Searching journal content for articles similar to Cuperus et al. 27 (12): 2015.

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  1. Method: Delineating yeast cleavage and polyadenylation signals using deep learning
    • Emily Kunce Stroup and
    • Zhe Ji
    Genome Res. July 2024 34: 1066-1080; Published in Advance June 24, 2024, doi:10.1101/gr.278606.123
    ...address this issue by developing deep learning models to deconvolute degenerate cis-regulatory elements and quantify their positional importance in mediating yeast poly(A) site formation, cleavage heterogeneity, and strength. In S. cerevisiae, cleavage heterogeneity is promoted by the depletion of U...
  2. Resource: Modeling transcriptional regulation of model species with deep learning
    • Evan M. Cofer,
    • João Raimundo,
    • Alicja Tadych,
    • Yuji Yamazaki,
    • Aaron K. Wong,
    • Chandra L. Theesfeld,
    • Michael S. Levine,
    • and Olga G. Troyanskaya
    Genome Res. June 2021 31: 1097-1105; Published in Advance April 22, 2021, doi:10.1101/gr.266171.120
    ..., Rosenberg AB, Jojic N, Fields S, Seelig G. 2017. Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences. Genome Res 27: 2015–2024. doi:10.1101/gr.224964.117 ↵Cusanovich DA, Reddington JP, Garfield DA, Daza RM, Aghamirzaie D, Marco-Ferreres R, Pliner HA...
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  3. Method: Long-read subcellular fractionation and sequencing reveals the translational fate of full-length mRNA isoforms during neuronal differentiation
    • Alexander J. Ritter,
    • Jolene M. Draper,
    • Christopher Vollmers,
    • and Jeremy R. Sanford
    Genome Res. November 2024 34: 2000-2011; Published in Advance June 5, 2024, doi:10.1101/gr.279170.124
    ...for the majority of differences between isoforms from the same gene. Random forest classifiers implicated coding sequence (CDS) and untranslated region (UTR) lengths as important determinants of isoform-specific sedimentation profiles, and motif analyses reveal potential cell type–specific and subcellular fraction...
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