Impressive quantities of experimental data are being obtained and computational tools are essential to integrate and to organize this information. This paper presents Protopia, a biological tool that offers a way of searching for proteins and their interactions in different Protein Interaction Web Databases, as a part of a multidisciplinary initiative of our institution for the integration of biological data The tool accesses the different Databases , and results are expressed with biological protein names or databases codes and can be depicted as a vector or a matrix. They can be represented and handled interactively as an organic graph. Comparison among databases is carried out using the Uniprot codes annotated for each protein.The tool locates and integrates the current information stored in the aforementioned databases, and redundancies among them are detected. Results are compatible with the most important network analysers, so that they can be compared and analysed by other world-wide known tools and platforms. The visualization possibilities help to attain this goal and they are especially interesting for handling multiple-step or complex networks. A living organism is an open system that is constantly exchanging chemical compounds, energy and information with its environment. This exchange involves a large number of elements (molecules) related to each other in a dynamic hierarchical and modular manner. Modules can be identified from the analysis of the interaction patterns. At a molecular level, interacting networks include protein-protein interactions, metabolic pathways, and the different biosignalling pathways controlling intercellular cross-talk and the regulation of gene expression ,2. Thus,When a new interactome is built with current information integration tools, nodes represent macromolecules and connecting segments represent specific interactions. Nodes are associated with additional information about the genes/proteins; for instance, chromosome number and gene location, intracellular location of the protein, and codes for locations in different Data Banks and ontologies. All this information is extensively stored in several data warehouses  -10.Multiple sources of information on protein to protein interaction exist. They have usually been analyzed individually by genetic, biochemical and biophysical techniques. Recently, however, new methods have been developed for high-throughput macromolecular interaction analysis, including both experimental and biocomputational approaches. The high speed of new data generation has caused a problem, namely standardization of its notation in database(s) . In the In this paper, we present a tool called Protopia for searching for and integrating protein-protein interactions and the information about them contained in five different Protein Interaction Web Databases. It can be useful as a friendly search interface among different databases, as a validator of redundant information, as a network visualization tool and also as an export tool to SBML .Graphic User Interface, Model, Visualization Engine, Search Engine Interface, Data Source Adapter Interface and Data Source Extractor Interface. Protopia has been implemented in Java because it makes it possible to manage all required software patterns and also provides Operative System independence. Protopia components have been designed to increase reusability and the possibility of including new databases when required. The description of these components is shown below:Protopia is a modularized application, with separate functionalities providing a high degree of reusability and error correction. There are several main modules Figure  that worThe Graphic User Interface: A user interface is provided to use and to manage all the capabilities of Protopia in an easy manner.• Model: It is the component that manages all the other components, and makes cooperation amongst them possible.• Visualization Engine: It provides diverse representations of the located protein to protein interactions. Two kinds of text representations (vector and matrix), and two kinds of graph representations  have been implemented using a hypergraph [• pergraph  library pergraph  library.Search Engine Interface: It uses the Data Source Adapter Interface of every Data Service implemented to perform the specific search among the data sources included in Protopia.• Data Source Adapter Interface: An abstract interface defined to be implemented by a custom data source extractor in order to be able to both communicate and cooperate with the Search Engine Interface. It describes some basic operations to implement the service.• Data Source Extractor Interface: An abstract interface defined to be implemented by a custom data source extractor in order to be able to transform the original data to a common data structure usable by the Search Engine Interface.• The data source extractors are the components capable of extracting and transforming the protein interaction information. They implement the Data Source Adapter Interface and the Data Source Extractor Interface required to make them compatible with Protopia Figure .This project has been implemented with five data source extractors from five main Protein Web Databases  by using an access via HTTP. It was achieved by implementing parses among the HTTP text. Thus, our tool is able to use an abstract interface that defines the extraction and adaptation of the data by applying a usual data extraction method (via HTTP parse), but it can also be deployed by using direct access to databases (via FTP), web services, and other available data access.When a data source extractor is needed, it is easy to provide a new one by extending two java classes, one to define the URLs and characteristics (Data Source Adapter Interface) and the other one to define the way to obtain the different nodes and interactions