Searching journal content for articles similar to Teng et al. 34 (11): 1987.

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  1. Research: Nanopore direct RNA sequencing reveals METTL2A-mediated m3C sites in poly(A) RNA
    • Shuhei Mitsutomi,
    • Anzu Sugawara,
    • Masahide Seki,
    • Yutaka Suzuki,
    • Sotaro Miyao,
    • Haozhe Du,
    • Kenji Takahashi,
    • Yusuke Mizukami,
    • Kenzui Taniue,
    • and Nobuyoshi Akimitsu
    Genome Res. November 2025 35: 2406-2417; Published in Advance October 30, 2025, doi:10.1101/gr.280269.124
    ...junctions influence S100A4 isoform expressionRNA modifications are known to influence isoform expression, as demonstrated by METTL3, an m6A writer, which affects RNA splicing patterns (Xiao et al. 2016; Price et al. 2020). We investigated whether METTL2A-mediated m3C modification affects isoform expression...
  2. Method: Rapid and accurate demultiplexing of direct RNA nanopore sequencing data with SeqTagger
    • Leszek P. Pryszcz,
    • Gregor Diensthuber,
    • Laia Llovera,
    • Rebeca Medina,
    • Anna Delgado-Tejedor,
    • Luca Cozzuto,
    • Julia Ponomarenko,
    • and Eva Maria Novoa
    Genome Res. April 2025 35: 956-966; Published in Advance January 29, 2025, doi:10.1101/gr.279290.124
    ...and modifications by nanopore RNA sequencing. Nat Biotechnol 42: 72–86. doi:10.1038/s41587-023-01743-6 ↵Price AM, Hayer KE, McIntyre ABR, Gokhale NS, Abebe JS, Della Fera AN, Mason CE, Horner SM, Wilson AC, Depledge DP, et al. 2020. Direct RNA sequencing reveals m6A modifications on adenovirus RNA are necessary...
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  3. Research: Assessing the readiness of Oxford Nanopore sequencing for clinical genomics applications
    • Judith Arres,
    • Santosh Elavalli,
    • Shalini Behl,
    • Daniel Matias Sanchez,
    • Ayesha Al Ali,
    • Abdelrahman Ahmed Yehia Abdelaziz Saad,
    • Azza Attia,
    • Cyla Minas,
    • Sharika Pariyachery,
    • Shariq Ahmed,
    • Fatmah Aldhuhoori,
    • Nitu Thulasidharan,
    • Gurunath Katagi,
    • Omar Soliman,
    • Shilp Purohit,
    • Vinay Kusuma,
    • Raony Cardenas,
    • Thyago Cardoso,
    • Luis F. Paulin,
    • Philippe Sanio,
    • Joseph Mafofo,
    • Haiguo Wu,
    • Val Zvereff,
    • Albarah El-Khani,
    • Fahed Al Marzooqi,
    • Tiago R. Magalhães,
    • Fritz J. Sedlazeck,
    • and Javier Quilez
    Genome Res. March 2026 36: 460-471; Published in Advance February 11, 2026, doi:10.1101/gr.280134.124
    ...detection remains the main weakness inherent to the Nanopore sequencing technology (Amarasinghe et al. 2020), the new R10 chemistry marks a significant improvement, particularly in loci that are medically relevant and traditionally challenging for SRS. This improvement compounds with ONT's enhanced exon...
  4. Method: A new compression strategy to reduce the size of nanopore sequencing data
    • Kavindu Jayasooriya,
    • Sasha P. Jenner,
    • Pasindu Marasinghe,
    • Udith Senanayake,
    • Hassaan Saadat,
    • David Taubman,
    • Roshan Ragel,
    • Hasindu Gamaarachchi,
    • and Ira W. Deveson
    Genome Res. July 2025 35: 1574-1582; Published in Advance May 15, 2025, doi:10.1101/gr.280090.124
    ...Genom Bioinform 6: lqae052. doi:10.1093/nargab/lqae052 ↵Hendra C, Pratanwanich PN, Wan YK, Goh WSS, Thiery A, Göke J. 2022. Detection of m6A from direct RNA sequencing using a multiple instance learning framework. Nat Methods 19: 1590–1598. doi:10.1038/s41592-022-01666-1 ↵Illumina. 2014. Reducing whole...
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  5. Method: A versatile type VI CRISPR-based approach for targeted m6A demethylation in mRNAs
    • Panagiotis G. Adamopoulos,
    • Konstantina Athanasopoulou,
    • and Andreas Scorilas
    Genome Res. January 2026 36: 169-182; Published in Advance December 11, 2025, doi:10.1101/gr.280476.125
    ...had detectable methylation stoichiometry. To define high-confident m6A sites with negligible false-discovery rates (FDR; approximately zero), we used CHEUI, a two-stage neural network model trained to identify and quantify m6A from ionic currents derived from nanopore direct RNA sequencing (Acera...
  6. 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...
  7. Research: Enhanced detection of RNA modifications and read mapping with high-accuracy nanopore RNA basecalling models
    • Gregor Diensthuber,
    • Leszek P. Pryszcz,
    • Laia Llovera,
    • Morghan C. Lucas,
    • Anna Delgado-Tejedor,
    • Sonia Cruciani,
    • Jean-Yves Roignant,
    • Oguzhan Begik,
    • and Eva Maria Novoa
    Genome Res. November 2024 34: 1865-1877; Published in Advance September 13, 2024, doi:10.1101/gr.278849.123
    ...intensity signal and/or downstream signals are also caused by RNA modifications, providing a basis for modification detection with nanopore sequencing (Garalde et al. 2018; Workman et al. 2019). In the last few years, DRS has been successfully applied to detect diverse RNA modification types, including m6A...
  8. Research: Genetic regulation of nascent RNA maturation revealed by direct RNA nanopore sequencing
    • Karine Choquet,
    • Louis-Philippe Chaumont,
    • Simon Bache,
    • Autum R. Baxter-Koenigs,
    • and L. Stirling Churchman
    Genome Res. April 2025 35: 712-724; Published in Advance February 14, 2025, doi:10.1101/gr.279203.124
    ..., consistent with the conclusions from another independent study (Gohr et al. 2023). These findings suggest that the determinants of splicing order are hardcoded in the .Direct nanopore RNA sequencing (dnRNA-seq) (Garalde et al. 2018) yields long reads that allow for the simultaneous detection of several...
  9. Method: EnDeep4mC predicts DNA N4-methylcytosine sites using a dual-adaptive feature encoding framework in deep ensembles
    • Shuyu Zhang,
    • Quan Zou,
    • Mengting Niu,
    • Zhibin Lv,
    • Antony Stalin,
    • and Ximei Luo
    Genome Res. March 2026 36: 589-599; Published in Advance February 17, 2026, doi:10.1101/gr.280977.125
    ..., Powell DR, Akutsu T, Webb G, et al. 2020. Ilearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Brief Bioinform 21: 1047–1057. doi:10.1093/bib/bbz041 ↵Chen Z, Ni P, Wang JX. 2025. Identifying DNA methylation...
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  10. Method: Geometric deep learning framework for de novo genome assembly
    • Lovro Vrček,
    • Xavier Bresson,
    • Thomas Laurent,
    • Martin Schmitz,
    • Kenji Kawaguchi,
    • and Mile Šikić
    Genome Res. April 2025 35: 839-849; Published in Advance October 29, 2024, doi:10.1101/gr.279307.124
    ...parameters from scratch for a new set of s can be tedious. Here, learning-based methods facilitate transferability, as the new s can easily be introduced into the training set and the model can be retrained with minimal modifications to the framework.In this work, we borrow the terminology and the definition...
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