A deep learning framework combined with word embedding to identify DNA replication origins

Abstract The DNA replication influences the inheritance of genetic information in the DNA life cycle. As the distribution of replication origins (ORIs) is the major determinant to precisely regulate the replication process, the correct identification of ORIs is significant in giving an insightful un...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Feng Wu, Runtao Yang, Chengjin Zhang, Lina Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a3ea873f210f4f4ea4b3efa7c4d395c2
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a3ea873f210f4f4ea4b3efa7c4d395c2
record_format	dspace
spelling	oai:doaj.org-article:a3ea873f210f4f4ea4b3efa7c4d395c22021-12-02T14:12:09ZA deep learning framework combined with word embedding to identify DNA replication origins10.1038/s41598-020-80670-x2045-2322https://doaj.org/article/a3ea873f210f4f4ea4b3efa7c4d395c22021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80670-xhttps://doaj.org/toc/2045-2322Abstract The DNA replication influences the inheritance of genetic information in the DNA life cycle. As the distribution of replication origins (ORIs) is the major determinant to precisely regulate the replication process, the correct identification of ORIs is significant in giving an insightful understanding of DNA replication mechanisms and the regulatory mechanisms of genetic expressions. For eukaryotes in particular, multiple ORIs exist in each of their gene sequences to complete the replication in a reasonable period of time. To simplify the identification process of eukaryote’s ORIs, most of existing methods are developed by traditional machine learning algorithms, and target to the gene sequences with a fixed length. Consequently, the identification results are not satisfying, i.e. there is still great room for improvement. To break through the limitations in previous studies, this paper develops sequence segmentation methods, and employs the word embedding technique, ‘Word2vec’, to convert gene sequences into word vectors, thereby grasping the inner correlations of gene sequences with different lengths. Then, a deep learning framework to perform the ORI identification task is constructed by a convolutional neural network with an embedding layer. On the basis of the analysis of similarity reduction dimensionality diagram, Word2vec can effectively transform the inner relationship among words into numerical feature. For four species in this study, the best models are obtained with the overall accuracy of 0.975, 0.765, 0.885, 0.967, the Matthew’s correlation coefficient of 0.940, 0.530, 0.771, 0.934, and the AUC of 0.975, 0.800, 0.888, 0.981, which indicate that the proposed predictor has a stable ability and provide a high confidence coefficient to classify both of ORIs and non-ORIs. Compared with state-of-the-art methods, the proposed predictor can achieve ORI identification with significant improvement. It is therefore reasonable to anticipate that the proposed method will make a useful high throughput tool for genome analysis.Feng WuRuntao YangChengjin ZhangLina ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Feng Wu Runtao Yang Chengjin Zhang Lina Zhang A deep learning framework combined with word embedding to identify DNA replication origins
description	Abstract The DNA replication influences the inheritance of genetic information in the DNA life cycle. As the distribution of replication origins (ORIs) is the major determinant to precisely regulate the replication process, the correct identification of ORIs is significant in giving an insightful understanding of DNA replication mechanisms and the regulatory mechanisms of genetic expressions. For eukaryotes in particular, multiple ORIs exist in each of their gene sequences to complete the replication in a reasonable period of time. To simplify the identification process of eukaryote’s ORIs, most of existing methods are developed by traditional machine learning algorithms, and target to the gene sequences with a fixed length. Consequently, the identification results are not satisfying, i.e. there is still great room for improvement. To break through the limitations in previous studies, this paper develops sequence segmentation methods, and employs the word embedding technique, ‘Word2vec’, to convert gene sequences into word vectors, thereby grasping the inner correlations of gene sequences with different lengths. Then, a deep learning framework to perform the ORI identification task is constructed by a convolutional neural network with an embedding layer. On the basis of the analysis of similarity reduction dimensionality diagram, Word2vec can effectively transform the inner relationship among words into numerical feature. For four species in this study, the best models are obtained with the overall accuracy of 0.975, 0.765, 0.885, 0.967, the Matthew’s correlation coefficient of 0.940, 0.530, 0.771, 0.934, and the AUC of 0.975, 0.800, 0.888, 0.981, which indicate that the proposed predictor has a stable ability and provide a high confidence coefficient to classify both of ORIs and non-ORIs. Compared with state-of-the-art methods, the proposed predictor can achieve ORI identification with significant improvement. It is therefore reasonable to anticipate that the proposed method will make a useful high throughput tool for genome analysis.
format	article
author	Feng Wu Runtao Yang Chengjin Zhang Lina Zhang
author_facet	Feng Wu Runtao Yang Chengjin Zhang Lina Zhang
author_sort	Feng Wu
title	A deep learning framework combined with word embedding to identify DNA replication origins
title_short	A deep learning framework combined with word embedding to identify DNA replication origins
title_full	A deep learning framework combined with word embedding to identify DNA replication origins
title_fullStr	A deep learning framework combined with word embedding to identify DNA replication origins
title_full_unstemmed	A deep learning framework combined with word embedding to identify DNA replication origins
title_sort	deep learning framework combined with word embedding to identify dna replication origins
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a3ea873f210f4f4ea4b3efa7c4d395c2
work_keys_str_mv	AT fengwu adeeplearningframeworkcombinedwithwordembeddingtoidentifydnareplicationorigins AT runtaoyang adeeplearningframeworkcombinedwithwordembeddingtoidentifydnareplicationorigins AT chengjinzhang adeeplearningframeworkcombinedwithwordembeddingtoidentifydnareplicationorigins AT linazhang adeeplearningframeworkcombinedwithwordembeddingtoidentifydnareplicationorigins AT fengwu deeplearningframeworkcombinedwithwordembeddingtoidentifydnareplicationorigins AT runtaoyang deeplearningframeworkcombinedwithwordembeddingtoidentifydnareplicationorigins AT chengjinzhang deeplearningframeworkcombinedwithwordembeddingtoidentifydnareplicationorigins AT linazhang deeplearningframeworkcombinedwithwordembeddingtoidentifydnareplicationorigins
_version_	1718391802008961024

A deep learning framework combined with word embedding to identify DNA replication origins

Ejemplares similares