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Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. E. Ledsam, Agnieszka Grabska‐Barwińska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, David R. Kelley
How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend on improved solutions. Here, we report substantially improved gene expression prediction accuracy from DNA sequences through the use of a deep learning architecture, called Enformer, that is able to integrate information from long-range interactions (up to 100 kb away) in the genome. This improvement yielded more accurate variant effect predictions on gene expression for both natural genetic variants and saturation mutagenesis measured by massively parallel reporter assays. Furthermore, Enformer learned to predict enhancer–promoter interactions directly from the DNA sequence competitively with methods that take direct experimental data as input. We expect that these advances will enable more effective fine-mapping of human disease associations and provide a framework to interpret cis -regulatory evolution.
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Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R. E., Grabska‐Barwińska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021). Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods, 18(10), 1196–1203. https://doi.org/10.1038/s41592-021-01252-x
Avsec, Žiga, et al. "Effective gene expression prediction from sequence by integrating long-range interactions." Nature Methods, vol. 18, no. 10, 2021, pp. 1196–1203, https://doi.org/10.1038/s41592-021-01252-x.
Avsec, Žiga, Vikram Agarwal, Daniel Visentin, Joseph R. E. Ledsam, Agnieszka Grabska‐Barwińska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, and David R. Kelley. "Effective gene expression prediction from sequence by integrating long-range interactions." Nature Methods 18, no. 10 (2021): 1196–1203. https://doi.org/10.1038/s41592-021-01252-x.
Avsec Ž., Agarwal V., Visentin D. [и др.] Effective gene expression prediction from sequence by integrating long-range interactions // Nature Methods. — 2021. — Vol. 18, № 10. — P. 1196–1203. — URL: https://doi.org/10.1038/s41592-021-01252-x
@article{avsec2021effective,
title = {{Effective gene expression prediction from sequence by integrating long-range interactions}},
author = {Žiga Avsec and Vikram Agarwal and Daniel Visentin and Joseph R. E. Ledsam and Agnieszka Grabska‐Barwińska and Kyle R. Taylor and Yannis Assael and John Jumper and Pushmeet Kohli and David R. Kelley},
journal = {Nature Methods},
year = {2021},
volume = {18},
number = {10},
pages = {1196--1203},
doi = {10.1038/s41592-021-01252-x},
url = {https://doi.org/10.1038/s41592-021-01252-x}
}TY - JOUR AU - Avsec, Žiga AU - Agarwal, Vikram AU - Visentin, Daniel AU - Ledsam, Joseph R. E. AU - Grabska‐Barwińska, Agnieszka AU - Taylor, Kyle R. AU - Assael, Yannis AU - Jumper, John AU - Kohli, Pushmeet AU - Kelley, David R. TI - Effective gene expression prediction from sequence by integrating long-range interactions JO - Nature Methods VL - 18 IS - 10 SP - 1196 EP - 1203 PY - 2021 DA - 2021/10/01 DO - 10.1038/s41592-021-01252-x UR - https://doi.org/10.1038/s41592-021-01252-x ER -
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