ArticleBioinformatics (Oxford, England)2019
DeeReCT-PolyA: a robust and generic deep learning method for PAS identification.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 55 citations in OpenAlex.
- scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.Briefings in bioinformatics · 2026Article
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
- Computational methods for alternative polyadenylation and splicing in post-transcriptional gene regulation.Experimental & molecular medicine · 2025Review
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Delineating yeast cleavage and polyadenylation signals using deep learning.Genome research · 2024Article
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- Accurate transcriptome-wide identification and quantification of alternative polyadenylation from RNA-seq data with APAIQ.Genome research · 2023Article
- A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq.Genomics, proteomics & bioinformatics · 2023Review
- Applications of deep learning in understanding gene regulation.Cell reports methods · 2023Review
- Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS.Genomics, proteomics & bioinformatics · 2022Article
- DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.Genomics, proteomics & bioinformatics · 2022Article
- From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome.Human genomics · 2022Review
- Protein-RNA interaction prediction with deep learning: structure matters.Briefings in bioinformatics · 2022Article
- Investigating the Genomic Background of CRISPR-Cas Genomes for CRISPR-Based Antimicrobials.Evolutionary bioinformatics online · 2022Article
- QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs.Genomics, proteomics & bioinformatics · 2021Article
- Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach.BMC bioinformatics · 2021Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species.PLoS computational biology · 2020Article
- Article
- DeepPASTA: deep neural network based polyadenylation site analysis.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationPolyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PASs) identification is not only desired for the purpose of better transcripts' end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif- and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals.
resultsIn this work, we propose a robust, PAS motif agnostic, and highly interpretable and transferrable deep learning model for accurate PAS recognition, which requires no prior knowledge or human-designed features. We show that our single model trained over all human PAS motifs not only outperforms the state-of-the-art methods trained on specific motifs, but can also be generalized well to two mouse datasets. Moreover, we further increase the prediction accuracy by transferring the deep learning model trained on the data of one species to the data of a different species. Several novel underlying poly(A) patterns are revealed through the visualization of important oligomers and positions in our trained models. Finally, we interpret the deep learning models by converting the convolutional filters into sequence logos and quantitatively compare the sequence logos between human and mouse datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/likesum/DeeReCT-PolyA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.