ArticleCell2019
A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.
Article in Cell, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
133 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The genetic and biochemical determinants of mRNA degradation rates in mammals.Genome biology · 2022Pooled it
- LENG8 granule regulates alternative polyadenylation in mammals.Nature communications · 2026Article
- Leveraging long read RNA-seq to decipher neuronal regulation of alternative polyadenylation.Nature communications · 2026Article
- Promoter identity shapes splicing outcomes and fidelity.Nature communications · 2026Article
- The competition between splicing and 3' processing shapes the human transcriptome.Genes & development · 2026Article
- Machine learning-based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening.Biomedical reports · 2026Article
- Post-transcriptional regulatory networks: The dynamic interplay of RNA-binding proteins.The FEBS journal · 2026Review
- Article
- Unannotated noncoding transcripts as a source of intratumor heterogeneity in malignant cell states.Science China. Life sciences · 2026Article
- Decoding the language of messenger RNA.Nature methods · 2026Article
- Regulation of gene expression by alternative polyadenylation in health and disease.Nature reviews. Genetics · 2026Review
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Non-B DNA structures and their contributions to genetic diversity, aging, and disease.Nucleic acids research · 2026Review
- Article
- Accelerate the Highly Efficient Development of mRNA Vaccines Through Advanced Computational Methods.MedComm · 2026Review
- Decoding thebioRxiv : the preprint server for biology · 2026Article
- A recently evolved TAF8 isoform arising from anbioRxiv : the preprint server for biology · 2026Article
- TEITbase: a database for transposable element (TE)-initiated transcripts in human cancers.Database : the journal of biological databases and curation · 2026Article
- Design, Mechanisms, and Applications of DNA-Mediated Dynamically Reconfigurable Plasmonic Gold Nanostructures.Small methods · 2026Review
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
73 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over 3 million APA reporters. APARENT's predictions are highly accurate when tasked with inferring APA in synthetic and human 3'UTRs. Visualizing features learned across all network layers reveals that APARENT recognizes sequence motifs known to recruit APA regulators, discovers previously unknown sequence determinants of 3' end processing, and integrates these features into a comprehensive, interpretable, cis-regulatory code. We apply APARENT to forward engineer functional polyadenylation signals with precisely defined cleavage position and isoform usage and validate predictions experimentally. Finally, we use APARENT to quantify the impact of genetic variants on APA. Our approach detects pathogenic variants in a wide range of disease contexts, expanding our understanding of the genetic origins of disease.
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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.