Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.

Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xin Wang, Mauro A Castro, Klaas W Mulder, Florian Markowetz
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/79ee6acd63d44cc3a060fcbb94e8580b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:79ee6acd63d44cc3a060fcbb94e8580b
record_format	dspace
spelling	oai:doaj.org-article:79ee6acd63d44cc3a060fcbb94e8580b2021-11-18T05:51:13ZPosterior association networks and functional modules inferred from rich phenotypes of gene perturbations.1553-734X1553-735810.1371/journal.pcbi.1002566https://doaj.org/article/79ee6acd63d44cc3a060fcbb94e8580b2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22761558/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the 'search space' for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs) to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package implementing PAN is freely available online at http://bioconductor.org/packages/release/bioc/html/PANR.html.Xin WangMauro A CastroKlaas W MulderFlorian MarkowetzPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 6, p e1002566 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Xin Wang Mauro A Castro Klaas W Mulder Florian Markowetz Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.
description	Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the 'search space' for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs) to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package implementing PAN is freely available online at http://bioconductor.org/packages/release/bioc/html/PANR.html.
format	article
author	Xin Wang Mauro A Castro Klaas W Mulder Florian Markowetz
author_facet	Xin Wang Mauro A Castro Klaas W Mulder Florian Markowetz
author_sort	Xin Wang
title	Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.
title_short	Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.
title_full	Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.
title_fullStr	Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.
title_full_unstemmed	Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.
title_sort	posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/79ee6acd63d44cc3a060fcbb94e8580b
work_keys_str_mv	AT xinwang posteriorassociationnetworksandfunctionalmodulesinferredfromrichphenotypesofgeneperturbations AT mauroacastro posteriorassociationnetworksandfunctionalmodulesinferredfromrichphenotypesofgeneperturbations AT klaaswmulder posteriorassociationnetworksandfunctionalmodulesinferredfromrichphenotypesofgeneperturbations AT florianmarkowetz posteriorassociationnetworksandfunctionalmodulesinferredfromrichphenotypesofgeneperturbations
_version_	1718424738516172800

Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.

Ejemplares similares