Deep learning for DNase I hypersensitive sites identification
Abstract Background The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets...
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Online Access: | http://link.springer.com/article/10.1186/s12864-018-5283-8 |
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doaj-e050cb42d96945bb95d0457dd1ddfa962020-11-25T01:28:22ZengBMCBMC Genomics1471-21642018-12-0119S1015516510.1186/s12864-018-5283-8Deep learning for DNase I hypersensitive sites identificationChuqiao Lyu0Lei Wang1Juhua Zhang2School of Life Science, Beijing Institute of TechnologySchool of Life Science, Beijing Institute of TechnologySchool of Life Science, Beijing Institute of TechnologyAbstract Background The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood. Methods Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens. Results Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens. Conclusions Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.http://link.springer.com/article/10.1186/s12864-018-5283-8DNase I hypersensitive sitesDeep learningConvolutional neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chuqiao Lyu Lei Wang Juhua Zhang |
spellingShingle |
Chuqiao Lyu Lei Wang Juhua Zhang Deep learning for DNase I hypersensitive sites identification BMC Genomics DNase I hypersensitive sites Deep learning Convolutional neural network |
author_facet |
Chuqiao Lyu Lei Wang Juhua Zhang |
author_sort |
Chuqiao Lyu |
title |
Deep learning for DNase I hypersensitive sites identification |
title_short |
Deep learning for DNase I hypersensitive sites identification |
title_full |
Deep learning for DNase I hypersensitive sites identification |
title_fullStr |
Deep learning for DNase I hypersensitive sites identification |
title_full_unstemmed |
Deep learning for DNase I hypersensitive sites identification |
title_sort |
deep learning for dnase i hypersensitive sites identification |
publisher |
BMC |
series |
BMC Genomics |
issn |
1471-2164 |
publishDate |
2018-12-01 |
description |
Abstract Background The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood. Methods Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens. Results Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens. Conclusions Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning. |
topic |
DNase I hypersensitive sites Deep learning Convolutional neural network |
url |
http://link.springer.com/article/10.1186/s12864-018-5283-8 |
work_keys_str_mv |
AT chuqiaolyu deeplearningfordnaseihypersensitivesitesidentification AT leiwang deeplearningfordnaseihypersensitivesitesidentification AT juhuazhang deeplearningfordnaseihypersensitivesitesidentification |
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1725102194356649984 |