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

Kristian Ovaska, Marko Laakso, Saija Haapa-Paananen, Riku Louhimo, Ping Chen, Viljami Aittomäki, Erkka Valo, Javier Núñez-Fontarnau, Ville Rantanen, Sirkku Karinen, Kari Nousiainen, Anna-Maria Lahesmaa-Korpinen, Minna Miettinen, Lilli Saarinen, Pekka Kohonen, Jianmin Wu, Jukka Westermarck, Sampsa Hautaniemi (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Original languageEnglish
Article number65
Number of pages12
JournalGenome Medicine
Volume2
Issue number9
DOIs
Publication statusPublished - 2010
MoE publication typeA1 Journal article-refereed

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Glioblastoma
Genes
Genetic Loci
Translating
Documentation
Cell Proliferation
Databases
Neoplasms

Cite this

Ovaska, K., Laakso, M., Haapa-Paananen, S., Louhimo, R., Chen, P., Aittomäki, V., ... Hautaniemi, S. (2010). Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme. Genome Medicine, 2(9), [65]. https://doi.org/10.1186/gm186
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. / Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme. In: Genome Medicine. 2010 ; Vol. 2, No. 9.
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title = "Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme",
abstract = "Background 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. Methods 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. Results 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. Conclusions 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.",
author = "Kristian Ovaska and Marko Laakso and Saija Haapa-Paananen and Riku Louhimo and Ping Chen and Viljami Aittom{\"a}ki and Erkka Valo and Javier N{\'u}{\~n}ez-Fontarnau and Ville Rantanen and Sirkku Karinen and Kari Nousiainen and Anna-Maria Lahesmaa-Korpinen and Minna Miettinen and Lilli Saarinen and Pekka Kohonen and Jianmin Wu and Jukka Westermarck and Sampsa Hautaniemi",
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Ovaska, K, Laakso, M, Haapa-Paananen, S, Louhimo, R, Chen, P, Aittomäki, V, Valo, E, Núñez-Fontarnau, J, Rantanen, V, Karinen, S, Nousiainen, K, Lahesmaa-Korpinen, A-M, Miettinen, M, Saarinen, L, Kohonen, P, Wu, J, Westermarck, J & Hautaniemi, S 2010, 'Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme', Genome Medicine, vol. 2, no. 9, 65. https://doi.org/10.1186/gm186

Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme. / 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 (Corresponding Author).

In: Genome Medicine, Vol. 2, No. 9, 65, 2010.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme

AU - Ovaska, Kristian

AU - Laakso, Marko

AU - Haapa-Paananen, Saija

AU - Louhimo, Riku

AU - Chen, Ping

AU - Aittomäki, Viljami

AU - Valo, Erkka

AU - Núñez-Fontarnau, Javier

AU - Rantanen, Ville

AU - Karinen, Sirkku

AU - Nousiainen, Kari

AU - Lahesmaa-Korpinen, Anna-Maria

AU - Miettinen, Minna

AU - Saarinen, Lilli

AU - Kohonen, Pekka

AU - Wu, Jianmin

AU - Westermarck, Jukka

AU - Hautaniemi, Sampsa

PY - 2010

Y1 - 2010

N2 - Background 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. Methods 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. Results 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. Conclusions 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.

AB - Background 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. Methods 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. Results 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. Conclusions 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.

U2 - 10.1186/gm186

DO - 10.1186/gm186

M3 - Article

VL - 2

JO - Genome Medicine

JF - Genome Medicine

SN - 1756-994X

IS - 9

M1 - 65

ER -

Ovaska K, Laakso M, Haapa-Paananen S, Louhimo R, Chen P, Aittomäki V et al. Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme. Genome Medicine. 2010;2(9). 65. https://doi.org/10.1186/gm186