of the graphs (Data Source Extractor Interface). In the same way, it is possible to build custom data source extractors for a desirable data source without the need to recompile the source code, in other words simply by packaging those two implemented classes, by creating a simple jar, and by copying it in an interface folder. Protopia automatically detects the changes in the folder and loads the new data source extractor.Most efforts in Protopia have been directed to providing useful and friendly protein interaction graph representations. In Protein to Protein interaction graph visualization a perfect visualization method does not exist, but there are applications that achieve it with a moderate to high degree of satisfaction. That is the case of ProViz, iPfam or Cytoscape -20. SincFour different kinds of visualization have been implemented. In all cases proteins can be represented by either the abbreviated name or the code of the respective Data Bank:• The first one is based on a text vector form Figure  and the • The third one is based on the Graphviz  library,• The fourth one has been implemented on the basis of an organic hypergraph library , which pOn the other hand, an optimum system should be able to reach a high degree of integration with any information repository, as well as maximum interoperability among the different data analysis tools. In our system integration of a new Data source will be easily executed by following the same procedure described above. Reciprocally, the information collected by our tool can be recorded in a file, saving the interaction data graph in plain text mode or in the standard SBML format, making results compatible with many otherplatforms , which is interesting for the comparative analysis of different networks.An optimum system should be able to locate and unify the information on a given molecule stored under any of its identifiers and return a confidence value to the user for each located edge.in silico, in vitro and/or in vivo). This information is often available in databases, but the different technologies and their values integrated in a "global confidence score algorithm" is not an easy task, and may be premature. In our opinion, such an algorithm should be reached by consensus and its cut-off should be selected by the user, which could be particularly important for studies of poorly-characterized interactomes.The increasing activity of the scientific community in applying technologies feeding protein-protein interaction information , followed by their notations in Data Banks, makes it likely that information on protein-protein interactions will grow exponentially in the short-term. It would be convenient that such a volume of information could be discriminated to select the most relevant in order to avoid an undesirable increase of background. Multiple facts should be taken into account for an accurate estimation of confidence; for instance, the method(s) used to deduce such an interaction  approach to the final objective could be provided by the quotient between the number of repositories-containing information on the interaction and the total number of queried databases. In our tool, this quotient is called "redundancy".The Protopia algorithm to look for redundancy is based on a simple but efficient search across all the available data sources . Thus, high redundancy (the darkest edges) will be considered when the numerator is as big as the denominator. It means that the interaction is redundantly defined in all the databases used in Protopia. No redundancy (the lightest edges) will be considered when the numerator is one. It means that the interaction is defined only in the original database and no matches have been obtained from searching in other databases. Medium redundancy will be considered in other cases. It means that the interaction is defined in some (more than one) of the databases used in Protopia.Then, Protopia can validate those interactions searching among all the integrated databases, looking for those relationships in all the databases and retrieving the validation for the user. This is called the redundancy factor. Depending on the visualization mode, the results are expressed as quotients on the edges and/or by a three colour code for the edges  was analysed and is shown in Figure In this example, only 62.16% of the protein interactions were redundant to some extent. Of this percentage, only 10.81% of the interactions are described in three of the databases, 40.54% are described in two of the databases, and 10.81% are described in just one of the databases.. This section will describe Protopia characteristics. Briefly, Protopia provides a configuration tool for selecting the database(s) used to retrieve information. Once the target database(s) has been selected, users can search by either the name or the code of the desired protein, and it retrieves a list of proteins from which to choose the required one. Once the protein is chosen, the tool will search its interactions through the selected database(s), and returns a collection of proteins and their interactions. The result of a search can be visualized in three different ways: a vector, a matrix, and a graph . Consequently, the redundancy quotients of any interaction involving p53 are lacking the information coming from HPRD. This problem is solved by analyzing the known names or synonyms (or even the sequence) when the Swissprot code is not available (due to an annotation error or because the protein is not identified by the Uniprot).Protopia deals with integration problems caused by the heterogeneity of Biological databases. For instance, there is no universal identification code for proteins accepted by the whole scientific community. Protopia tries to validate interactions using the Swissprot code which is usually available in most protein data files, matching them with