Predicting effects of noncoding variants with deep learning–based sequence model
Open accessAbstract
Identifying functional effects of noncoding variants is a major challenge in human genetics. To predict the noncoding-variant effects de novo from sequence, we developed a deep learning-based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. We further used this capability to improve prioritization of functional variants including expression quantitative trait loci (eQTLs) and disease-associated variants.
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The DeepSEA dataset is composed of 2.4 million sequences, each of size 1,000 nucleotides, and associated with 919 chromatin features, including 690 TF, 125 DNase and 104 histone features.
“The dataset is composed of 2.4 million sequences, each of size 1,000 nucleotides, and associated with 919 chromatin features. These include 690 TF, 125 DNase and 104 histone features.”
From Nucleotide Transformer: building and evaluating robust foundation models for human genomics (2025) · cites this paper as Zhou et al.10
Online Methods, “Data for training DeepSEA” (page not specified), says each sample is a 1,000-bp sequence paired with labels for 919 chromatin features. “Model design and training” gives the stated breakdown: 690 TF, 125 DNase and 104 histone features. The copy does not state that the dataset contains 2.4 million sequences; it describes 521,636,200 bp divided into 200-bp bins, with some chromosomes held out for testing. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThus 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 [5]
The paper describes a sequence-based model trained on TF-binding, DNase I–sensitivity, and histone-mark profiles (Abstract) and uses sequencing reads to assess allelic imbalance (Online Methods, ‘Evaluation of the single-nucleotide sensitivity of chromatin feature prediction’). It does not state that measured activity in these assays is proportional to local read counts, or establish that sequence-based models generally had focused on such assays. Its own training labels are binary peak-overlap labels (Online Methods, ‘Data for training DeepSEA’). (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 [5]
The Abstract describes DeepSEA, a deep-learning model that learns from large-scale chromatin-profiling data to predict effects of noncoding variants. This supports the broader point that machine learning can provide quantitative biological insight, but the paper does not describe coupling machine learning with massively parallel reporter assays. A search for “reporter” found no relevant passage. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn perturbation-based approaches, a portion of the input is changed and the effect of this change on the model output is observed.
“In the perturbation-based approaches, a portion of the input is changed and the effect of this change on the model output is observed [170–173].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [170]
Machine-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 [5]
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- Authors
- Zhou, J., & Troyanskaya, O. G.
- Affiliations
- Princeton University (US)
- Corresponding author
- Troyanskaya, Olga G
- Venue
- Nature Methods
- Published
- 2015-08-24
- Volume
- 12
- Issue
- 10
- Pages
- 931-934
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC4768299
- OpenAlex
- W2198606573
- Cited by
- 2,338
- References
- 26
- Citations by year
- 2026: 245 · 2025: 243 · 2024: 265 · 2023: 214 · 2022: 250 · 2021: 275 · 2020: 261 · 2019: 209 · 2018: 199 · 2017: 125 · 2016: 45 · 2015: 3 · 2014: 1 · 2013: 1
- FWCI
- 53.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- National Human Genome Research Institute (R01HG005998, T32HG003284)
- National Institute of General Medical Sciences (R01GM071966, P50GM071508)
- 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 › Genetics › Genomics and Rare Diseases
- Keywords
- non-coding variants, DeepSEA, chromatin profiling, variant prioritization, expression quantitative trait loci, disease-associated variants, deep learning
- MeSH terms
- Algorithms, Chromatin, Humans, Models, Genetic, Mutation, Regulatory Sequences, Nucleic Acid, Transcription Factors, Genome, Human, Polymorphism, Single Nucleotide, RNA, Untranslated, Quantitative Trait Loci, Hepatocyte Nuclear Factor 3-alpha, Epigenomics, Support Vector Machine
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