WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data

Abstract Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, due to consideration of computational expense, most of existing DMD methods have to choose approximate schemes that greatly restrict the search space, leading to...

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Autores principales:	Hongbo Zhang, Lin Zhu, De-Shuang Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8549a77124914ef5abffcf118184cd6e
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id	oai:doaj.org-article:8549a77124914ef5abffcf118184cd6e
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spelling	oai:doaj.org-article:8549a77124914ef5abffcf118184cd6e2021-12-02T15:05:58ZWSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data10.1038/s41598-017-03554-72045-2322https://doaj.org/article/8549a77124914ef5abffcf118184cd6e2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03554-7https://doaj.org/toc/2045-2322Abstract Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, due to consideration of computational expense, most of existing DMD methods have to choose approximate schemes that greatly restrict the search space, leading to significant loss of predictive accuracy. In this paper, we propose Weakly-Supervised Motif Discovery (WSMD) to discover motifs from ChIP-seq datasets. In contrast to the learning strategies adopted by previous DMD methods, WSMD allows a “global” optimization scheme of the motif parameters in continuous space, thereby reducing the information loss of model representation and improving the quality of resultant motifs. Meanwhile, by exploiting the connection between DMD framework and existing weakly supervised learning (WSL) technologies, we also present highly scalable learning strategies for the proposed method. The experimental results on both real ChIP-seq datasets and synthetic datasets show that WSMD substantially outperforms former DMD methods (including DREME, HOMER, XXmotif, motifRG and DECOD) in terms of predictive accuracy, while also achieving a competitive computational speed.Hongbo ZhangLin ZhuDe-Shuang HuangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hongbo Zhang Lin Zhu De-Shuang Huang WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data
description	Abstract Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, due to consideration of computational expense, most of existing DMD methods have to choose approximate schemes that greatly restrict the search space, leading to significant loss of predictive accuracy. In this paper, we propose Weakly-Supervised Motif Discovery (WSMD) to discover motifs from ChIP-seq datasets. In contrast to the learning strategies adopted by previous DMD methods, WSMD allows a “global” optimization scheme of the motif parameters in continuous space, thereby reducing the information loss of model representation and improving the quality of resultant motifs. Meanwhile, by exploiting the connection between DMD framework and existing weakly supervised learning (WSL) technologies, we also present highly scalable learning strategies for the proposed method. The experimental results on both real ChIP-seq datasets and synthetic datasets show that WSMD substantially outperforms former DMD methods (including DREME, HOMER, XXmotif, motifRG and DECOD) in terms of predictive accuracy, while also achieving a competitive computational speed.
format	article
author	Hongbo Zhang Lin Zhu De-Shuang Huang
author_facet	Hongbo Zhang Lin Zhu De-Shuang Huang
author_sort	Hongbo Zhang
title	WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data
title_short	WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data
title_full	WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data
title_fullStr	WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data
title_full_unstemmed	WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data
title_sort	wsmd: weakly-supervised motif discovery in transcription factor chip-seq data
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/8549a77124914ef5abffcf118184cd6e
work_keys_str_mv	AT hongbozhang wsmdweaklysupervisedmotifdiscoveryintranscriptionfactorchipseqdata AT linzhu wsmdweaklysupervisedmotifdiscoveryintranscriptionfactorchipseqdata AT deshuanghuang wsmdweaklysupervisedmotifdiscoveryintranscriptionfactorchipseqdata
_version_	1718388654630502400

WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data

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