Searching journal content for articles similar to Guo et al. 28 (6): 891.

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  1. Review: k-mer approaches for biodiversity genomics
    • Katharine M. Jenike,
    • Lucía Campos-Domínguez,
    • Marilou Boddé,
    • José Cerca,
    • Christina N. Hodson,
    • Michael C. Schatz,
    • and Kamil S. Jaron
    Genome Res. February 2025 35: 219-230; Published in Advance January 31, 2025, doi:10.1101/gr.279452.124
    ...process of dividing the analyzed sequences (e.g., raw reads or s) into a set of subsequences of length k, called k-mers, and then analyzing the frequency or sequences of those k-mers. Analyses based on k-mers allow for a rapid and intuitive assessment of complex sequencing data sets. Here, we provide...
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  2. Method: k-mer manifold approximation and projection for visualizing DNA sequences
    • Chengbo Fu,
    • Einari A. Niskanen,
    • Gong-Hong Wei,
    • Zhirong Yang,
    • Marta Sanvicente-García,
    • Marc Güell,
    • and Lu Cheng
    Genome Res. May 2025 35: 1234-1246; Published in Advance April 10, 2025, doi:10.1101/gr.279458.124
    ...for motif discovery, as they enable the efficient identification of conserved regions and regulatory elements across s. The Jellyfish algorithm, introduced by Marçais and Kingsford (2011), is a widely used k-mer counting tool that emphasizes the utility of k-mers in detecting frequent motifs and conserved...
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  3. Method: Memory-bound k-mer selection for large and evolutionarily diverse reference libraries
    • Ali Osman Berk Şapcı and
    • Siavash Mirarab
    Genome Res. September 2024 34: 1455-1467; Published in Advance August 29, 2024, doi:10.1101/gr.279339.124
    ...and scalability imposed by the need to subsample k-mers will be increasingly felt by developers of k-mer matching methods and those aiming to build ultra-large reference sets.For the k-mer-based methods to benefit from the ever-growing set of available s, we need improved methods of subsampling k-mers in a memory...
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  4. Method: Seamless, rapid, and accurate analyses of outbreak genomic data using split k-mer analysis
    • Romain Derelle,
    • Johanna von Wachsmann,
    • Tommi Mäklin,
    • Joel Hellewell,
    • Timothy Russell,
    • Ajit Lalvani,
    • Leonid Chindelevitch,
    • Nicholas J. Croucher,
    • Simon R. Harris,
    • and John A. Lees
    Genome Res. October 2024 34: 1661-1673; Published in Advance October 15, 2024, doi:10.1101/gr.279449.124
    ...and reference-based functional interpretation of variants needs to be kept, which we demonstrate in an example for recombination detection in the third section below. For large sample sets in which diversity may be larger, ska map remains scalable and robust.Tools to investigate shared k-mer content ska nk...
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  5. Method: Early feature extraction drives model performance in high-resolution chromatin accessibility prediction
    • Aayush Grover,
    • Till Muser,
    • Liine Kasak,
    • Lin Zhang,
    • Ekaterina Krymova,
    • and Valentina Boeva
    Genome Res. March 2026 36: 619-629; Published in Advance January 12, 2026, doi:10.1101/gr.281042.125
    ...for genomic data. Integrated into diverse architectures such as convoluted neural networks (CNNs), long short-term memory (LSTM), dilated CNNs, and transformers, ConvNeXt V2 blocks consistently improve performance, leading to similar prediction accuracy across these different model types. This reveals...
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  6. Method: Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet
    • Yidi Deng,
    • Jiadong Mao,
    • Jarny Choi,
    • and Kim-Anh Le Cao
    Genome Res. March 5, 2026 gr.281171.125; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
    ...), contributes to the local regression model that predict the expression of the response gene 83 within the cell’s KNN. Our KNN-PC regression approach efficiently captures coexpression at 84 scale. Rather than computing pairwise coexpression, it models a gene’s expression using a small 85 set of PCs, enabling...
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  7. Resource: Cell type–specific gene regulatory atlas prioritizes drug targets and repurposable medicines in Alzheimer's disease
    • Yunxiao Ren,
    • Ming Hu,
    • Yang E. Li,
    • Andrew A. Pieper,
    • Jeffrey Cummings,
    • and Feixiong Cheng
    Genome Res. March 2026 36: 645-659; Published in Advance January 21, 2026, doi:10.1101/gr.280436.125
    ...) using the addMotifAnnotations and peakAnnoEnrichment functions. Motif footprinting was further performed with getFootprints to evaluate motif accessibility and regulatory activity (Granja et al. 2021).Integration of snRNA-seq and snATAC-seq data setsIntegrated analysis of snATAC-seq and snRNA-seq data...
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  8. Method: Multisample motif discovery and visualization for tandem repeats
    • Yaran Zhang,
    • Marc Hulsman,
    • Alex Salazar,
    • Niccolò Tesi,
    • Lydian Knoop,
    • Sven van der Lee,
    • Sanduni Wijesekera,
    • Jana Krizova,
    • Erik-Jan Kamsteeg,
    • and Henne Holstege
    Genome Res. April 2025 35: 850-862; Published in Advance November 13, 2024, doi:10.1101/gr.279278.124
    ...representation of motif composition across sequences.AlgorithmMotif discovery and annotationMotifScope identifies repeat motifs across a given set of input sequences S = {s1, s2, …, sn} based on highly occurring k-mers (see Supplemental Algorithm 1 for implementation details). Due to the repetitive nature of TRs...
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  9. Method: Automated interpretable artificial intelligence genomic prediction with AIGP
    • Lei Wei,
    • Ziqin Jiang,
    • Baoliang Fan,
    • Yidan Yan,
    • Zhenqiang Xu,
    • Xiaoxiang Hu,
    • and Yuzhe Wang
    Genome Res. March 5, 2026 gr.281006.125; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
    ...algorithm tuning. Subsequently, the SHAP method was introduced to 93 assess the effects of genomic variants and generate effect distribution maps of SNPs at the 94 individual, site, and population levels. This study developed a comprehensive set of AI 95 prediction toolkits that integrate feature selection...
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  10. 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
    ...can improve the performance of predicting gene expression. In addition, ScPGE-KL outperformed ScPGE-LP when a smaller number of cCREs were used (e.g., cCRE-20), suggesting that the regulatory pattern of exponential decay enhances the model's ability to identify proximal cCRE–gene interactions...
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