ArticleBMC genomics2017
Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Computational methods for alternative polyadenylation and splicing in post-transcriptional gene regulation.Experimental & molecular medicine · 2025Review
- InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data.Nature communications · 2024Article
- PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data.Cell reports methods · 2024Article
- 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
- APA-Scan: detection and visualization of 3'-UTR alternative polyadenylation with RNA-seq and 3'-end-seq data.BMC bioinformatics · 2022Article
- DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.Genomics, proteomics & bioinformatics · 2022Article
- Leveraging omic features with F3UTER enables identification of unannotated 3'UTRs for synaptic genes.Nature communications · 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
- Aptardi predicts polyadenylation sites in sample-specific transcriptomes using high-throughput RNA sequencing and DNA sequence.Nature communications · 2021Article
- Advances in the Bioinformatics Knowledge of mRNA Polyadenylation in Baculovirus Genes.Viruses · 2020Review
- Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA.Gene: X · 2020Article
- 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
- Sequence determinants of polyadenylation-mediated regulation.Genome research · 2019Article
- DeeReCT-PolyA: a robust and generic deep learning method for PAS identification.Bioinformatics (Oxford, England) · 2019Article
- DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions.Bioinformatics (Oxford, England) · 2019Article
- U1 snRNP Telescripting: Suppression of Premature Transcription Termination in Introns as a New Layer of Gene Regulation.Cold Spring Harbor perspectives in biology · 2019Review
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPolyadenylation is a critical stage of RNA processing during the formation of mature mRNA, and is present in most of the known eukaryote protein-coding transcripts and many long non-coding RNAs. The correct identification of poly(A) signals (PAS) not only helps to elucidate the 3'-end genomic boundaries of a transcribed DNA region and gene regulatory mechanisms but also gives insight into the multiple transcript isoforms resulting from alternative PAS. Although progress has been made in the in-silico prediction of genomic signals, the recognition of PAS in DNA genomic sequences remains a challenge.
resultsIn this study, we analyzed human genomic DNA sequences for the 12 most common PAS variants. Our analysis has identified a set of features that helps in the recognition of true PAS, which may be involved in the regulation of the polyadenylation process. The proposed features, in combination with a recognition model, resulted in a novel method and tool, Omni-PolyA. Omni-PolyA combines several machine learning techniques such as different classifiers in a tree-like decision structure and genetic algorithms for deriving a robust classification model. We performed a comparison between results obtained by state-of-the-art methods, deep neural networks, and Omni-PolyA. Results show that Omni-PolyA significantly reduced the average classification error rate by 35.37% in the prediction of the 12 considered PAS variants relative to the state-of-the-art results.
conclusionsThe results of our study demonstrate that Omni-PolyA is currently the most accurate model for the prediction of PAS in human and can serve as a useful complement to other PAS recognition methods. Omni-PolyA is publicly available as an online tool accessible at www.cbrc.kaust.edu.sa/omnipolya/ .
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