AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model Article Swipe
Žiga Avsec
,
Natasha S. Latysheva
,
Jun Cheng
,
Guido Novati
,
Kyle R. Taylor
,
Tom Ward
,
Clare Bycroft
,
Lauren Nicolaisen
,
Eirini Arvaniti
,
Joshua Pan
,
Raina W. Thomas
,
Vincent Dutordoir
,
Matteo Perino
,
S. P. M. Boer De
,
Alexander Karollus
,
Adam Gayoso
,
Tobias Sargeant
,
Anne Mottram
,
Lai Hong Wong
,
Pavol Drotár
,
Adam R. Kosiorek
,
Andrew Senior
,
Richard Tanburn
,
Taylor Applebaum
,
Souradeep Basu
,
Demis Hassabis
,
Pushmeet Kohli
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.06.25.661532
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.06.25.661532
Deep learning models that predict functional genomic measurements from DNA sequence are powerful tools for deciphering the genetic regulatory code. Existing methods trade off between input sequence length and prediction resolution, thereby limiting their modality scope and performance. We present AlphaGenome, which takes as input 1 megabase of DNA sequence and predicts thousands of functional genomic tracks up to single base pair resolution across diverse modalities – including gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chro- matin contact maps, splice site usage, and splice junction coordinates and strength. Trained on human and mouse genomes, AlphaGenome matches or exceeds the strongest respective available external models on 24 out of 26 evaluations on variant effect prediction. AlphaGenome’s ability to simultaneously score variant effects across all modalities accurately recapitulates the mechanisms of clinically-relevant variants near the TAL1 oncogene. To facilitate broader use, we provide tools for making genome track and variant effect predictions from sequence.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.06.25.661532
- https://www.biorxiv.org/content/biorxiv/early/2025/06/27/2025.06.25.661532.full.pdf
- OA Status
- green
- Cited By
- 39
- References
- 100
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411721855
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411721855Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.06.25.661532Digital Object Identifier
- Title
-
AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence modelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-27Full publication date if available
- Authors
-
Žiga Avsec, Natasha S. Latysheva, Jun Cheng, Guido Novati, Kyle R. Taylor, Tom Ward, Clare Bycroft, Lauren Nicolaisen, Eirini Arvaniti, Joshua Pan, Raina W. Thomas, Vincent Dutordoir, Matteo Perino, S. P. M. Boer De, Alexander Karollus, Adam Gayoso, Tobias Sargeant, Anne Mottram, Lai Hong Wong, Pavol Drotár, Adam R. Kosiorek, Andrew Senior, Richard Tanburn, Taylor Applebaum, Souradeep Basu, Demis Hassabis, Pushmeet KohliList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.06.25.661532Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/06/27/2025.06.25.661532.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/06/27/2025.06.25.661532.full.pdfDirect OA link when available
- Concepts
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Sequence (biology), Computational biology, DNA sequencing, DNA, Computer science, Genetics, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
39Total citation count in OpenAlex
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-
2025: 38, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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100Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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