this code which is also available in Expasy. However, if for example the user is using the HPRD engine, the Swissprot code of the p53 regulated apoptosis inducing protein 1 is not available . Only a little cache disk space is maintained to provide quicker access to the interaction information. The ease in implementing a new data source extractor interface adds functionality and new sources to compare interactions. The possibility of exporting the interactions to the standard SBML format enables the reuse of the interaction graph with other programs and tools. Thus, Protopia is not just an end-user application; it can also be used as a protein to protein interaction extractor capable of saving the selected proteins and interactions in a SBML file for subsequent analysis and manipulation.Current databases offer a different level of curation, annotation, number of proteins, and number of interactions. We intend to continue working towards taking all this information  to offer an improved interaction analysis method and tool able to take into account new filters, as if they were the tissues of an organism, and the experimental technology used to obtain the information. In fact, we believe that in the future (when enough information will have been extracted and annotated) the ideal tool should be able to detect and compare intercellular differences . In the silico-experimental strategy to study different biomedical problems [silico approaches can provide the maximum degree of information, mostly due to the scarce number of human samples, as a result of which advance is only possible by using experimental approaches [silico tools not only to integrate information, but also to filter and to organize the information obtained from many thousands of experimental molecular, biochemical and cellular studies in an automated way.It is accepted that this kind of biocomputational effort and tool is essential for the advance of knowledge in Systems Biology. Analyzing protein interaction networks using structural information is one of these approaches . In partproblems . Cancer,problems -33 to adproaches ,35. Due Characterization of protein networks, as well as formalization (mathematic modelling) of the metabolic behaviour could help the scientific community in searching for biochemical answers at the systemic level for many pathologies, including orphan diseases. It could allow us to predict the consequences of a given alteration in one of the protein/gene elements involved in a metabolic pathway, taking into account the cell specificities of the different human tissues and cell types. In fact, due to the differential expression patterns of the different cell types, a similar genetic alteration can be compensated or can have little importance in the correct function of one cell type, whereas it could have important consequences in the function of another one. This approach can also be useful to obtain emergent information on how to transmit the consequences of a molecular alteration to other apparently long-distance metabolic reactions of a given cell type, thus helping to understand  the biochemical causes for the loss of homeostasis of patients. As evidenced in the acknowledgement section and affiliation, our group is engaged in different projects and networks  with the aim of contributing to a better understanding of the molecular causes or consequences of any of these pathologies and, consequently, to the design of new and more efficient intervention strategies.Project name: Protopia, A protein-protein interaction tool.• Project home page: • Operating System: All which supports Java.• Programming language: Java.• Other requirements: Java SE 6.0, Graphviz to visualize dot graphs.• License: Creative Commons, • Manual: • The authors declare that they have no competing interests.ARC has designed the architecture of Protopia, drafted the manual and implemented together with IRM, AK and OC the tool. IND has implemented the extractors and drafted the manual. FJS and MAM have conceived the infrastructure, testing the tool, coordination and helped to draft the manual. JAM participate on the architecture's design, conceived the infrastructure, coordination, and helped to draft the manual.


The study of pathway disruption is key to understanding cancer biology. Advances in high throughput technologies have led to the rapid accumulation of genomic data. The explosion in available data has generated opportunities for investigation of concerted changes that disrupt biological functions, this in turns created a need for computational tools for pathway analysis. In this review, we discuss approaches to the analysis of genomic data and describe the publicly available resources for studying biological pathways. The interaction between pathways and the involvement of pathways in multiple phenotypes complicate the interpretation of gene expression patterns. For example, the epidermal growth factor receptor  signaling pathway plays a role in specific phenotypes including resistance to apoptosis, increased proliferation, mitogenesis, transcription of numerous target genes, and actin reorganization, in several cancers , a structural language to annotate gene functions for batch processing, and also methods of clustering analysis. The algorithmic basis of clustering identifies a pattern associated in a data set, which could be subsequently followed by GO analysis to identify its underlying biology.A major analytical step to mine large microarray data is sample classification or identification of gene sets with characteristic biological function. Entrez Gene at the National Center for Biotechnology Information (NCBI) provides unique identifiers for genes, and is a searchable database providing gene-specific information and links to external databases, including the Gene Ontology (GO) consortium, KEGG and Reactome7. GO classifies genes and