RCytoscape: tools for exploratory network analysis.

BACKGROUND: Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. 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. RESULTS: RCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance. CONCLUSIONS: Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. 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.

Integrated phosphoproteomics analysis of a signaling network governing nutrient response and peroxisome induction.

Phosphorylation of proteins is a key posttranslational modification in cellular signaling, regulating many aspects of cellular responses. We used a quantitative, integrated, phosphoproteomics approach to characterize the cellular responses of the yeast Saccharomyces cerevisiae to the fatty acid oleic acid, a molecule with broad human health implications and a potent inducer of peroxisomes. A combination of cryolysis and urea solubilization was used to minimize the opportunity for reorientation of the phosphoproteome, and hydrophilic interaction liquid chromatography and IMAC chemistries were used to fractionate and enrich for phosphopeptides. Using these approaches, numerous phosphorylated peptides specific to oleate-induced and glucose-repressed conditions were identified and mapped to known signaling pathways. These include several transcription factors, two of which, Pip2p and Cst6p, must be phosphorylated for the normal transcriptional response of fatty acid-responsive loci encoding peroxisomal proteins. The phosphoproteome data were integrated with results from genome-wide assays studying the effects of signaling molecule deletions and known protein-protein interactions to generate a putative fatty acid-responsive signaling network. In this network, the most highly connected nodes are those with the largest effects on cellular responses to oleic acid. These properties are consistent with a scale-free topology, demonstrating that scale-free properties are conserved in condition-specific networks.

Integrative network modeling reveals mechanisms underlying T cell exhaustion.

Failure to clear antigens causes CD8+ T cells to become increasingly hypo-functional, a state known as exhaustion. We combined manually extracted information from published literature with gene expression data from diverse model systems to infer a set of molecular regulatory interactions that underpin exhaustion. Topological analysis and simulation modeling of the network suggests CD8+ T cells undergo 2 major transitions in state following stimulation. The time cells spend in the earlier pro-memory/proliferative (PP) state is a fixed and inherent property of the network structure. Transition to the second state is necessary for exhaustion. Combining insights from network topology analysis and simulation modeling, we predict the extent to which each node in our network drives cells towards an exhausted state. We demonstrate the utility of our approach by experimentally testing the prediction that drug-induced interference with EZH2 function increases the proportion of pro-memory/proliferative cells in the early days post-activation.

PhosphoPep--a phosphoproteome resource for systems biology research in Drosophila Kc167 cells.

The ability to analyze and understand the mechanisms by which cells process information is a key question of systems biology research. Such mechanisms critically depend on reversible phosphorylation of cellular proteins, a process that is catalyzed by protein kinases and phosphatases. Here, we present PhosphoPep, a database containing more than 10 000 unique high-confidence phosphorylation sites mapping to nearly 3500 gene models and 4600 distinct phosphoproteins of the Drosophila melanogaster Kc167 cell line. This constitutes the most comprehensive phosphorylation map of any single source to date. To enhance the utility of PhosphoPep, we also provide an array of software tools that allow users to browse through phosphorylation sites on single proteins or pathways, to easily integrate the data with other, external data types such as protein-protein interactions and to search the database via spectral matching. Finally, all data can be readily exported, for example, for targeted proteomics approaches and the data thus generated can be again validated using PhosphoPep, supporting iterative cycles of experimentation and analysis that are typical for systems biology research.

The Firegoose: two-way integration of diverse data from different bioinformatics web resources with desktop applications.

