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A Clustering Approach for Motif Discovery in ChIP-Seq Dataset

Chromatin immunoprecipitation combined with next-generation sequencing (ChIP-Seq) technology has enabled the identification of transcription factor binding sites (TFBSs) on a genome-wide scale. To effectively and efficiently discover TFBSs in the thousand or more DNA sequences generated by a ChIP-Se...

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Detalles Bibliográficos
Autores principales: Sun, Chun-xiao, Yang, Yu, Wang, Hua, Wang, Wen-hu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515331/
https://www.ncbi.nlm.nih.gov/pubmed/33267515
http://dx.doi.org/10.3390/e21080802
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author Sun, Chun-xiao
Yang, Yu
Wang, Hua
Wang, Wen-hu
author_facet Sun, Chun-xiao
Yang, Yu
Wang, Hua
Wang, Wen-hu
author_sort Sun, Chun-xiao
collection PubMed
description Chromatin immunoprecipitation combined with next-generation sequencing (ChIP-Seq) technology has enabled the identification of transcription factor binding sites (TFBSs) on a genome-wide scale. To effectively and efficiently discover TFBSs in the thousand or more DNA sequences generated by a ChIP-Seq data set, we propose a new algorithm named AP-ChIP. First, we set two thresholds based on probabilistic analysis to construct and further filter the cluster subsets. Then, we use Affinity Propagation (AP) clustering on the candidate cluster subsets to find the potential motifs. Experimental results on simulated data show that the AP-ChIP algorithm is able to make an almost accurate prediction of TFBSs in a reasonable time. Also, the validity of the AP-ChIP algorithm is tested on a real ChIP-Seq data set.
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spelling pubmed-75153312020-11-09 A Clustering Approach for Motif Discovery in ChIP-Seq Dataset Sun, Chun-xiao Yang, Yu Wang, Hua Wang, Wen-hu Entropy (Basel) Article Chromatin immunoprecipitation combined with next-generation sequencing (ChIP-Seq) technology has enabled the identification of transcription factor binding sites (TFBSs) on a genome-wide scale. To effectively and efficiently discover TFBSs in the thousand or more DNA sequences generated by a ChIP-Seq data set, we propose a new algorithm named AP-ChIP. First, we set two thresholds based on probabilistic analysis to construct and further filter the cluster subsets. Then, we use Affinity Propagation (AP) clustering on the candidate cluster subsets to find the potential motifs. Experimental results on simulated data show that the AP-ChIP algorithm is able to make an almost accurate prediction of TFBSs in a reasonable time. Also, the validity of the AP-ChIP algorithm is tested on a real ChIP-Seq data set. MDPI 2019-08-16 /pmc/articles/PMC7515331/ /pubmed/33267515 http://dx.doi.org/10.3390/e21080802 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Chun-xiao
Yang, Yu
Wang, Hua
Wang, Wen-hu
A Clustering Approach for Motif Discovery in ChIP-Seq Dataset
title A Clustering Approach for Motif Discovery in ChIP-Seq Dataset
title_full A Clustering Approach for Motif Discovery in ChIP-Seq Dataset
title_fullStr A Clustering Approach for Motif Discovery in ChIP-Seq Dataset
title_full_unstemmed A Clustering Approach for Motif Discovery in ChIP-Seq Dataset
title_short A Clustering Approach for Motif Discovery in ChIP-Seq Dataset
title_sort clustering approach for motif discovery in chip-seq dataset
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515331/
https://www.ncbi.nlm.nih.gov/pubmed/33267515
http://dx.doi.org/10.3390/e21080802
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