https://xpresso.gs.washington.edu/
Agarwal V, Shendure J. Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networks. 2020. Cell Reports 31 (7), 107663.
Posts Tagged ‘enformer’
Xpresso
January 25, 2026Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation | Nature Genetics
January 25, 2026https://github.com/calico/borzoi
N&V – https://www.nature.com/articles/s41588-025-02154-w
https://www.nature.com/articles/s41588-024-02053-6#:~:text=This%20paper%20proposes%20a%20new%20machine%2Dlearning%20model%2C,that%20drive%20RNA%20expression%20and%20post%2Dtranscriptional%20regulation QT:{{” ere, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence. “}}
Linder, J., Srivastava, D., Yuan, H., Agarwal, V., & Kelley, D. R. (2025). Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nature Genetics, 57(4), 949–961.
https://doi.org/10.1038/s41588-024-02053-6
Predicting cell type-specific epigenomic profiles accounting for distal genetic effects | Nature Communications
January 25, 2026https://www.nature.com/articles/s41467-024-54441-5
QT:{{” Enformer Celltyping can predict in new cell types, imputing their epigenetic signal, by embedding global and local chromatin accessibility (ATAC-Seq) signals for the cell type of interest. “}}
Murphy, A. E., Beardall, W., Rei, M., Phuycharoen, M., & Skene, N. G. (2024). Predicting cell type-specific epigenomic profiles accounting for distal genetic effects. Nature Communications, 15(1), 9951. https://doi.org/10.1038/s41467-024-54441-5
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