gene products based on three hierarchical structures that describe a given entry’s biological processes, cellular components, and molecular functions, and organizes them into a parent-child relationship6. Through easy on-line access (http://www.geneontology.org), the genome databases are being unified to expedite the process of retrieving information on genes and proteins based on shared biology among multiple organisms. Several software tools, including GoMiner9, MAPPFinder10, and Onto-Express12, have been developed to explore the GO relationships among high-throughput data. However, the biological functions of genes/proteins are often complex and annotating them into categories may oversimplify their biology. The flat-format output does not convey the richness of GO’s hierarchical structure. Nevertheless, this established system of nomenclature of genes and proteins is important for the interoperability of databases, batch processing, and the future design of pathway databases.The Gene Ontology (GO) Consortium was established in 2000 to provide a controlled vocabulary for annotating homologous gene and protein sequences in different organisms13–16. Transcriptional profiling studies typically aim to identify patterns of change among clinically related samples or to classify subgroups of samples15–17. Clustering of microarray data is widely used to identify groups of genes that display coordinated expression patterns performed in a supervised or unsupervised manner 21–24. Clustering techniques are generally classified into two types: hierarchical and partitional26. Hierarchical clustering is constructed by either agglomerative (bottom-up) or divisive (top-down) approaches25. Agglomerative algorithms begin with separate clusters and merge them into successively larger clusters, while divisive algorithms begin with the whole dataset and divide the data into smaller clusters successively25. The output of agglomerative clustering is a tree of clusters called a dendrogram, in which each branch represents group of genes that have a higher order relationship 25. k-means clustering requires the user to define k number of clusters28, and SOM partitions data into a two dimensional grid of clusters30. However, hierarchical clustering is more frequently used30. Detailed reviews of clustering algorithms are available and this topic will not be discussed further in this review31–33.The biological system is integrative with tightly regulated processes, and genes with similar functions often exhibit coordinated expression patternsd manner 13,14,17–tionship 25,27. Pa35. Algorithms like principle component analysis (PCA) and multi-dimensional scaling (MDS) both employ this technique for classification procedures37. PCA visualizes multidimensional datasets by projecting data into a sub-space with 2 or 3 dimensions esources . The maj57. This method is useful as a first-pass tool for mining and extracting the knowledge in the literature. However, the constantly advancing nature of research, the further refinement of biological knowledge associated with each gene or protein further refining, the incompletion of the annotation database, and the complexity of entity names in the biological domain often makes it challenging for NLP to be high-quality with huge successes.The wealth of biological resources can complicate the construction of pathway databases . When ashttp://www.pathguide.org)58. This website is updated regularly and currently about 224 biological pathway resources are accessible through the Pathguide website. Here, we focus on a subset of databases that are publicly available.Pathway databases facilitate the data mining process for cancer researchers. The major pathway databases are listed in 59. The KEGG PATHWAY database consists of manually assembled pathway maps based on inspection of published literature. Pathway maps are grouped into metabolism, genetic information processing, environmental information processing, cellular processes, human diseases, and drug development. Most of the pathways associated with cancer are listed in the environmental information processing section, which is further subdivided into membrane transport, signal transduction, and signaling molecules and interaction. Beside human databases, information from other model organisms such as chimpanzee, mouse, rat, dogs, cows, and pigs is also available. KEGG pathway maps can be manipulated through the KEGG Markup Language (KGML) files, which provide graphical information to customize pathways.The KEGG (Kyoto Encyclopedia of Genes and Genomes) database has been established since 1995 and has been one of the most popular knowledge databases to datehttp://cancer.cellmap.org) is the only database that focuses on signaling pathways implicated in cancer. This resource contains ten cancer-related pathways and each pathway has approximately 100 to 400 interactions. Interactions are manually curated and reviewed for biological validity. Extensive information is provided in each pathway, including the cellular locations of the proteins, the types of physical interactions including molecular interaction, biochemical reaction, catalysis and transport, and post-translational protein modifications. The original citations, experimental evidences, and links to other databases are also listed. Gene expression data can also be visualized in the context of Cancer Cell Map pathways using the Cytoscape network visualization and analysis software60.The Cancer Cell Map . The protein domain architecture is graphically visualized with description of the domains and motifs within the queried protein. Post-translational modifications, protein interactions, and disease type are linked to PubMed, OMIM, Swiss Prot, Gene-Prot, Entrez Gene, or pathways within the HPRD. Individual genes within the pathway map are also linked to biologically relevant databases. Results