![]() ![]() Whole genomes have been sequenced, global mRNA and miRNA levels assessed, protein expression measured, phosphorylation and methylation states assayed. Molecular biology has made great progress in recent years by measuring the abundance and characteristics of many kinds of molecules, often at a global level. Network visualizations - molecular maps - created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression. These activities will eventually permit the prediction and control of complex biological systems. Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. RCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand. The software architecture is based on the WSRF standard.Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Lower-level computation will be performed through MPIBLAST. The Grid Service under development will analyse requests based on the number of sequences, splitting them accordingly to the available resources. These are the infrastructures that could reach the largest number of resources and the best load balancing for data access. Many efforts have been done in the literature concerning the speeding up of Blast searches, but few of them deal with the use of large heterogeneous production Grid Infrastructures. A prototype has been conceived for the particular problem of speeding up the Blast searches to obtain fast results for large datasets. In order to be able to offer the possibility of an enhanced computational capacity within this bioinformatics application, a Grid component is being developed. The power and analytical potential of both annotation and function data-mining is somehow restricted to the computational power behind each particular installation. ![]() Typical B2G users are middle size genomics labs carrying out sequencing, ETS and microarray projects, handling datasets up to several thousand sequences. The application has been developed with the aim of affering an easy-to-use tool for functional genomics research. Blast2GO (B2G) is a bioinformatics tool for Gene Ontology-based DNA or protein sequence annotation and function-based data mining. The vast amount in complexity of data generated in Genomic Research implies that new dedicated and powerful computational tools need to be developed to meet their analysis requirements. ![]()
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