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PAGER: constructing PAGs and new PAG–PAG relationships for network biology

In this article, we described a new database framework to perform integrative “gene-set, network, and pathway analysis” (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database a...

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Autores principales: Yue, Zongliang, Kshirsagar, Madhura M., Nguyen, Thanh, Suphavilai, Chayaporn, Neylon, Michael T., Zhu, Liugen, Ratliff, Timothy, Chen, Jake Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4553834/
https://www.ncbi.nlm.nih.gov/pubmed/26072489
http://dx.doi.org/10.1093/bioinformatics/btv265
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author Yue, Zongliang
Kshirsagar, Madhura M.
Nguyen, Thanh
Suphavilai, Chayaporn
Neylon, Michael T.
Zhu, Liugen
Ratliff, Timothy
Chen, Jake Y.
author_facet Yue, Zongliang
Kshirsagar, Madhura M.
Nguyen, Thanh
Suphavilai, Chayaporn
Neylon, Michael T.
Zhu, Liugen
Ratliff, Timothy
Chen, Jake Y.
author_sort Yue, Zongliang
collection PubMed
description In this article, we described a new database framework to perform integrative “gene-set, network, and pathway analysis” (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene–gene relationships and two types of 3 174 323 PAG–PAG regulatory relationships—co-membership based and regulatory relationship based. To help users assess each PAG’s biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG–PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/. Contact: jakechen@iupui.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-45538342015-09-02 PAGER: constructing PAGs and new PAG–PAG relationships for network biology Yue, Zongliang Kshirsagar, Madhura M. Nguyen, Thanh Suphavilai, Chayaporn Neylon, Michael T. Zhu, Liugen Ratliff, Timothy Chen, Jake Y. Bioinformatics Ismb/Eccb 2015 Proceedings Papers Committee July 10 to July 14, 2015, Dublin, Ireland In this article, we described a new database framework to perform integrative “gene-set, network, and pathway analysis” (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene–gene relationships and two types of 3 174 323 PAG–PAG regulatory relationships—co-membership based and regulatory relationship based. To help users assess each PAG’s biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG–PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/. Contact: jakechen@iupui.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-06-15 2015-06-10 /pmc/articles/PMC4553834/ /pubmed/26072489 http://dx.doi.org/10.1093/bioinformatics/btv265 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/licenses/by-nc/3.0/),which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Ismb/Eccb 2015 Proceedings Papers Committee July 10 to July 14, 2015, Dublin, Ireland
Yue, Zongliang
Kshirsagar, Madhura M.
Nguyen, Thanh
Suphavilai, Chayaporn
Neylon, Michael T.
Zhu, Liugen
Ratliff, Timothy
Chen, Jake Y.
PAGER: constructing PAGs and new PAG–PAG relationships for network biology
title PAGER: constructing PAGs and new PAG–PAG relationships for network biology
title_full PAGER: constructing PAGs and new PAG–PAG relationships for network biology
title_fullStr PAGER: constructing PAGs and new PAG–PAG relationships for network biology
title_full_unstemmed PAGER: constructing PAGs and new PAG–PAG relationships for network biology
title_short PAGER: constructing PAGs and new PAG–PAG relationships for network biology
title_sort pager: constructing pags and new pag–pag relationships for network biology
topic Ismb/Eccb 2015 Proceedings Papers Committee July 10 to July 14, 2015, Dublin, Ireland
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4553834/
https://www.ncbi.nlm.nih.gov/pubmed/26072489
http://dx.doi.org/10.1093/bioinformatics/btv265
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