Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comment
. 2021 Oct;18(10):1155-1156.
doi: 10.1038/s41592-021-01259-4.

A wider field of view to predict expression

Yang Young Lu  1 , William Stafford Noble  2   3
Affiliations
Comment

A wider field of view to predict expression

Yang Young Lu et al. Nat Methods. 2021 Oct.

Abstract

A sequence-to-expression machine learning model achieves higher accuracy by incorporating information about potential long-range interactions.

PubMed Disclaimer

Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

Figure 1:
Figure 1:
Enformer uses a transformer deep learning architecture that is able to integrate information from up to 100 kb away in the genome. Transformers use an attention mechanism that associates each locus with all other positions in the sequence, enabling information to flow between distal elements and thus producing more accurate predictions. The model outputs thousands of predicted tracks, including transcription factor and histone ChIP-seq, DNase-seq, ATAC-seq, and CAGE data.
See this image and copyright information in PMC

Comment on

References

    1. Avsec Z, Agarwal V, Visentin D, Ledsam JR, Grabska-Barwinska A, Taylor KR, Assael Y, Jumper J, Kohli P, and Kelley DR. Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods, in press, 2021. - PMC - PubMed
    1. Kelley DR, Reshef YA, Bileschi M, Belanger D, McLean CY, and Snoek J. Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Research, 28(5):739–750, 2018. - PMC - PubMed
    1. Ouyang Z, Zhou Q, and Wong HW. ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells. Proceedings of the National Academy of Sciences of the United States of America, 106:21521–21526, 2009. - PMC - PubMed
    1. Karlic R, Chung HR, Lasserre J, Vlahovicek K, and Vingron M. Histone modification levels are predictive for gene expression. Proceedings of the National Academy of Sciences of the United States of America, 107(7):2926–2931, 2010. - PMC - PubMed
    1. Kelley DR, Snoek J, and Rinn JL . Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Research, 26(7):990–999, 2016. - PMC - PubMed

Publication types