ArticleBioinformatics (Oxford, England)2019
DeepPASTA: deep neural network based polyadenylation site analysis.
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 29 papers.
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Who cites it
29 citing papers in PubMed, 61 citations in OpenAlex.
- Cross-domain signal grammars of transcription termination.Advanced biotechnology · 2026Review
- Benchmarking computational methods for identifying and quantifying polyadenylation sites from 3' tag-based single-cell RNA-seq data.Nucleic acids research · 2026Article
- PolyAseqTrap: a universal tool for genome-wide identification and quantification of polyadenylation sites from different 3' end sequencing data.Genome biology · 2026Article
- Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases.Computational and structural biotechnology journal · 2026Review
- Single-nucleus transcriptome atlas of orbitofrontal cortex in ALS with a deep learning-based decoding of alternative polyadenylation mechanisms.Cell genomics · 2025Article
- Alternative cleavage and polyadenylation: key regulatory mechanisms in health and disease.RNA biology · 2025Review
- RNA polymerase III transcription-associated polyadenylation promotes the accumulation of noncoding retrotransposons during infection.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation.Nature genetics · 2025Article
- Technological breakthroughs and advancements in the application of mRNA vaccines: a comprehensive exploration and future prospects.Frontiers in immunology · 2025Review
- Challenges in identifying mRNA transcript starts and ends from long-read sequencing data.Genome research · 2024Review
- Fundamentals for predicting transcriptional regulations from DNA sequence patterns.Journal of human genetics · 2024Review
- Delineating yeast cleavage and polyadenylation signals using deep learning.Genome research · 2024Article
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- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data.Nature communications · 2024Article
- Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease.Nature communications · 2023Article
- Alternative Transcripts Diversify Genome Function for Phenome Relevance to Health and Diseases.Genes · 2023Review
- A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq.Genomics, proteomics & bioinformatics · 2023Review
- Deciphering the impact of genetic variation on human polyadenylation using APARENT2.Genome biology · 2022Article
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Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
motivationAlternative polyadenylation (polyA) sites near the 3' end of a pre-mRNA create multiple mRNA transcripts with different 3' untranslated regions (3' UTRs). The sequence elements of a 3' UTR are essential for many biological activities such as mRNA stability, sub-cellular localization, protein translation, protein binding and translation efficiency. Moreover, numerous studies in the literature have reported the correlation between diseases and the shortening (or lengthening) of 3' UTRs. As alternative polyA sites are common in mammalian genes, several machine learning tools have been published for predicting polyA sites from sequence data. These tools either consider limited sequence features or use relatively old algorithms for polyA site prediction. Moreover, none of the previous tools consider RNA secondary structures as a feature to predict polyA sites.
resultsIn this paper, we propose a new deep learning model, called DeepPASTA, for predicting polyA sites from both sequence and RNA secondary structure data. The model is then extended to predict tissue-specific polyA sites. Moreover, the tool can predict the most dominant (i.e. frequently used) polyA site of a gene in a specific tissue and relative dominance when two polyA sites of the same gene are given. Our extensive experiments demonstrate that DeepPASTA signisficantly outperforms the existing tools for polyA site prediction and tissue-specific relative and absolute dominant polyA site prediction. AVAILABILITY AND IMPLEMENTATION: https://github.com/arefeen/DeepPASTA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.