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Research: Evolutionary dynamics of polyadenylation signals and their recognition strategies in protists
- Marcin P. Sajek,
- Danielle Y. Bilodeau,
- Michael A. Beer,
- Emma Horton,
- Yukiko Miyamoto,
- Katrina B. Velle,
- Lars Eckmann,
- Lillian Fritz-Laylin,
- Olivia S. Rissland,
- and Neelanjan Mukherjee
Genome Res. October 2024 34: 1570-1581; Published in Advance September 26, 2024, doi:10.1101/gr.279526.124
...the assembly of the CPA machinery on the poly(A) signal and direct the endonuclease CPSF73 for cleavage of the nascent RNA (Takagaki and Manley 1997; Hu et al. 2005; Mandel et al. 2006; Sullivan et al. 2009). In yeast, UA-rich elements located ∼40 nt upstream are bound by the cleavage and polyadenylation...
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Resource: Multitissue single-nucleus RNA-seq reveals cell type–specific regulatory patterns of alternative polyadenylation in pigs
- Qiuhan Wen,
- Zhen Wang,
- Qi Bao,
- Tianli Ding,
- Haihan Zhang,
- Jianbo Li,
- Zhuang Liu,
- Jieping Huang,
- and Guoqiang Yi
Genome Res. September 2025 35: 2116-2129; Published in Advance June 16, 2025, doi:10.1101/gr.280095.124
...is known to be recognized by the cleavage and polyadenylation specificity factor (CPSF) complex (Mitschka and Mayr 2022), which is a key component of the 3′-end processing machinery. The presence of these classical motifs supports the reliability of the predicted PASs.View larger version: In this window...
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Method: Iterative improvement of deep learning models using synthetic regulatory genomics
- André M. Ribeiro-dos-Santos and
- Matthew T. Maurano
Genome Res. November 2025 35: 2539-2549; Published in Advance October 22, 2025, doi:10.1101/gr.280540.125
...York University Grossman School of Medicine, New York, New York 10016, USA Corresponding author: maurano@nyu.eduAbstractDeep learning models can accurately reconstruct -wide epigenetic tracks from the reference sequence alone. But it is unclear what predictive power they have on sequence diverging from...
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Research: Endonucleolytic cleavage is the primary mechanism of decay elicited by C. elegans nonsense-mediated mRNA decay
- Marcus J. Viscardi,
- Enisha Sehgal,
- and Joshua A. Arribere
Genome Res. June 2025 35: 1337-1348; Published in Advance May 9, 2025, doi:10.1101/gr.280046.124
...to the -wide distribution and similar to unadapted normal mRNAs produced from the same genes. Thus, at (or immediately after) the time that cleavage occurs, NMD targets have a poly(A) tail length similar to that of non-NMD targets. Put another way, the existence of polyadenylated, cleaved NMD targets...
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Method: Probing the eukaryotic microbes of ruminants with a deep-learning classifier and comprehensive protein databases
- Ming Yan,
- Thea O. Andersen,
- Phillip B. Pope,
- and Zhongtang Yu
Genome Res. February 2025 35: 368-378; Published in Advance December 27, 2024, doi:10.1101/gr.279825.124
...of eukaryotes. We thus developed a specialized deep-learning classifier built upon diverse protozoal, fungal, and prokaryotic s to improve the classification of eukaryotic microbes in the rumen and gut. Leveraging thousands of prokaryotic, fungal, and protozoal s, we developed GutEuk, an ensembled deep-learning...
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Research: Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis
- Olga M. Sigalova,
- Mattia Forneris,
- Frosina Stojanovska,
- Bingqing Zhao,
- Rebecca R. Viales,
- Adam Rabinowitz,
- Fayrouz Hammal,
- Benoît Ballester,
- Judith B. Zaugg,
- and Eileen E.M. Furlong
Genome Res. May 2025 35: 1138-1153; Published in Advance April 15, 2025, doi:10.1101/gr.279652.124
...trained a deep neural network, which pinpointed the causal variant for strong effect variants with >90% accuracy. Taken together, this study provides a functional assessment of the sequence requirements for the occupancy of four essential regulators and identifies new dependency relationships...
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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
...at variant loci. We introduce Ralphi, a novel deep reinforcement learning framework for haplotype assembly, which integrates the representational power of deep learning with reinforcement learning to accurately partition read fragments into their respective haplotype sets. To set the reward objective...
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Method: Infernape uncovers cell type–specific and spatially resolved alternative polyadenylation in the brain
- Bowei Kang,
- Yalan Yang,
- Kaining Hu,
- Xiangbin Ruan,
- Yi-Lin Liu,
- Pinky Lee,
- Jasper Lee,
- Jingshu Wang,
- and Xiaochang Zhang
Genome Res. October 2023 33: 1774-1787; Published in Advance October 31, 2023, doi:10.1101/gr.277864.123
...and Manley 2017). Cleavage and polyadenylation (C/P) are regulated by cis-acting RNA sequences and their interaction with trans-acting C/P protein complexes (Shi 2012; Elkon et al. 2013; Gruber and Zavolan 2019; Mitschka and Mayr 2022). The cleavage and polyadenylation site (PA) is often a CA dinucleotide...
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Method: Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach
- Fabien Wehbe,
- Levi Adams,
- Jordan Babadoudou,
- Samantha Yuen,
- Yoon-Seong Kim,
- and Yoshiaki Tanaka
Genome Res. January 2025 35: 135-146; Published in Advance December 2, 2024, doi:10.1101/gr.278812.123
...disease mechanisms in humans. However, individual cells in patient-derived tissues are in different pathological stages, and hence, such cellular variability impedes subsequent differential gene expression analyses. To overcome such a heterogeneity issue, we present a novel deep learning approach, sc...
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Method: Modeling gene interactions in polygenic prediction via geometric deep learning
- Han Li,
- Jianyang Zeng,
- Michael P. Snyder,
- and Sai Zhang
Genome Res. January 2025 35: 178-187; Published in Advance November 19, 2024, doi:10.1101/gr.279694.124
...-Net, an interpretable geometric deep learning–based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the -wide PRS at the single-gene resolution and then explicitly encapsulates gene–gene interactions...
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