saitou and Nei 1987). The results on the initial tree accuracy for fixed genotypes versus uncertain genotypes are shown in Supplemental Figure S1. Initial trees are reasonably accurate with high-quality data but are less accurate than the trees...
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  7. Method: Highly accurate assembly polishing with DeepPolisher
    • Mira Mastoras,
    • Mobin Asri,
    • Lucas Brambrink,
    • Prajna Hebbar,
    • Alexey Kolesnikov,
    • Daniel E. Cook,
    • Maria Nattestad,
    • Julian Lucas,
    • Taylor S. Won,
    • Pi-Chuan Chang,
    • Andrew Carroll,
    • Benedict Paten,
    • Kishwar Shafin,
    • and and the Human Pangenome Reference Consortium
    Genome Res. July 2025 35: 1595-1608; Published in Advance May 19, 2025, doi:10.1101/gr.280149.124
    ...assemblies, DeepPolisher improves the overall concordance with the Q100 within centromeres, removing 85 SNVs (out of 21,073 total) and 443 INDELs (out of 8516 total). (Supplemental Table 5).To test how DeepPolisher impacts the coding regions, we extracted genes containing frameshift and premature stop...
  8. Method: Accurate estimation of intraspecific microbial gene content variation in metagenomic data with MIDAS v3 and StrainPGC
    • Byron J. Smith,
    • Chunyu Zhao,
    • Veronika Dubinkina,
    • Xiaofan Jin,
    • Liron Zahavi,
    • Saar Shoer,
    • Jacqueline Moltzau-Anderson,
    • Eran Segal,
    • and Katherine S. Pollard
    Genome Res. May 2025 35: 1247-1260; Published in Advance April 10, 2025, doi:10.1101/gr.279543.124
    ...-specific impacts on human health, and methods that accurately determine gene content from metagenomic data can greatly improve our understanding of the extent and importance of this intraspecific diversity. Widely used tools for analyzing metagenomic data can accurately quantify the abundance of species present...
    OPEN ACCESS ARTICLE
  9. Method: Highly accurate reference and method selection for universal cross–data set cell type annotation with CAMUS
    • Qunlun Shen,
    • Shuqin Zhang,
    • and Shihua Zhang
    Genome Res. November 2025 35: 2527-2538; Published in Advance October 1, 2025, doi:10.1101/gr.280821.125
    ...the reference selection was guided by the CAMUS score (median 0.8341 vs. 0.8101, P = 7.5 × 10−4), and the annotation accuracy improved significantly (Fig. 2F). This phenomenon was similarly reflected in other methods. That is to say, prioritizing the reference data sets is essential for more accurate annotation...
  10. Method: A fast and scalable method for inferring phylogenetic networks from trees by aligning lineage taxon strings
    • Louxin Zhang,
    • Niloufar Abhari,
    • Caroline Colijn,
    • and Yufeng Wu
    Genome Res. July 2023 33: 1053-1060; Published in Advance May 22, 2023, doi:10.1101/gr.277669.123
    ...and Semple 2019). Other desired properties of tree–child networks include the fact that all the tree–child networks are efficiently enumerated (Zhang 2019a,b; Cardona and Zhang 2020).ResultsWe mainly report a scalable computer program for inferring tree–child networks from multiple gene trees. The program...
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