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With AlphaGenome, Researchers Are Using A.I. to Decode the Human Blueprint – The New York Times

January 28, 2026

https://www.nytimes.com/2026/01/28/science/alphagenome-ai-deepmind-genetics.html

QT:{{”
But Dr. Koo and other outside experts cautioned that it represented just one step on a long road ahead. “This is not AlphaFold, and it’s not going to win the Nobel Prize,” said Mark Gerstein, a computational biologist at Yale.

AlphaGenome will be useful. Dr. Gerstein said that he would probably add it to his toolbox for exploring DNA, and others expect to follow suit. But not all scientists trust A.I. programs like AlphaGenome to help them understand the genome.
…
In 2021, Dr. Avsec and his colleagues unveiled a preliminary A.I. called Enformer, which they have since expanded into AlphaGenome. They trained the program on an even greater expanse of biological data. “It’s really an industrial scale,” Dr. Gerstein said
“}}

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Tags: carlzimmer, enformer, entex, g0entex, x78qtcore

Nature medcine “A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s dis ease pathology”

January 26, 2026

QT:{{”
The DROP-AD project investigates the potential of dried plasma spot (DPS) and dried blood spot (DBS) analysis, derived from capillary blood, for detecting AD biomarkers, including phosphorylated tau at amino acid 217 (p-tau217), glial fibrillary acidic protein and neurofilament light. …. Similarly, we demonstrated the successful detection of glial fibrillary acidic protein and neurofilament light with strong correlations between DBS and DPS, respectively, using paired venous plasma samples.
“}}

Might find this paper very interesting. Just published this month in Nature Medicine. “A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s disease pathology.”

A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s disease pathology – Nature Medicine
https://www.nature.com/articles/s41591-025-04080-0

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Tags: AD, from, fromemail, RM

Xpresso

January 25, 2026

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.

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Tags: enformer

Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation | Nature Genetics

January 25, 2026

https://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

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Tags: enformer

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects | Nature Communications

January 25, 2026

https://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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Tags: enformer