Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks
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Abstract
The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance—deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.
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Thus far, sequence-based models have focused on assays in which measured activity is proportional to local sequencing read counts.
“Thus far, such models have focused on assays in which measured activity is proportional to local sequencing read counts.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [6]
The Results section describes Basset as a sequence-based model of accessibility using DNase-seq data. But Methods (“DNase I hypersensitivity data”; page not provided) says its training labels were binary indicators of significant peaks, not activity values proportional to local sequencing read counts. The paper therefore supports the assay focus but not the claimed read-count relationship. (LLM, conf=0.84)
Experimental — accuracy not yet measuredExisting sequence-to-function methods involve a trade-off between input sequence length and prediction resolution, limiting their modality scope and performance.
“Existing methods involve a trade-off between input sequence length and prediction resolution, thereby limiting their modality scope and performance1–5.”
From Advancing regulatory variant effect prediction with AlphaGenome (2026) · cites this paper as [5]
The Discussion says prior sequence-kernel methods lose positional information, while position-specific kernels can suffer in predictive performance; it also says Basset offers finer nucleotide-level resolution than annotation-overlap approaches. It does not describe a trade-off between input sequence length and prediction resolution or say that such a trade-off limits modality scope. (LLM, conf=0.78)
Experimental — accuracy not yet measuredMassively parallel reporter assays coupled with machine learning have proven useful in addressing similar voids by producing quantitative biological insight that would be difficult to achieve through traditional approaches.
“Massively parallel reporter assays (MPRAs) – methods that assess thousands to millions of sequence variants in a single experiment – coupled with machine learning have proven useful in addressing similar voids by producing quantitative biological insight that would be difficult to achieve through traditional approaches3–9.”
From Human 5′ UTR design and variant effect prediction from a massively parallel translation assay (2019) · cites this paper as [8]
The Results section (‘In silico saturation mutagenesis’; page unavailable) describes parallel reporter assays as a way to measure the activity of many mutations, and presents Basset’s machine-learning predictions as an in silico approach to the same problem. It does not report coupling an MPRA with machine learning; Basset was trained on DNase-seq accessibility data, so the cited paper supports the broader point about quantitative insight but not the claimed combination. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMachine-learning models trained to predict function from DNA sequences have been successful at characterizing regulatory syntax and interpreting genetic variant effects.
“Machine-learning models trained to predict function from DNA sequences have been successful at characterizing regulatory syntax and interpreting genetic variant effects.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [6]
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“With the rapid growth in the availability of biological data, deep learning has also been extensively used in bioinformatics, 68–75 including transcription factor binding site prediction, 76 genome functional annotation, 77 and assessment of variant function.”
MethodDetecting genome-wide directional effects of transcription factor binding on polygenic disease risk
Reshef, Y. A., Finucane, H. K., Kelley, D. R., Gusev, A., Kotliar, D., Ulirsch, J. C., Hormozdiari, F., Nasser, J., O’Connor, L., van de Geijn, B., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0196-7 · Cited by 68 · see citing works
Self-citationLi, 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
Incorporating biological structure into machine learning models in biomedicine
Crawford, J., & Greene, C. S. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.12.021 · Cited by 41 · see citing works
Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting
Wei, J., Lotfy, P., Faizi, K., Baungaard, S., Gibson, E., Wang, E., Slabodkin, H., Kinnaman, E., Chandrasekaran, S., Kitano, H., et al. (2021) · doi:10.1101/2021.09.14.460134 · Cited by 67 · see citing works
A Review on Genomics Data Analysis using Machine Learning
Aggarwal, A. K. (2023) · WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE · doi:10.37394/23208.2023.20.12 · Cited by 17 · see citing works
TFXcan reveals transcription factor programs driving GWAS loci
Adeluwa, T., Salazar-Magaña, S., Sumner, S., Gona, S., Nyasimi, F., Carbonetto, P., Baca, S. C., Freedman, M., Gusev, A., Liu, B., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101398
- Authors
- Kelley, D. R., Snoek, J., & Rinn, J. L.
- Affiliations
- Harvard University (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2016-05-03
- Volume
- 26
- Issue
- 7
- Pages
- 990-999
- ISSN
- 1088-9051 (ISSN-L)
- 1549-5469
- PMC
- PMC4937568
- OpenAlex
- W2345512687
- Cited by
- 1,048
- References
- 11
- Citations by year
- 2026: 95 · 2025: 111 · 2024: 106 · 2023: 93 · 2022: 118 · 2021: 122 · 2020: 123 · 2019: 97 · 2018: 103 · 2017: 63 · 2016: 13 · 2013: 1
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Funders
- National Institute of Environmental Health Sciences (NIH) K25 (ES022984-02)
- National Institute of Mental Health (R01MH102416-03)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- Basset, deep convolutional neural networks, chromatin accessibility, DNase-seq, non-coding variants, GWAS SNPs, causal variants, linkage disequilibrium, variant effect prediction
- MeSH terms
- Base Sequence, Binding Sites, Humans, Models, Genetic, Linkage Disequilibrium, Consensus Sequence, Neural Networks, Computer, Sequence Analysis, DNA, Polymorphism, Single Nucleotide, Molecular Sequence Annotation, Support Vector Machine
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