ArticlePloS one2019
Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- Using Deep Learning Models of Gene Regulation to Guide Drug Prioritization.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Using Deep Learning Models of Gene Regulation to Guide Drug Prioritization.bioRxiv : the preprint server for biology · 2026Article
- A foundation model for nucleotide sequences.Nucleic acids research · 2026Article
- Article
- Bridging the variant-to-function gap in type 2 diabetes: advances and challenges.Diabetologia · 2026Review
- Review
- Predicting gene expression from DNA sequence using deep learning models.Nature reviews. Genetics · 2025Review
- Lineage-specific regulatory evolution: insights from massively parallel reporter assays.Current opinion in genetics & development · 2025Review
- Iterative deep learning design of human enhancers exploits condensed sequence grammar to achieve cell-type specificity.Cell systems · 2025Article
- ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types.Genome biology · 2025Article
- Article
- Active learning of enhancers and silencers in the developing neural retina.Cell systems · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Automated Design of Oligopools and Rapid Analysis of Massively Parallel Barcoded Measurements.ACS synthetic biology · 2024Article
- Decoding biology with massively parallel reporter assays and machine learning.Genes & development · 2024Review
- Article
- Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers.Science (New York, N.Y.) · 2024Article
- Regression convolutional neural network models implicate peripheral immune regulatory variants in the predisposition to Alzheimer's disease.PLoS computational biology · 2024Article
- Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization.bioRxiv : the preprint server for biology · 2024Article
- Strategies for effectively modelling promoter-driven gene expression using transfer learning.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ∼500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ρ = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.