Cargando…
MONGKIE: an integrated tool for network analysis and visualization for multi-omics data
BACKGROUND: Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4797132/ https://www.ncbi.nlm.nih.gov/pubmed/26987515 http://dx.doi.org/10.1186/s13062-016-0112-y |
_version_ | 1782421893821759488 |
---|---|
author | Jang, Yeongjun Yu, Namhee Seo, Jihae Kim, Sun Lee, Sanghyuk |
author_facet | Jang, Yeongjun Yu, Namhee Seo, Jihae Kim, Sun Lee, Sanghyuk |
author_sort | Jang, Yeongjun |
collection | PubMed |
description | BACKGROUND: Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. RESULTS: Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. CONCLUSION: We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. REVIEWERS: This article was reviewed by Prof. Limsoon Wong, Prof. Soojin Yi, and Prof. David Kreil. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-016-0112-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4797132 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47971322016-03-18 MONGKIE: an integrated tool for network analysis and visualization for multi-omics data Jang, Yeongjun Yu, Namhee Seo, Jihae Kim, Sun Lee, Sanghyuk Biol Direct Application Note BACKGROUND: Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. RESULTS: Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. CONCLUSION: We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. REVIEWERS: This article was reviewed by Prof. Limsoon Wong, Prof. Soojin Yi, and Prof. David Kreil. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-016-0112-y) contains supplementary material, which is available to authorized users. BioMed Central 2016-03-18 /pmc/articles/PMC4797132/ /pubmed/26987515 http://dx.doi.org/10.1186/s13062-016-0112-y Text en © Jang et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Application Note Jang, Yeongjun Yu, Namhee Seo, Jihae Kim, Sun Lee, Sanghyuk MONGKIE: an integrated tool for network analysis and visualization for multi-omics data |
title | MONGKIE: an integrated tool for network analysis and visualization for multi-omics data |
title_full | MONGKIE: an integrated tool for network analysis and visualization for multi-omics data |
title_fullStr | MONGKIE: an integrated tool for network analysis and visualization for multi-omics data |
title_full_unstemmed | MONGKIE: an integrated tool for network analysis and visualization for multi-omics data |
title_short | MONGKIE: an integrated tool for network analysis and visualization for multi-omics data |
title_sort | mongkie: an integrated tool for network analysis and visualization for multi-omics data |
topic | Application Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4797132/ https://www.ncbi.nlm.nih.gov/pubmed/26987515 http://dx.doi.org/10.1186/s13062-016-0112-y |
work_keys_str_mv | AT jangyeongjun mongkieanintegratedtoolfornetworkanalysisandvisualizationformultiomicsdata AT yunamhee mongkieanintegratedtoolfornetworkanalysisandvisualizationformultiomicsdata AT seojihae mongkieanintegratedtoolfornetworkanalysisandvisualizationformultiomicsdata AT kimsun mongkieanintegratedtoolfornetworkanalysisandvisualizationformultiomicsdata AT leesanghyuk mongkieanintegratedtoolfornetworkanalysisandvisualizationformultiomicsdata |