Abstract
BackgroundCoordinated 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.MethodsWe 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.ResultsWe 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.ConclusionsOur 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/
Highlights
Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases
Full technical details of the framework together with worked examples are available in the Anduril User Guide [18]
Our results demonstrate that integrated data analysis combining amplification, expression, and methylation status is integral in order to draw conclusions about functional consequences of gene amplifications or deletions detected by comparative genomic hybridization array (aCGH) microarrays
Summary
Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. Comprehensive characterization of complex diseases calls for coordinated efforts to collect and share genome-scale data from large patient cohorts. Translating genome-scale data into knowledge and further to effective diagnosis, treatment and prevention strategies requires computational tools that are designed for large-scale data analysis as well as for the integration of multidimensional data with clinical parameters and knowledge available in bio-databases. In order to facilitate interpretation of the large-scale data analysis results, Anduril generates 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 views of genes, transcripts, genomic regions, protein-protein interactions and pathways
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