Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme

Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualiz...

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Veröffentlicht in:Genome medicine 2010-09, Vol.2 (9), p.65-65, Article 65
Hauptverfasser: Ovaska, Kristian, Laakso, Marko, Haapa-Paananen, Saija, Louhimo, Riku, Chen, Ping, Aittomäki, Viljami, Valo, Erkka, Núñez-Fontarnau, Javier, Rantanen, Ville, Karinen, Sirkku, Nousiainen, Kari, Lahesmaa-Korpinen, Anna-Maria, Miettinen, Minna, Saarinen, Lilli, Kohonen, Pekka, Wu, Jianmin, Westermarck, Jukka, Hautaniemi, Sampsa
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container_end_page 65
container_issue 9
container_start_page 65
container_title Genome medicine
container_volume 2
creator Ovaska, Kristian
Laakso, Marko
Haapa-Paananen, Saija
Louhimo, Riku
Chen, Ping
Aittomäki, Viljami
Valo, Erkka
Núñez-Fontarnau, Javier
Rantanen, Ville
Karinen, Sirkku
Nousiainen, Kari
Lahesmaa-Korpinen, Anna-Maria
Miettinen, Minna
Saarinen, Lilli
Kohonen, Pekka
Wu, Jianmin
Westermarck, Jukka
Hautaniemi, Sampsa
description Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed. We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available. We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website. Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/
doi_str_mv 10.1186/gm186
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In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed. We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available. We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website. Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. 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subjects Analysis
Glioblastoma multiforme
Information management
title Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme
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