An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.

Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGP...

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Autores principales: Shaun Mahony, Matthew D Edwards, Esteban O Mazzoni, Richard I Sherwood, Akshay Kakumanu, Carolyn A Morrison, Hynek Wichterle, David K Gifford
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Publicado: Public Library of Science (PLoS) 2014
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spelling oai:doaj.org-article:0289b6da2442497b8c2d4cd3352d835c2021-11-18T05:53:03ZAn integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.1553-734X1553-735810.1371/journal.pcbi.1003501https://doaj.org/article/0289b6da2442497b8c2d4cd3352d835c2014-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24675637/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery. We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes, sequence dependence, and replicate-specific noise sources. MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each event. MultiGPS's multi-experiment modeling approach thus provides a reliable platform for detecting differential binding enrichment across experimental conditions. We demonstrate the advantages of MultiGPS with an analysis of Cdx2 binding in three distinct developmental contexts. By accurately characterizing condition-specific Cdx2 binding, MultiGPS enables novel insight into the mechanistic basis of Cdx2 site selectivity. Specifically, the condition-specific Cdx2 sites characterized by MultiGPS are highly associated with pre-existing genomic context, suggesting that such sites are pre-determined by cell-specific regulatory architecture. However, MultiGPS-defined condition-independent sites are not predicted by pre-existing regulatory signals, suggesting that Cdx2 can bind to a subset of locations regardless of genomic environment. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2-5.Shaun MahonyMatthew D EdwardsEsteban O MazzoniRichard I SherwoodAkshay KakumanuCarolyn A MorrisonHynek WichterleDavid K GiffordPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 3, p e1003501 (2014)
institution DOAJ
collection DOAJ
language EN
topic Biology (General)
QH301-705.5
spellingShingle Biology (General)
QH301-705.5
Shaun Mahony
Matthew D Edwards
Esteban O Mazzoni
Richard I Sherwood
Akshay Kakumanu
Carolyn A Morrison
Hynek Wichterle
David K Gifford
An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.
description Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery. We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes, sequence dependence, and replicate-specific noise sources. MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each event. MultiGPS's multi-experiment modeling approach thus provides a reliable platform for detecting differential binding enrichment across experimental conditions. We demonstrate the advantages of MultiGPS with an analysis of Cdx2 binding in three distinct developmental contexts. By accurately characterizing condition-specific Cdx2 binding, MultiGPS enables novel insight into the mechanistic basis of Cdx2 site selectivity. Specifically, the condition-specific Cdx2 sites characterized by MultiGPS are highly associated with pre-existing genomic context, suggesting that such sites are pre-determined by cell-specific regulatory architecture. However, MultiGPS-defined condition-independent sites are not predicted by pre-existing regulatory signals, suggesting that Cdx2 can bind to a subset of locations regardless of genomic environment. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2-5.
format article
author Shaun Mahony
Matthew D Edwards
Esteban O Mazzoni
Richard I Sherwood
Akshay Kakumanu
Carolyn A Morrison
Hynek Wichterle
David K Gifford
author_facet Shaun Mahony
Matthew D Edwards
Esteban O Mazzoni
Richard I Sherwood
Akshay Kakumanu
Carolyn A Morrison
Hynek Wichterle
David K Gifford
author_sort Shaun Mahony
title An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.
title_short An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.
title_full An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.
title_fullStr An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.
title_full_unstemmed An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.
title_sort integrated model of multiple-condition chip-seq data reveals predeterminants of cdx2 binding.
publisher Public Library of Science (PLoS)
publishDate 2014
url https://doaj.org/article/0289b6da2442497b8c2d4cd3352d835c
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