from pathway analysis and HPRD entries can be readily exported. The use of XML (extensible markup language) for HPRD entries makes this database interoperable with other public databases. As with Cytoscape, the development of GenMAPP allows users to map microarray data onto pathway maps61.The HPRD (Human Protein Reference Database) contains ten cancer signaling pathways and ten immune signaling pathways which are graphically visualized in 62 Although the primary focus is on H. sapiens, it is now extending human pathways onto other organisms via putative orthologs to make them applicable to 21 model organisms, including mouse, rat, chicken, puffer fish, worm, fly, yeast, and E. coli. All the information in Reactome is cross-referenced with PubMed, GO, and the sequence databases at NCBI, Ensembl, and UniProt. Small molecules are linked to ChEBI (http://www.ebi.ac.uk/chebi), catalyst activities to the GO molecular function ontology, and sub-cellular locations to the GO cellular compartment ontology. The OMIM morbid map can be overlaid into the reaction map to see which genes have been implicated in the disease in the literature. Reactions from direct evidence in the literature and indirect evidence that are inferred via orthology in other species are indicated by color-coding. The Reactome SkyPainter tool facilitates the labeling of genes or proteins in the reaction maps. Thus, quantitative data from microarray experiments can be superimposed on Reactome maps to provide visualization and exploration in a pathway context.Reactome is a publicly available, peer-reviewed resource of human biological pathways.Cytoscape60 and GenMAPP61 have been developed to illustrate molecular interactions intuitively.Cross-talk between pathways can complicate the graphical representation of observed biological interactions. Therefore, visualization tools such as Cytoscape is a software tool for the integration of pathways with expression profiles. It allows the querying of networks by using several filtering tools, and linking a given network to public databases for functional annotations60. An important feature of Cytoscape is its extensible software framework which allows users to implement new algorithms and network computations. In addition to its use by the Cancer Cell Map (described above), Cytoscape can also be used in conjunction with other protein interaction databases or genetic interaction databases64. Molecular species are represented as nodes and intermolecular interactions are linked as edges. Different visual properties such as node color, shape, and size can be chosen, and subsets of nodes and edges can be displayed based on the criteria that are selected by the user. Visualization properties and analysis parameters are customizable.GenMAPP  is a computer program designed to display gene expression data in the context of biological pathways61. Based on the quantitative data that is loaded, the program will map genes onto relevant pathways and the user can set up criteria to color code the genes accordingly. GenMAPP visualize data in a file format called “MAPPs”, which allow users to organize the genes by their functional component. The user has the choice to download specific pathways or from the archive of MAPPs at www.netpath.org. The MAPPs database is manually curated, with interactions derived from textbooks, review articles, and public pathway databases. Gen-MAPP also has the feature to construct and modify the pathways by the user, a quality that is not possible if analyzing pre-existing pathway databases like EcoCyc, MetaCyc, and KEGG. Gene identification (ID) from GenBank, SWISS-PROT, Gene Ontology, or other known databases is used to link the gene object on the MAPP to public databases like SWISS-PROT or Entrez Gene by selecting the gene of interest. In addition, GenMAPP displays gene expression levels and provides statistical analysis based on the representation of altered genes in a given pathway MAPP.GoMiner9, MAPPFinder10, and EASE65 are software tools developed to correlate gene expression changes with GO terms to categorize the biological processes, cellular components, or molecular functions that are statistically affected. However, visualization of the pathway networks is challenging and complicated. Many software tools have been developed for microarray researchers to analyze large scale high-throughput data within the context of biological pathways, including the above mentioned Cytoscape and GenMAPP. Some of the most commonly used software tools are listed in Pathway Processor is designed to visualize whole genome microarray data in the framework of metabolic networks and provides statistical significance of the reliability of each differentially expressed gene66. This program displays data based on the information from the KEGG pathway database. Pathway Processor is implemented as two programs: Pathway Analyzer and Expression Mapper. Pathway Analyzer is the portion responsible for the statistical analysis of pathway significance, while Expression Mapper facilitates the visualization of this data on KEGG pathway maps.Whole Pathway Scope (WPS) is a software tool to analyze high-throughput microarray experiments by referencing pathway or gene information from KEGG, BioCarta, and Gene Ontology67. The internal database also includes information from the Genetic Association Database and MedGene Database to allow users to rapidly identify disease-associated genes and highlight them inside their network diagram or select them for further network manipulation. One of the key features is the ability to view multiple experiments simultaneously and color-code the expression value with its p-value. In addition, this software allows users to customize their own metabolic pathway and gene groupings with the option of using statistical analysis.Pathway Explorer is a web-based service available at