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  1. Research: A transcriptome-wide systematic search does not detect A-to-I RNA editing in cis-antisense RNA duplexes
    • Zohar Rosenwasser,
    • Roni Cohen-Fultheim,
    • Ofir Shliefer,
    • Erez Y. Levanon,
    • and Eli Eisenberg
    Genome Res. March 12, 2026 ; Published in Advance March 12, 2026, doi:10.1101/gr.280820.125
    ...the possibility of dsRNA structures forming due to pairing of cisNAT transcripts, we first searched for genomic regions that potentially can give rise to cis-NAT editing. Our strategy involved two complementary approaches. First, we focused on identifying regions exhibiting extensive editing on at least one...
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  2. 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
    ..., or attention mechanisms, drive performance in these high-resolution predictions. To address these knowledge gaps, we systematically evaluate classic architectural choices and introduce ConvNeXt V2 blocks, originally developed for computer vision, as high-resolution feature extractors in deep learning models...
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  3. Method: Robust and efficient annotation of cell states through gene signature scoring
    • Laure Ciernik,
    • Agnieszka Kraft,
    • Florian Barkmann,
    • Josephine Yates,
    • and Valentina Boeva
    Genome Res. March 2026 36: 630-644; Published in Advance February 18, 2026, doi:10.1101/gr.280926.125
    ...of the score depends on how well the selected genes reflect the underlying biological process, regardless of the performance of the scoring method itself.Numerous techniques for gene signature scoring in bulk RNA-seq and scRNA-seq have been developed lately. Recent studies, however, showed that methods created...
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  4. Research: Stable genome structures in living fossil fishes
    • Cheng Wang,
    • Chase D. Brownstein,
    • Wenjun Chen,
    • Zufa Ding,
    • Dan Yu,
    • Yu Deng,
    • Chenguang Feng,
    • Thomas J. Near,
    • Shunping He,
    • and Liandong Yang
    Genome Res. February 2026 36: 318-329; Published in Advance January 13, 2026, doi:10.1101/gr.280800.125
    ...of the has contributed to the genomic stasis observed in Lepisosteidae. In other vertebrates, principally humans, epigenetic mechanisms (Slotkin and Martienssen 2007; Deniz et al. 2019) and microRNAs (Shalgi et al. 2010) appear to regulate TE proliferation and activity. In a sense, TE proliferation...
  5. 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
    ...Geometric deep learning framework for de novo assembly Lovro Vrček1,2, Xavier Bresson3, Thomas Laurent4, Martin Schmitz1,3, Kenji Kawaguchi3 and Mile Šikić1,2 1Genome Institute of Singapore, A*STAR, Singapore 138672; 2Faculty of Electrical Engineering and Computing, University of Zagreb, 10000...
  6. Review: De novo gene birth and the conundrum of ORFan genes in bacteria
    • Md. Hassan uz-Zaman and
    • Howard Ochman
    Genome Res. August 2025 35: 1679-1688; Published in Advance July 10, 2025, doi:10.1101/gr.280157.124
    ...non-coding sequence: the role of de novo protein-coding genes in eukaryotic evolutionary innovation. Philos Trans R Soc Lond B Biol Sci 370: 20140332. doi:10.1098/rstb.2014.0332 ↵McLysaght A, Hurst LD. 2016. Open questions in the study of de novo genes: what, how and why. Nat Rev Genet 17: 567...
  7. 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...
    OPEN ACCESS ARTICLE
  8. 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
    ..., a recently developed variant caller, SComatic, can detect somatic SNVs from scRNA-seq and scATAC-seq, which typically contain 1000s to 10,000s of cells (Muyas et al. 2024). Currently, no existing probabilistic cell lineage tree inference methods are capable of handling data sets of this size. Thus, a new...
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  9. 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
    ..., to examine these interlinked features jointly (Foord et al. 2023) by capturing genetic and transcriptomic variants at the single-cell level (Mansoori et al. 2017; Dagogo-Jack and Shaw 2018; Marine et al. 2020).Droplet-based scRNA-seq (e.g., 10x Genomics Chromium) can detect same-cell genetic...
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  10. Research: Loss of multilevel 3D genome organization during breast cancer progression
    • Roberto Rossini,
    • Saleh Oshaghi,
    • Maxim Nekrasov,
    • Aurélie Bellanger,
    • Renae Domaschenz,
    • Yasmin Dijkwel,
    • Mohamed Abdelhalim,
    • Rahul Agrawal,
    • Marit Ledsaak,
    • Philippe Collas,
    • Ragnhild Eskeland,
    • David Tremethick,
    • and Jonas Paulsen
    Genome Res. January 2026 36: 20-37; Published in Advance October 9, 2025, doi:10.1101/gr.280791.125
    ...six different genomic deletions spanning RE1–3 (Supplemental Fig. S48B) or sgRNAs for mCherry or EGFP as controls. The Cas9 protein was equally expressed from all vectors in C1 cells compared with GAPDH (Supplemental Fig. S48C). After 2 days of puromycin selection for Cas9-expressing C1 cells, we...
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