Introduction

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.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/

Publications

  1. Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme.
    Cite this
    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 AM, Miettinen M, Saarinen L, Kohonen P, Wu J, Westermarck J, Hautaniemi S, 2010-01-01 - Genome medicine

Credits

  1. Kristian Ovaska
    Developer

    Computational Systems Biology Laboratory, Institute of Biomedicine and Genome-Scale Biology Research Program

  2. Marko Laakso
    Developer

  3. Saija Haapa-Paananen
    Developer

  4. Riku Louhimo
    Developer

  5. Ping Chen
    Developer

  6. Viljami Aittomäki
    Developer

  7. Erkka Valo
    Developer

  8. Javier Núñez-Fontarnau
    Developer

  9. Ville Rantanen
    Developer

  10. Sirkku Karinen
    Developer

  11. Kari Nousiainen
    Developer

  12. Anna-Maria Lahesmaa-Korpinen
    Developer

  13. Minna Miettinen
    Developer

  14. Lilli Saarinen
    Developer

  15. Pekka Kohonen
    Developer

  16. Jianmin Wu
    Developer

  17. Jukka Westermarck
    Developer

  18. Sampsa Hautaniemi
    Investigator

Community Ratings

UsabilityEfficiencyReliabilityRated By
0 user
Sign in to rate
Summary
AccessionBT006237
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesPerl, R
User InterfaceTerminal Command Line
Download Count0
Submitted BySampsa Hautaniemi