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DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning
Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic studies have mainly focused on the recognition of polyadenylation sites (PASs) in a given genomic sequence, which is a binary classification problem. Recently, computational methods for pre...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9801043/ https://www.ncbi.nlm.nih.gov/pubmed/33662629 http://dx.doi.org/10.1016/j.gpb.2020.05.004 |
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author | Li, Zhongxiao Li, Yisheng Zhang, Bin Li, Yu Long, Yongkang Zhou, Juexiao Zou, Xudong Zhang, Min Hu, Yuhui Chen, Wei Gao, Xin |
author_facet | Li, Zhongxiao Li, Yisheng Zhang, Bin Li, Yu Long, Yongkang Zhou, Juexiao Zou, Xudong Zhang, Min Hu, Yuhui Chen, Wei Gao, Xin |
author_sort | Li, Zhongxiao |
collection | PubMed |
description | Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic studies have mainly focused on the recognition of polyadenylation sites (PASs) in a given genomic sequence, which is a binary classification problem. Recently, computational methods for predicting the usage level of alternative PASs in the same gene have been proposed. However, all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into account. To address this, here we propose a deep learning architecture, Deep Regulatory Code and Tools for Alternative Polyadenylation (DeeReCT-APA), to quantitatively predict the usage of all alternative PASs of a given gene. To accommodate different genes with potentially different numbers of PASs, DeeReCT-APA treats the problem as a regression task with a variable-length target. Based on a convolutional neural network-long short-term memory (CNN-LSTM) architecture, DeeReCT-APA extracts sequence features with CNN layers, uses bidirectional LSTM to explicitly model the interactions among competing PASs, and outputs percentage scores representing the usage levels of all PASs of a gene. In addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene, we show that our method consistently outperforms other existing methods on three different tasks for which they are trained: pairwise comparison task, highest usage prediction task, and ranking task. Finally, we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA regulation. Our code and data are available at https://github.com/lzx325/DeeReCT-APA-repo. |
format | Online Article Text |
id | pubmed-9801043 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-98010432022-12-31 DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning Li, Zhongxiao Li, Yisheng Zhang, Bin Li, Yu Long, Yongkang Zhou, Juexiao Zou, Xudong Zhang, Min Hu, Yuhui Chen, Wei Gao, Xin Genomics Proteomics Bioinformatics Method Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic studies have mainly focused on the recognition of polyadenylation sites (PASs) in a given genomic sequence, which is a binary classification problem. Recently, computational methods for predicting the usage level of alternative PASs in the same gene have been proposed. However, all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into account. To address this, here we propose a deep learning architecture, Deep Regulatory Code and Tools for Alternative Polyadenylation (DeeReCT-APA), to quantitatively predict the usage of all alternative PASs of a given gene. To accommodate different genes with potentially different numbers of PASs, DeeReCT-APA treats the problem as a regression task with a variable-length target. Based on a convolutional neural network-long short-term memory (CNN-LSTM) architecture, DeeReCT-APA extracts sequence features with CNN layers, uses bidirectional LSTM to explicitly model the interactions among competing PASs, and outputs percentage scores representing the usage levels of all PASs of a gene. In addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene, we show that our method consistently outperforms other existing methods on three different tasks for which they are trained: pairwise comparison task, highest usage prediction task, and ranking task. Finally, we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA regulation. Our code and data are available at https://github.com/lzx325/DeeReCT-APA-repo. Elsevier 2022-06 2021-03-02 /pmc/articles/PMC9801043/ /pubmed/33662629 http://dx.doi.org/10.1016/j.gpb.2020.05.004 Text en © 2022 Beijing Institute of Genomics https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Method Li, Zhongxiao Li, Yisheng Zhang, Bin Li, Yu Long, Yongkang Zhou, Juexiao Zou, Xudong Zhang, Min Hu, Yuhui Chen, Wei Gao, Xin DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning |
title | DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning |
title_full | DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning |
title_fullStr | DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning |
title_full_unstemmed | DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning |
title_short | DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning |
title_sort | deerect-apa: prediction of alternative polyadenylation site usage through deep learning |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9801043/ https://www.ncbi.nlm.nih.gov/pubmed/33662629 http://dx.doi.org/10.1016/j.gpb.2020.05.004 |
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