Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.

Identifying transcription factors (TF) involved in producing a genome-wide transcriptional profile is an essential step in building mechanistic model that can explain observed gene expression data. We developed a statistical framework for constructing genome-wide signatures of TF activity, and for u...

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Autores principales:	Jing Chen, Zhen Hu, Mukta Phatak, John Reichard, Johannes M Freudenberg, Siva Sivaganesan, Mario Medvedovic
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c4c9bcad41d842abb5563bb115e9351a
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spelling	oai:doaj.org-article:c4c9bcad41d842abb5563bb115e9351a2021-11-18T05:53:37ZGenome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.1553-734X1553-735810.1371/journal.pcbi.1003198https://doaj.org/article/c4c9bcad41d842abb5563bb115e9351a2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24039560/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Identifying transcription factors (TF) involved in producing a genome-wide transcriptional profile is an essential step in building mechanistic model that can explain observed gene expression data. We developed a statistical framework for constructing genome-wide signatures of TF activity, and for using such signatures in the analysis of gene expression data produced by complex transcriptional regulatory programs. Our framework integrates ChIP-seq data and appropriately matched gene expression profiles to identify True REGulatory (TREG) TF-gene interactions. It provides genome-wide quantification of the likelihood of regulatory TF-gene interaction that can be used to either identify regulated genes, or as genome-wide signature of TF activity. To effectively use ChIP-seq data, we introduce a novel statistical model that integrates information from all binding "peaks" within 2 Mb window around a gene's transcription start site (TSS), and provides gene-level binding scores and probabilities of regulatory interaction. In the second step we integrate these binding scores and regulatory probabilities with gene expression data to assess the likelihood of True REGulatory (TREG) TF-gene interactions. We demonstrate the advantages of TREG framework in identifying genes regulated by two TFs with widely different distribution of functional binding events (ERα and E2f1). We also show that TREG signatures of TF activity vastly improve our ability to detect involvement of ERα in producing complex diseases-related transcriptional profiles. Through a large study of disease-related transcriptional signatures and transcriptional signatures of drug activity, we demonstrate that increase in statistical power associated with the use of TREG signatures makes the crucial difference in identifying key targets for treatment, and drugs to use for treatment. All methods are implemented in an open-source R package treg. The package also contains all data used in the analysis including 494 TREG binding profiles based on ENCODE ChIP-seq data. The treg package can be downloaded at http://GenomicsPortals.org.Jing ChenZhen HuMukta PhatakJohn ReichardJohannes M FreudenbergSiva SivaganesanMario MedvedovicPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 9, p e1003198 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jing Chen Zhen Hu Mukta Phatak John Reichard Johannes M Freudenberg Siva Sivaganesan Mario Medvedovic Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.
description	Identifying transcription factors (TF) involved in producing a genome-wide transcriptional profile is an essential step in building mechanistic model that can explain observed gene expression data. We developed a statistical framework for constructing genome-wide signatures of TF activity, and for using such signatures in the analysis of gene expression data produced by complex transcriptional regulatory programs. Our framework integrates ChIP-seq data and appropriately matched gene expression profiles to identify True REGulatory (TREG) TF-gene interactions. It provides genome-wide quantification of the likelihood of regulatory TF-gene interaction that can be used to either identify regulated genes, or as genome-wide signature of TF activity. To effectively use ChIP-seq data, we introduce a novel statistical model that integrates information from all binding "peaks" within 2 Mb window around a gene's transcription start site (TSS), and provides gene-level binding scores and probabilities of regulatory interaction. In the second step we integrate these binding scores and regulatory probabilities with gene expression data to assess the likelihood of True REGulatory (TREG) TF-gene interactions. We demonstrate the advantages of TREG framework in identifying genes regulated by two TFs with widely different distribution of functional binding events (ERα and E2f1). We also show that TREG signatures of TF activity vastly improve our ability to detect involvement of ERα in producing complex diseases-related transcriptional profiles. Through a large study of disease-related transcriptional signatures and transcriptional signatures of drug activity, we demonstrate that increase in statistical power associated with the use of TREG signatures makes the crucial difference in identifying key targets for treatment, and drugs to use for treatment. All methods are implemented in an open-source R package treg. The package also contains all data used in the analysis including 494 TREG binding profiles based on ENCODE ChIP-seq data. The treg package can be downloaded at http://GenomicsPortals.org.
format	article
author	Jing Chen Zhen Hu Mukta Phatak John Reichard Johannes M Freudenberg Siva Sivaganesan Mario Medvedovic
author_facet	Jing Chen Zhen Hu Mukta Phatak John Reichard Johannes M Freudenberg Siva Sivaganesan Mario Medvedovic
author_sort	Jing Chen
title	Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.
title_short	Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.
title_full	Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.
title_fullStr	Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.
title_full_unstemmed	Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.
title_sort	genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/c4c9bcad41d842abb5563bb115e9351a
work_keys_str_mv	AT jingchen genomewidesignaturesoftranscriptionfactoractivityconnectingtranscriptionfactorsdiseaseandsmallmolecules AT zhenhu genomewidesignaturesoftranscriptionfactoractivityconnectingtranscriptionfactorsdiseaseandsmallmolecules AT muktaphatak genomewidesignaturesoftranscriptionfactoractivityconnectingtranscriptionfactorsdiseaseandsmallmolecules AT johnreichard genomewidesignaturesoftranscriptionfactoractivityconnectingtranscriptionfactorsdiseaseandsmallmolecules AT johannesmfreudenberg genomewidesignaturesoftranscriptionfactoractivityconnectingtranscriptionfactorsdiseaseandsmallmolecules AT sivasivaganesan genomewidesignaturesoftranscriptionfactoractivityconnectingtranscriptionfactorsdiseaseandsmallmolecules AT mariomedvedovic genomewidesignaturesoftranscriptionfactoractivityconnectingtranscriptionfactorsdiseaseandsmallmolecules
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Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.

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