Anduril 2: upgraded large-scale data integration framework.

SUMMARY: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. AVAILABILITYAND IMPLEMENTATION: Freely available at http://anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Plasticity of blood- and lymphatic endothelial cells and marker identification.

The distinction between lymphatic and blood vessels is biologically fundamental. Here we wanted to rigorously analyze the universal applicability of vascular markers and characteristics of the two widely used vascular model systems human microvascular endothelial cell line-1 (HMEC-1) and telomerase-immortalized microvascular endothelial cell line (TIME). Therefore we studied the protein expression and functional properties of the endothelial cell lines HMEC-1 and TIME by flow cytometry and in vitro flow assays. We then performed microarray analyses of the gene expression in these two cell lines and compared them to primary endothelial cells. Using bioinformatics we then defined 39 new, more universal, endothelial-type specific markers from 47 primary endothelial microarray datasets and validated them using immunohistochemistry with normal and pathological tissues. We surprisingly found that both HMEC-1 and TIME are hybrid blood- and lymphatic cells. In addition, we discovered great discrepancies in the previous identifications of blood- and lymphatic endothelium-specific genes. Hence we identified and validated new, universally applicable vascular markers. Summarizing, the hybrid blood-lymphatic endothelial phenotype of HMEC-1 and TIME is indicative of plasticity in the gene expression of immortalized endothelial cell lines. Moreover, we identified new, stable, vessel-type specific markers for blood- and lymphatic endothelium, useful for basic research and clinical diagnostics.

Rule-based induction method for haplotype comparison and identification of candidate disease loci.

There is a need for methods that are able to identify rare variants that cause low or moderate penetrance disease susceptibility. To answer this need, we introduce a rule-based haplotype comparison method, Haplous, which identifies haplotypes within multiple samples from phased genotype data and compares them within and between sample groups. We demonstrate that Haplous is able to accurately identify haplotypes that are identical by descent, exclude common haplotypes in the studied population and select rare haplotypes from the data. Our analysis of three families with multiple individuals affected by lymphoma identified several interesting haplotypes shared by distantly related patients.

Data integration workflow for search of disease driving genes and genetic variants.

Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for (i) the p53 gene and (ii) three glioblastoma multiforme (GBM) associated risk genes. For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/

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

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/

Computational identification of cancer susceptibility loci.

The identification of novel cancer susceptibility syndromes and genes from very limited numbers of study individuals has become feasible through the use of high-throughput genotype microarrays. With such an approach, highly sensitive genome-wide computational methods are needed to identify the regions of interest. We have developed novel methods to identify and compare homozygous and compound heterozygous regions between cases and controls, to facilitate the identification of recessively inherited cancer susceptibility loci. As our approach is optimized for sensitivity, it creates many hits that may be unrelated to the phenotype of interest. We compensate for this compromised specificity by the automated use of additional sources of biological information along with a ranking function to focus on the most relevant regions. The methods are demonstrated here by comparing colorectal cancer patients to controls.

Early recovery from cow's milk allergy is associated with decreasing IgE and increasing IgG4 binding to cow's milk epitopes.

BACKGROUND: The dynamics and balance of allergen-specific IgE, IgG4, and IgA binding might contribute to the development of tolerance in patients with cow's milk allergy (CMA). Profiling of antibody binding to cow's milk (CM) protein epitopes might help in predicting the natural history of allergy. OBJECTIVE: We sought to investigate differences in IgE, IgG4, and IgA binding to CM epitopes over time between patients with early recovery or with persisting CMA. METHODS: We studied serum samples at the time of diagnosis (mean age, 7 months), 1 year later, and at follow-up (mean age, 8.6 years) from 11 patients with persisting IgE-mediated CMA at age 8 to 9 years and 12 patients who recovered by age 3 years. We measured the binding of IgE, IgG4, and IgA antibodies to sequential epitopes derived from 5 major CM proteins with a peptide microarray-based immunoassay. We analyzed the data with a novel image-processing method together with machine learning prediction. RESULTS: IgE epitope-binding patterns were stable over time in patients with persisting CMA, whereas binding decreased in patients who recovered early. Binding patterns of IgE and IgG4 overlapped. Among patients who recovered early, the signal of IgG4 binding increased and that of IgE decreased over time. IgE and IgG4 binding to a panel of alpha(s1)-, alpha(s2)-, beta-, and kappa-casein regions predicted outcome with significant accuracy. CONCLUSIONS: Attaining tolerance to CM is associated with decreased epitope binding by IgE and a concurrent increase in corresponding epitope binding by IgG4.

Combined T regulatory cell and Th2 expression profile identifies children with cow's milk allergy.

The role of T regulatory cells in spontaneous recovery from cow's milk allergy (CMA) is unclear. We investigated the mRNA expression of 12 T-cell markers and the protein expression of CD4, CD25, CD127, FoxP3 after in vitro beta-lactoglobulin stimulation of peripheral blood mononuclear cells from children with persisting CMA (n=16), early recovery (n=20) or no atopy (n=21). Artificial neural networks with exhaustive search for all marker combinations revealed that markers FoxP3, Nfat-C2, IL-16 and GATA-3 distinguished patients with persisting CMA most accurately from other study groups. FoxP3 mRNA expression following beta-lactoglobulin stimulation was highest in children with persisting CMA. Also the FoxP3 intensity in CD4(+) CD25(high)CD127(low) cells was higher in children with CMA compared with non-atopic children. The expression profile of both Th2- and T regulatory cell-related genes thus reflects the clinical activity of CMA. Tolerance, in contrast, is not characterized by activation of circulating T regulatory cells.