ArticleRNA (New York, N.Y.)2016
Poly(A) code analyses reveal key determinants for tissue-specific mRNA alternative polyadenylation.
Article in RNA (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq.Genomics, proteomics & bioinformatics · 2023Review
- SRSF3 and SRSF7 modulate 3'UTR length through suppression or activation of proximal polyadenylation sites and regulation of CFIm levels.Genome biology · 2021Article
- 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
- DeepPASTA: deep neural network based polyadenylation site analysis.Bioinformatics (Oxford, England) · 2019Article
- Cluster analysis of replicated alternative polyadenylation data using canonical correlation analysis.BMC genomics · 2019Article
- Inference of the human polyadenylation code.Bioinformatics (Oxford, England) · 2018Article
- QAPA: a new method for the systematic analysis of alternative polyadenylation from RNA-seq data.Genome biology · 2018Article
- Molecular Mechanisms for CFIm-Mediated Regulation of mRNA Alternative Polyadenylation.Molecular cell · 2018Article
- Comparative analysis of alternative polyadenylation inGenome research · 2017Article
- Article
- Nonsense in the testis: multiple roles for nonsense-mediated decay revealed in male reproduction.Biology of reproduction · 2017Review
- The roles of RNA processing in translating genotype to phenotype.Nature reviews. Molecular cell biology · 2017Review
- The Advances in Deep Learning Modeling of Polyadenylation Codes.Wiley interdisciplinary reviews. RNAReview
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Authors and funding
4 authors.
Funding
Abstract
mRNA alternative polyadenylation (APA) is a critical mechanism for post-transcriptional gene regulation and is often regulated in a tissue- and/or developmental stage-specific manner. An ultimate goal for the APA field has been to be able to computationally predict APA profiles under different physiological or pathological conditions. As a first step toward this goal, we have assembled a poly(A) code for predicting tissue-specific poly(A) sites (PASs). Based on a compendium of over 600 features that have known or potential roles in PAS selection, we have generated and refined a machine-learning algorithm using multiple high-throughput sequencing-based data sets of tissue-specific and constitutive PASs. This code can predict tissue-specific PASs with >85% accuracy. Importantly, by analyzing the prediction performance based on different RNA features, we found that PAS context, including the distance between alternative PASs and the relative position of a PAS within the gene, is a key feature for determining the susceptibility of a PAS to tissue-specific regulation. Our poly(A) code provides a useful tool for not only predicting tissue-specific APA regulation, but also for studying its underlying molecular mechanisms.
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Registered trials
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