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

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/

Prostatic acid phosphatase is the main acid phosphatase with 5′-ectonucleotidase activity in the male mouse saliva and regulates salivation

“…Differentially expressed genes (DEGs) between PAP Ϫ/Ϫ and wild-type replicate samples were calculated using the median fold change (FC) value of the replicate pairs. DEGs with a Benjamini and Hochberg false discovery rate (FDR) corrected P value cutoff of 0.05 were selected for further analyses (13,39). Ontological analyses were performed using GoMiner software (65), and ontological groups with "P value changed" Ͻ 0.05 were considered as significant.…”

Identification of a c-Jun N-terminal kinase-2-dependent signal amplification cascade that regulates c-Myc levels in ras transformation

“…Chips were scanned with Illumina BeadArray Reader and the raw data were extracted with Bead Studio v3.0 (Illumina, San Diego, CA, USA). The gene microarray data analysis was carried out with the Anduril framework (Ovaska et al, 2010). The expression values were normalized using quantile normalization and the Illumina probe identifiers were annotated with the Ensembl mouse gene identifiers (v. 57; Hubbard et al, 2009; EMBL-EBI and Wellcome Trust Sanger Institute, Hinxton, UK).…”

Identification of genetic markers with synergistic survival effect in cancer

“…We have implemented Geninter so that it can be run as an individual program but also on Anduril bioinformatics workflow engine that allows advanced processing of the Geninter results, such as automated annotation ( e.g. , linkage disequilibrium (LD) mapping) from bio-databases [14]. …”