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New, in silico ways of generating hypotheses based on large data sets have emerged in the past decade. These data sets have been used to investigate different aspects of plant biology, especially at the level of transcriptome, from tissue-specific expression patterns to patterns in as little as a few cells. Such publicly available data are a boon to researchers for hypothesis generation by providing a guide for experimental work such as phenotyping or genetic analysis. More advanced computational methods can leverage these data via gene coexpression analysis, the results of which can be visualized and refined using network analysis. Other kinds of networks of, e.g., protein-protein interactions, can also be used to inform biology. These networks can be visualized and analyzed with additional information on gene expression levels, subcellular localization, etc., or with other emerging kinds information. Finally, cross-level correlation is an area that will become increasingly important. Visualizing these cross-level correlations will require new data visualization tools.
Pubmed ID: 23181065
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Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsData analysis service to predict the function of your favorite genes and gene sets. Indexing 1,421 association networks containing 266,984,699 interactions mapped to 155,238 genes from 7 organisms. GeneMANIA interaction networks are available for download in plain text format. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets. GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest. GeneMANIA is also a gene recommendation system. GeneMANIA is also accessible via a Cytoscape plugin, designed for power users. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
View all literature mentionsBioLayout Express3D is a powerful new tool for the visualization and analysis of networks derived from biological systems. Network-based approaches are becoming increasing popular for the analysis of ''omics and other high dimensional data. Networks can be produced from a wide variety of biological relationships, such as interactions between individuals, disease transmission, sequence similarity, metabolic pathways, protein interactions, pathways, regulatory cascades, gene expression, etc. BioLayout Express3D has been specifically designed for visualization, clustering and analysis of large network graphs in two- and three-dimensional space derived primarily, but not exclusively, from biological data. Sponsors: This resource is supported by BBSRC (BB / F003722 / 1) and the Wellcome Trust (GR077040RP). Keywords: Biology, Tool, Software, visualization, Analysis, Network, Biological, System, Dimentional, Data, Disease, Transmission, Sequence, Metabolic, Pathway, Protein, Interaction, Gene, Expression, Clustering, Analysis,
View all literature mentionsCSB.DB presents the results of bio-statistical analysis on gene expression data in association with additional biochemical and physiological knowledge. The main aim of this database platform is to provide tools that support insight into life''s complexity pyramid with a special focus on the integration of data from transcript and metabolite profiling experiments. The main focus of the CSB project is the generation of new easily accessible knowledge about the relationship and the hierarchy of cellular components. Thus new progress towards understanding lifes complexity pyramid is made. For this aim statistical and computational algorithms are applied to organism specific data derived from publicly available multi-parallel technologies, currently such as expression profiles. The underlying data are derived from various research activities. Thus CSB project provides an integrated and centralized public resource allowing universal access on the generated knowledge CSB.DB: A Comprehensive Systems-Biology Database. The derived knowledge should support the formulation of new hypotheses about the respective functional involvement of genes beyond their (inter-) relationships. Another major goal of the CSB project is to supply the researchers with necessary information to formulate these new hypotheses without demanding any a-priori statistical knowledge of the user. The CSB project mainly focuses on application of required statistical tests as well as to assist the user during exploration of results with information / help files to support hypothesis generation
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