A two-stream convolutional neural network for microRNA transcription start site feature integration and identification

Abstract MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation and phenotype development. Understanding the regulation of miRNA genes is critical to understand gene regulation. One of the challenges to study miRNA gene regulation is the lack of condition-specific annotation...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mingyu Cha, Hansi Zheng, Amlan Talukder, Clayton Barham, Xiaoman Li, Haiyan Hu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ccdabcc9c3b34b83b49ec129f7e069ba
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ccdabcc9c3b34b83b49ec129f7e069ba
record_format	dspace
spelling	oai:doaj.org-article:ccdabcc9c3b34b83b49ec129f7e069ba2021-12-02T15:54:02ZA two-stream convolutional neural network for microRNA transcription start site feature integration and identification10.1038/s41598-021-85173-x2045-2322https://doaj.org/article/ccdabcc9c3b34b83b49ec129f7e069ba2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85173-xhttps://doaj.org/toc/2045-2322Abstract MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation and phenotype development. Understanding the regulation of miRNA genes is critical to understand gene regulation. One of the challenges to study miRNA gene regulation is the lack of condition-specific annotation of miRNA transcription start sites (TSSs). Unlike protein-coding genes, miRNA TSSs can be tens of thousands of nucleotides away from the precursor miRNAs and they are hard to be detected by conventional RNA-Seq experiments. A number of studies have been attempted to computationally predict miRNA TSSs. However, high-resolution condition-specific miRNA TSS prediction remains a challenging problem. Recently, deep learning models have been successfully applied to various bioinformatics problems but have not been effectively created for condition-specific miRNA TSS prediction. Here we created a two-stream deep learning model called D-miRT for computational prediction of condition-specific miRNA TSSs ( http://hulab.ucf.edu/research/projects/DmiRT/ ). D-miRT is a natural fit for the integration of low-resolution epigenetic features (DNase-Seq and histone modification data) and high-resolution sequence features. Compared with alternative computational models on different sets of training data, D-miRT outperformed all baseline models and demonstrated high accuracy for condition-specific miRNA TSS prediction tasks. Comparing with the most recent approaches on cell-specific miRNA TSS identification using cell lines that were unseen to the model training processes, D-miRT also showed superior performance.Mingyu ChaHansi ZhengAmlan TalukderClayton BarhamXiaoman LiHaiyan HuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mingyu Cha Hansi Zheng Amlan Talukder Clayton Barham Xiaoman Li Haiyan Hu A two-stream convolutional neural network for microRNA transcription start site feature integration and identification
description	Abstract MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation and phenotype development. Understanding the regulation of miRNA genes is critical to understand gene regulation. One of the challenges to study miRNA gene regulation is the lack of condition-specific annotation of miRNA transcription start sites (TSSs). Unlike protein-coding genes, miRNA TSSs can be tens of thousands of nucleotides away from the precursor miRNAs and they are hard to be detected by conventional RNA-Seq experiments. A number of studies have been attempted to computationally predict miRNA TSSs. However, high-resolution condition-specific miRNA TSS prediction remains a challenging problem. Recently, deep learning models have been successfully applied to various bioinformatics problems but have not been effectively created for condition-specific miRNA TSS prediction. Here we created a two-stream deep learning model called D-miRT for computational prediction of condition-specific miRNA TSSs ( http://hulab.ucf.edu/research/projects/DmiRT/ ). D-miRT is a natural fit for the integration of low-resolution epigenetic features (DNase-Seq and histone modification data) and high-resolution sequence features. Compared with alternative computational models on different sets of training data, D-miRT outperformed all baseline models and demonstrated high accuracy for condition-specific miRNA TSS prediction tasks. Comparing with the most recent approaches on cell-specific miRNA TSS identification using cell lines that were unseen to the model training processes, D-miRT also showed superior performance.
format	article
author	Mingyu Cha Hansi Zheng Amlan Talukder Clayton Barham Xiaoman Li Haiyan Hu
author_facet	Mingyu Cha Hansi Zheng Amlan Talukder Clayton Barham Xiaoman Li Haiyan Hu
author_sort	Mingyu Cha
title	A two-stream convolutional neural network for microRNA transcription start site feature integration and identification
title_short	A two-stream convolutional neural network for microRNA transcription start site feature integration and identification
title_full	A two-stream convolutional neural network for microRNA transcription start site feature integration and identification
title_fullStr	A two-stream convolutional neural network for microRNA transcription start site feature integration and identification
title_full_unstemmed	A two-stream convolutional neural network for microRNA transcription start site feature integration and identification
title_sort	two-stream convolutional neural network for microrna transcription start site feature integration and identification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ccdabcc9c3b34b83b49ec129f7e069ba
work_keys_str_mv	AT mingyucha atwostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT hansizheng atwostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT amlantalukder atwostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT claytonbarham atwostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT xiaomanli atwostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT haiyanhu atwostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT mingyucha twostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT hansizheng twostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT amlantalukder twostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT claytonbarham twostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT xiaomanli twostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification AT haiyanhu twostreamconvolutionalneuralnetworkformicrornatranscriptionstartsitefeatureintegrationandidentification
_version_	1718385476543447040

A two-stream convolutional neural network for microRNA transcription start site feature integration and identification

Ejemplares similares