BACKGROUND: Information resources on the World Wide Web play an indispensable role in modern biology. But integrating data from multiple sources is often encumbered by the need to reformat data files, convert between naming systems, or perform ongoing maintenance of local copies of public databases. Opportunities for new ways of combining and re-using data are arising as a result of the increasing use of web protocols to transmit structured data. RESULTS: The Firegoose, an extension to the Mozilla Firefox web browser, enables data transfer between web sites and desktop tools. As a component of the Gaggle integration framework, Firegoose can also exchange data with Cytoscape, the R statistical package, Multiexperiment Viewer (MeV), and several other popular desktop software tools. Firegoose adds the capability to easily use local data to query KEGG, EMBL STRING, DAVID, and other widely-used bioinformatics web sites. Query results from these web sites can be transferred to desktop tools for further analysis with a few clicks. Firegoose acquires data from the web by screen scraping, microformats, embedded XML, or web services. We define a microformat, which allows structured information compatible with the Gaggle to be embedded in HTML documents. We demonstrate the capabilities of this software by performing an analysis of the genes activated in the microbe Halobacterium salinarum NRC-1 in response to anaerobic environments. Starting with microarray data, we explore functions of differentially expressed genes by combining data from several public web resources and construct an integrated view of the cellular processes involved. CONCLUSION: The Firegoose incorporates Mozilla Firefox into the Gaggle environment and enables interactive sharing of data between diverse web resources and desktop software tools without maintaining local copies. Additional web sites can be incorporated easily into the framework using the scripting platform of the Firefox browser. Performing data integration in the browser allows the excellent search and navigation capabilities of the browser to be used in combination with powerful desktop tools.

The Gaggle: an open-source software system for integrating bioinformatics software and data sources.

BACKGROUND: Systems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of analysis, such as of microarrays, of metabolic networks and of predicted protein structure. A crucial challenge is to combine the capabilities of these (and other forthcoming) data resources and tools to create a data exploration and analysis environment that does justice to the variety and complexity of systems biology data sets. A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing. RESULTS: In this paper we describe the Gaggle -a simple, open-source Java software environment that helps to solve the problem of software and database integration. Guided by the classic software engineering strategy of separation of concerns and a policy of semantic flexibility, it integrates existing popular programs and web resources into a user-friendly, easily-extended environment. We demonstrate that four simple data types (names, matrices, networks, and associative arrays) are sufficient to bring together diverse databases and software. We highlight some capabilities of the Gaggle with an exploration of Helicobacter pylori pathogenesis genes, in which we identify a putative ricin-like protein -a discovery made possible by simultaneous data exploration using a wide range of publicly available data and a variety of popular bioinformatics software tools. CONCLUSION: We have integrated diverse databases (for example, KEGG, BioCyc, String) and software (Cytoscape, DataMatrixViewer, R statistical environment, and TIGR Microarray Expression Viewer). Through this loose coupling of diverse software and databases the Gaggle enables simultaneous exploration of experimental data (mRNA and protein abundance, protein-protein and protein-DNA interactions), functional associations (operon, chromosomal proximity, phylogenetic pattern), metabolic pathways (KEGG) and Pubmed abstracts (STRING web resource), creating an exploratory environment useful to 'web browser and spreadsheet biologists', to statistically savvy computational biologists, and those in between. The Gaggle uses Java RMI and Java Web Start technologies and can be found at http://gaggle.systemsbiology.net.

Exome sequencing identifies the cause of a mendelian disorder.

We demonstrate the first successful application of exome sequencing to discover the gene for a rare mendelian disorder of unknown cause, Miller syndrome (MIM%263750). For four affected individuals in three independent kindreds, we captured and sequenced coding regions to a mean coverage of 40x and sufficient depth to call variants at approximately 97% of each targeted exome. Filtering against public SNP databases and eight HapMap exomes for genes with two previously unknown variants in each of the four individuals identified a single candidate gene, DHODH, which encodes a key enzyme in the pyrimidine de novo biosynthesis pathway. Sanger sequencing confirmed the presence of DHODH mutations in three additional families with Miller syndrome. Exome sequencing of a small number of unrelated affected individuals is a powerful, efficient strategy for identifying the genes underlying rare mendelian disorders and will likely transform the genetic analysis of monogenic traits.

Analysis of genetic inheritance in a family quartet by whole-genome sequencing.

We analyzed the whole-genome sequences of a family of four, consisting of two siblings and their parents. Family-based sequencing allowed us to delineate recombination sites precisely, identify 70% of the sequencing errors (resulting in > 99.999% accuracy), and identify very rare single-nucleotide polymorphisms. We also directly estimated a human intergeneration mutation rate of approximately 1.1 x 10(-8) per position per haploid genome. Both offspring in this family have two recessive disorders: Miller syndrome, for which the gene was concurrently identified, and primary ciliary dyskinesia, for which causative genes have been previously identified. Family-based genome analysis enabled us to narrow the candidate genes for both of these Mendelian disorders to only four. Our results demonstrate the value of complete genome sequencing in families.