Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks
Open accessPublished version of a preprint
Abstract
Algorithms that accurately predict gene structure from primary sequence alone were transformative for annotating the human genome. Can we also predict the expression levels of genes based solely on genome sequence? Here, we sought to apply deep convolutional neural networks toward that goal. Surprisingly, a model that includes only promoter sequences and features associated with mRNA stability explains 59% and 71% of variation in steady-state mRNA levels in human and mouse, respectively. This model, termed Xpresso, more than doubles the accuracy of alternative sequence-based models and isolates rules as predictive as models relying on chromatic immunoprecipitation sequencing (ChIP-seq) data. Xpresso recapitulates genome-wide patterns of transcriptional activity, and its residuals can be used to quantify the influence of enhancers, heterochromatic domains, and microRNAs. Model interpretation reveals that promoter-proximal CpG dinucleotides strongly predict transcriptional activity. Looking forward, we propose cell-type-specific gene-expression predictions based solely on primary sequences as a grand challenge for the field.
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Previous models have only attempted to work with RNA-seq after summarizing gene expression in a single statistic.
“Previous models have only attempted to work with RNA-seq after summarizing gene expression in a single statistic.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [11]
By processing a large region centered on the transcription start site, several models can predict normalized gene counts.
“By processing a large region centered on the transcription start site (TSS), several models can predict normalized gene counts11–13.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [11]
Deep convolutional neural networks (CNNs) achieve the current state of the art at predicting gene expression from DNA sequences for the human and mouse genomes.
“Deep convolutional neural networks (CNNs) achieve the current state of the art at predicting gene expression from DNA sequences for the human and mouse genomes1–4.”
From Effective gene expression prediction from sequence by integrating long-range interactions (2021) · cites this paper as [4]
Cites
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · see citing works
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · see citing works
Interpretation of Neural Networks Is Fragile
Ghorbani, A., Abid, A., & Zou, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013681 · Cited by 765 · see citing works
Systematic mapping of functional enhancer–promoter connections with CRISPR interference
Fulco, C. P., Munschauer, M., Anyoha, R., Munson, G., Grossman, S. R., Perez, E. M., Kane, M., Cleary, B., Lander, E. S., & Engreitz, J. M. (2016) · Science · doi:10.1126/science.aag2445 · Cited by 643 · see citing works
Thiol-linked alkylation for the metabolic sequencing of RNA
Herzog, V. A., Reichholf, B., Neumann, T., Rescheneder, P., Bhat, P., Burkard, T. R., Wlotzka, W., von Haeseler, A., Zuber, J., & Ameres, S. L. (2017) · doi:10.1101/177642 · Cited by 622 · see citing works
Sharova, L. V., Sharov, A. A., Nedorezov, T., Piao, Y., Shaik, N., & Ko, M. S. H. (2009) · DNA Research · doi:10.1093/dnares/dsn030 · Cited by 591 · see citing works
Klann, T. S., Black, J. B., Chellappan, M., Safi, A., Song, L., Hilton, I. B., Crawford, G. E., Reddy, T. E., & Gersbach, C. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3853 · Cited by 447 · see citing works
Denzler, R., McGeary, S. E., Title, A. C., Agarwal, V., Bartel, D. P., & Stoffel, M. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.09.027 · Cited by 340 · see citing works
Li, J. J., Bickel, P. J., & Biggin, M. D. (2014) · PeerJ · doi:10.7717/peerj.270 · Cited by 279 · see citing works
Decoding enhancers using massively parallel reporter assays
Inoue, F., & Ahituv, N. (2015) · Genomics · doi:10.1016/j.ygeno.2015.06.005 · Cited by 272 · see citing works
Active promoters give rise to false positive ‘Phantom Peaks’ in ChIP-seq experiments
Jain, D., Baldi, S., Zabel, A., Straub, T., & Becker, P. B. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv637 · Cited by 156 · see citing works
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Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021) · Nature Methods · doi:10.1038/s41592-021-01252-x · Cited by 1,326 · see citing works
Self-citationNucleotide Transformer: building and evaluating robust foundation models for human genomics
Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., Lopez Carranza, N., Grzywaczewski, A. H., Oteri, F., Dallago, C., Trop, E., de Almeida, B. P., Sirelkhatim, H., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02523-z · Cited by 656 · see citing works
Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation
Linder, J., Srivastava, D., Yuan, H., Agarwal, V., & Kelley, D. R. (2025) · Nature Genetics · doi:10.1038/s41588-024-02053-6 · Cited by 265 · see citing works
Self-citationZrimec, J., Börlin, C. S., Buric, F., Muhammad, A. S., Chen, R., Siewers, V., Verendel, V., Nielsen, J., Töpel, M., & Zelezniak, A. (2020) · Nature Communications · doi:10.1038/s41467-020-19921-4 · Cited by 174 · see citing works
Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure
Zrimec, J., Buric, F., Muhammad, A. S., Chen, R., Verendel, V., Töpel, M., & Zelezniak, A. (2019) · doi:10.1101/792531 · Cited by 173 · see citing works
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Agarwal, V., & Kelley, D. R. (2022) · Genome Biology · doi:10.1186/s13059-022-02811-x · Cited by 155 · see citing works
Self-citationHold out the genome: a roadmap to solving the cis-regulatory code
de Boer, C. G., & Taipale, J. (2024) · Nature · doi:10.1038/s41586-023-06661-w · Cited by 107 · see citing works
Li, J., Wang, J., Zhang, P., Wang, R., Mei, Y., Sun, Z., Fei, L., Jiang, M., Ma, L., E, W., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01197-7 · Cited by 57 · see citing works
Predicting the translation efficiency of messenger RNA in mammalian cells
Zheng, D., Persyn, L., Wang, J., Liu, Y., Ulloa-Montoya, F., Cenik, C., & Agarwal, V. (2026) · Nature Biotechnology · doi:10.1038/s41587-025-02712-x · Cited by 52 · see citing works
Self-citation
- Authors
- Agarwal, V., & Shendure, J.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Agarwal, Vikram
- Shendure, Jay
- Venue
- Cell Reports
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 31
- Issue
- 7
- Pages
- 107663
- ISSN
- 2211-1247 (ISSN-L)
- OpenAlex
- W3026475692
- Cited by
- 243
- References
- 13
- Citations by year
- 2026: 30 · 2025: 33 · 2024: 53 · 2023: 50 · 2022: 36 · 2021: 24 · 2020: 14 · 2019: 1 · 2018: 2
- FWCI
- 10.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Howard Hughes Medical Institute
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- promoter, mRNA stability, deep convolutional neural networks, CpG, enhancers, heterochromatin, microRNA, transcriptional activity, human and mouse genomes, cell type-specific gene expression
- MeSH terms
- Animals, Humans, Nerve Net, RNA, Messenger, Genomics, Mice
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