https://pathwayexplorer.genome.tugraz.at to map expression profiles of genes onto pathway maps extracted from KEGG, BioCarta, and Gen-MAPP68. This web-based service reduces the local requirement for computational resources. It offers customizable analysis of the data by analyzing in a single or multiple pathways, and a right-tailed Fisher’s exact test and false discovery rate analysis were applied to determine the significance of the different pathways. Multiple experiments can also be displayed simultaneously on a single pathway with corresponding expression values. Data is linked to publicly available biological databases . The online accessibility of PathwayExplorer enables visualization of DNA or gene expression profiles within the context of biological pathways in a rapid manner.69. Pathway database users should be aware of the possible inherent problems associated with any databases due to the variable quality of the published data. Comprehensive examination of the literature, as well as additional experimental validation, should be used to confirm any findings. Cross-platform integrative analysis of genomics, epigenomics, and transcriptional profiling will offer a deeper understanding of the biological complexity underlying disease processes  number, which is a combination of four digits to annotate enzymatic activities without the fourth digit, is assigned to a gene, several pathway databases have used partial EC number annotations and inaccurately assigned them to a set of reactions that are associated with the same partial EC number under each orthology grouprocesses 70. The c


Linking high-throughput experimental data with biological networks is a key step for understanding complex biological systems. Currently, visualization tools for large metabolic networks often result in a dense web of connections that is difficult to interpret biologically. The MetNetGE application organizes and visualizes biological networks in a meaningful way to improve performance and biological interpretability.E. coli and Arabidopsis.MetNetGE is an interactive visualization tool based on the Google Earth platform. MetNetGE features novel visualization techniques for pathway and ontology information display. Instead of simply showing hundreds of pathways in a complex graph, MetNetGE gives an overview of the network using the hierarchical pathway ontology using a novel layout, called the Enhanced Radial Space-Filling (ERSF) approach that allows the network to be summarized compactly. The non-tree edges in the pathway or gene ontology, which represent pathways or genes that belong to multiple categories, are linked using orbital connections in a third dimension. Biologists can easily identify highly activated pathways or gene ontology categories by mapping of summary experiment statistics such as coefficient of variation and overrepresentation values onto the visualization. After identifying such pathways, biologists can focus on the corresponding region to explore detailed pathway structure and experimental data in an aligned 3D tiered layout. In this paper, the use of MetNetGE is illustrated with pathway diagrams and data from MetNetGE is a visualization tool that organizes biological networks according to a hierarchical ontology structure. The ERSF technique assigns attributes in 3D space, such as color, height, and transparency, to any ontological structure. For hierarchical data, the novel ERSF layout enables the user to identify pathways or categories that are differentially regulated in particular experiments. MetNetGE also displays complex biological pathway in an aligned 3D tiered layout for exploration. The availability of high-throughput experimental data provides new possibilities for understanding biological systems and creates new challenges for visualization tools as well. Data in high-throughput experiments can encompass thousands of RNAs, metabolites and/or polypeptides. Mapping such data onto a network that represents the interactions in an organism is essential for biologists to understand how the parts of the system influence each other and to generate data-driven new hypotheses -3. PopulA number of publicly accessible pathway databases containing data about genes, gene products, and interactions are available, e.g., BioCyc  MetNetDByFiles Organic, force-directed layout style [Despite the recent emergence of many pathway visualization tools, many tasks are still challenging. One important challenge is generating a meaningful visualization of the whole network. Unfortunately, generic layout algorithms such as the ut style  in Cytos3D approaches to display networks may offer methods to display more information ,10, howeArena3D  puts nodIn addition to being able to visualize a network organized in a meaningful manner, biologists need an overview of broader functional categories and network performance under different experimental conditions, e.g., to be able to ask questions such as whether degradation pathways have many highly expressed genes, or which biological process categories are overrepresented in the data. The remainder of this paper will use graph terminology to describe the ontology and visualization techniques. Thus, the term "tree" means the data structure, "leaf" node means the node in the tree structure that does not have any children, and "non-leaf" node means the node with at least one child, and is not related to the organism of a plant.Controlled vocabularies are graph-theoretical structures consisting of terms (which form the nodes of each corresponding graph) linked together by means of edges called relations . Structu• View the whole ontology on a single screen to gain a global feeling for the data and the main hierarchical structure.• View ontology details by navigation and/or interaction .• Map attributes on the ontology so that they are easily visible.