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A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules

Motivation: It is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of fun...

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Detalles Bibliográficos
Autores principales: Zhang, Shihua, Li, Qingjiao, Liu, Juan, Zhou, Xianghong Jasmine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117336/
https://www.ncbi.nlm.nih.gov/pubmed/21685098
http://dx.doi.org/10.1093/bioinformatics/btr206
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author Zhang, Shihua
Li, Qingjiao
Liu, Juan
Zhou, Xianghong Jasmine
author_facet Zhang, Shihua
Li, Qingjiao
Liu, Juan
Zhou, Xianghong Jasmine
author_sort Zhang, Shihua
collection PubMed
description Motivation: It is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of functional genomic data provides unprecedented opportunities to study the miRNA–gene regulation. A major challenge is how to integrate the diverse genomic data to identify the regulatory modules of miRNAs and genes. Results: Here we propose an effective data integration framework to identify the miRNA–gene regulatory comodules. The miRNA and gene expression profiles are jointly analyzed in a multiple non-negative matrix factorization framework, and additional network data are simultaneously integrated in a regularized manner. Meanwhile, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm. We apply the proposed method to integrate a set of heterogeneous data sources including the expression profiles of miRNAs and genes on 385 human ovarian cancer samples, computationally predicted miRNA–gene interactions, and gene–gene interactions. We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated. Moreover, the comodules are significantly enriched in known functional sets such as miRNA clusters, GO biological processes and KEGG pathways, respectively. Furthermore, many miRNAs and genes in the comodules are related with various cancers including ovarian cancer. Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics. Availability: The program and supplementary materials are available at http://zhoulab.usc.edu/SNMNMF/. Contact: xjzhou@usc.edu; zsh@amss.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-31173362011-06-17 A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules Zhang, Shihua Li, Qingjiao Liu, Juan Zhou, Xianghong Jasmine Bioinformatics Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria Motivation: It is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of functional genomic data provides unprecedented opportunities to study the miRNA–gene regulation. A major challenge is how to integrate the diverse genomic data to identify the regulatory modules of miRNAs and genes. Results: Here we propose an effective data integration framework to identify the miRNA–gene regulatory comodules. The miRNA and gene expression profiles are jointly analyzed in a multiple non-negative matrix factorization framework, and additional network data are simultaneously integrated in a regularized manner. Meanwhile, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm. We apply the proposed method to integrate a set of heterogeneous data sources including the expression profiles of miRNAs and genes on 385 human ovarian cancer samples, computationally predicted miRNA–gene interactions, and gene–gene interactions. We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated. Moreover, the comodules are significantly enriched in known functional sets such as miRNA clusters, GO biological processes and KEGG pathways, respectively. Furthermore, many miRNAs and genes in the comodules are related with various cancers including ovarian cancer. Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics. Availability: The program and supplementary materials are available at http://zhoulab.usc.edu/SNMNMF/. Contact: xjzhou@usc.edu; zsh@amss.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2011-07-01 2011-06-14 /pmc/articles/PMC3117336/ /pubmed/21685098 http://dx.doi.org/10.1093/bioinformatics/btr206 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria
Zhang, Shihua
Li, Qingjiao
Liu, Juan
Zhou, Xianghong Jasmine
A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules
title A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules
title_full A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules
title_fullStr A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules
title_full_unstemmed A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules
title_short A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules
title_sort novel computational framework for simultaneous integration of multiple types of genomic data to identify microrna-gene regulatory modules
topic Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117336/
https://www.ncbi.nlm.nih.gov/pubmed/21685098
http://dx.doi.org/10.1093/bioinformatics/btr206
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