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MONGKIE: an integrated tool for network analysis and visualization for multi-omics data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Yeongjun | - |
dc.contributor.author | Yu, Namhee | - |
dc.contributor.author | Seo, Jihae | - |
dc.contributor.author | Kim, Sun | - |
dc.contributor.author | Lee, Sanghyuk | - |
dc.date.accessioned | 2017-03-20T07:01:38Z | - |
dc.date.available | 2017-03-20T16:15:01Z | - |
dc.date.issued | 2016-03-18 | - |
dc.identifier.citation | Biology Direct, 11(1):10 | ko_KR |
dc.identifier.uri | https://hdl.handle.net/10371/109872 | - |
dc.description.abstract | 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 Maciej M Kańduła (nominated by Prof. David P Kreil). | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BioMed Central | ko_KR |
dc.subject | Network visualization | ko_KR |
dc.subject | Network modeling | ko_KR |
dc.subject | Graph clustering | ko_KR |
dc.subject | Omics data analysis | ko_KR |
dc.subject | Over-representation analysis | ko_KR |
dc.title | MONGKIE: an integrated tool for network analysis and visualization for multi-omics data | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 장영준 | - |
dc.contributor.AlternativeAuthor | 유남희 | - |
dc.contributor.AlternativeAuthor | 서지해 | - |
dc.contributor.AlternativeAuthor | 김선 | - |
dc.contributor.AlternativeAuthor | 이상혁 | - |
dc.language.rfc3066 | en | - |
dc.rights.holder | Jang et al. | - |
dc.date.updated | 2017-01-06T10:43:18Z | - |
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