• Clearly show non-tree connections.Normally, one single visualization method cannot satisfy all of the user requirements or facilitate all types of tasks. Therefore, MetNetGE links the global view of an ontology using ERSF to more traditional visualizations and data representation methods, including indented lists, parallel coordinate plots, and spreadsheets.This section assesses related work in ontology and hierarchy visualization in terms of the requirements given above. The most widely-used representation for ontology structure is the Windows™ Explorer-like tree view, or indented list , evaluations  find thaResearchers in economics have utilized 3D RSF to study hierarchical time-dependent data . HoweverIn order to show the overall structure of complete ontologies, MetNetGE provides a space-filling view of the biological network and maps the experimental data onto this view using the Google Earth software and API . MetNetG• A 3D tiered layout that shows the main pathway structure and cross layer patterns.• A novel representation and interaction based on our enhanced radial space filling (ERSF) technique with orbits for visualizing cross-links in an ontology dataset.• Methods that link the summary statistics of experimental high-throughput data to the ontology visualization.MetNetGE is implemented in Python. The pathway and ontology drawings were created as Keyhole Markup Language (KML) files, and were loaded into Google Earth through its COM API . The graThe major research focus of MetNetGE is on summarizing large biological networks on a single display. There are many well-known visualization platforms that are tailored to show biological pathways, e.g., Cytoscape, VisANT, however, none of them can handle the large number of 3D geometries used in the ERSF technique. Google Earth was designed to smoothly handle large 3D geometric datasets. The GE API provides methods for controlling the level of detail and zooming, selection etc. Moreover, GE is a well established and widely used platform with a well-known user interface, thus users may not need intensive training for successful use of MetNetGE.However, GE also has many limitations when used as an information visualization tool. For example, GE's COM API which we have been using currently does not support dynamic modification and removal of content. As a result, our program can not support many interactive actions like dragging and rearranging the network at this stage.MetNetGE features a 3D layout that is both aligned and tiered. Separating the nodes into different layers according to their types  provides a visually-clearer structure on which to superimpose the data.In the MetNetGE tiered layout, the node placement is based on the results from a user-selected major plane, rather than computing each planar layout independently. The layout of nodes occurs on the major plane first, and then other nodes are set based on their relation to the major plane nodes. For example, for the metabolic pathways, the metabolite layer serves as a natural choice of major plane. In Figure To summarize metabolic networks in a meaningful global view, MetNetGE employs the BioCyc pathway ontology, which hierarchically organizes the pathways as a directed acyclic graph, where many parents may point to the same child. For simplicity, we first assume the ontology is a pure tree structure which does not have any non-tree edges, and explain how the traditional radial space-filling (RSF) technique can visualize this simplified biological ontology. The next section shows how the Enhanced RSF algorithm can visualize a biological ontology which contains non-tree edges.Tree visualization is a widely studied topic. Among all the existing tree visualization techniques, we implement the RSF  because To the best of our knowledge, MetNetGE is the first application of 3D RSF in biology and the first algorithm to visualize non-tree edges on a RSF plot.RSF visualization of a pure tree uses the following rules:• Each circular region represents one node in the tree. The leaf nodes are placed on the edge of the drawing and the root node is placed at the center. Nodes with the same depth form one layer in the visualization, i.e. the root node forms layer 0, the nodes with depth 1 form layer 1.• Each circular region has five variables: sweeping angle, depth, radius length, height, and color.• The sweeping angle of a leaf node is determined by an attribute of the corresponding pathway. For the pathway ontology, we have set each pathway to an equal weight, thus spanning the same angle. For visualizing the gene ontology, it could also be linked to other factors, such as the number of differently expressed genes within a category.• The sweeping angle of a non-leaf node is the sum of all its children's sweeping angles.In the initial network view, we use structure-based coloring  where thacid resistance, and the leaf nodes represent the pathways that relate to this function, e.g., "A2" may represent the arginine dependent acid resistance pathway. In this example, we use a uniform radius length, structure-based coloring, and map the height of the subtree to the region's height.Figure As noted earlier, RSF cannot support non-tree edges which are very common in the pathway and gene ontologies. As a result, we developed the Enhanced RSF (ERSF) layout which uses orbits to represent non-tree edges.This concept is illustrated in Figure aldehyde degradation pathway belong to many categories?'.To help users find and trace interesting non-tree edges, the orbits need to be distinguishable from one another. The orbits are first restricted to span in the middle area of each layer, thus leaving a visually apparent gap between orbits in adjacent layers. To distinguish orbits in the same layer, our algorithm puts them at different heights and distances from the center. The orbit with most downlinks is placed as the most distant and highest. This arrangement can help users answer questions like 'Does the Coloring strategies also help the user visually distinguish orbits that are located on the same layer. When the orbit color is the same as the child's region, and non-leaf categories' regions are transparent, it becomes easier to see links between regions.Figure anaerobic stress?' can be addressed by mapping the values onto the Pathway or Gene Ontologies.The strategy of a biological scientist evaluating experimental data is to look for which parts of the network show significantly different measurements across different conditions. Questions such as 'Which pathways or categories are most changed under Since each region of the ERSF contains a group of genes, we use ensemble statistics to represent the overall behavior of the group such as the average or median value of those genes under a certain condition and map it to the color of this region. The height of the region can also be mapped to other values to capture the overall behavior of a gene or set of genes. For example, the height can be set to the average coefficient of variation (CoV) for each gene across a range of experimental conditions:MetNetGE uses animation to show the values of a series of experiments. For instance, a time-series experiment with 7 time points is presented as an animation of 7 frames. Users can use either the time controller from Google Earth or the animation control panel in MetNetGE to control the animation.Differentially expressed can also be mapped directly on the ontology drawing. The user first defines a threshold based on the experimental data, e.g., 0.7 fold-changes is the default suggested by collaborating biologists. Every gene with a value change more than the threshold is considered differentially expressed. A differentially expressed gene is down-regulated if the treatment value is lower; otherwise it is up-regulated.Another important value for biologists is the statistical significance of the observed differences in the experimental data. Simple analyses of experimental data, such as p-values for over-representation of PO terms, can be calculated using Fisher's exact test. One typical working scenario is: identify a list of genes of interest that are differentially expressed between conditions, or are of particular interest; calculate p-values of over-representation of pathways and categories using a Fisher's exact test; visualize these p-values on the ontology drawing.This section demonstrates the use of MetNetGE through illustrations of how the visualizations work along with the results of a pilot user study for the ERSF visualization of the pathway ontology. A use case comparing the standard methods for analyzing microarray expression data using ontology show the effectiveness of the method for providing a global understanding.Arabidopsis pathways. Two examples illustrate how MetNetGE enhances insight in both signaling and metabolic pathways. Figure ethylene biosynthesis and methionine cycle in Arabidopsis in both Cytoscape and MetNetGE. The 3D tiered layout in MetNetGE shows a clear circular structure in the top metabolite layer which illustrates the flow of mass through the metabolic cycle. The three blue edges from the protein layer indicate that three protein complexes catalyze the metabolic reactions. This structure separates out the signaling control from the metabolic flow very effectively. In Cytoscape, the same pathway is more difficult to interpret as it combines metabolic flow and regulation in a complex network.In this section, we present the 3D Tiered layout for The same pathway structure is also shown in Additional file Arabidopsis ethylene signaling. The MetNetGE 3D tiered layout shows how one metabolite (ethylene) and one protein (erf1) have many regulation links to other layers. This linkage is not obvious in the Cytoscape view.Figure E. coli Pathway Ontology using an experiment on BaeSR [E. coli gene expression database, M3D [This section compares how biologists can work with on BaeSR  from thease, M3D . This caThe standard working method for each of our test biologists is to use three tools together: Microsoft™ Excel, PathTree , and CytTo view the ontology structure, users need to export the PO to an XML file that is loaded into Cytoscape. Users can then use Cytoscape's layout to arrange the nodes automatically . However, due to the limitations of these node-link based layouts, the visual result is not comprehensible for the whole ontology  contains many children in other categories because its green uplink intersects many light blue orbits.Using the same ontology data from cell structures biosynthesis are also the children of another category fatty acids and lipids biosynthesis. When a user wants more information about those non-tree edges, he can rotate and zoom the view, and get results similar to Figure It is also clear that three pathways in the category To analyze the gene expression data, we initially map the average expression value on color, and map the CoV on height. The visual results of two frames are shown in Figure unusual fatty acid biosynthesis) stands out, because it and its descendants have very high CoV and expression values. This discovery demonstrates the benefit of using 3D to show these two attributes together. Another similar interesting discovery is the pathway alanine biosynthesis III, which also has very high CoV but very low expression values.Users can tilt the view to see the height of each region Figure . In thisBy switching between these two conditions, we notice that most of the pathways and categories have a greenish color under the treatment, which indicates lower expression values in the treatment condition than in the controlled condition. This is an interesting trend, since in most experiments the treatments normally have greater values. To confirm this trend, we can map the difference between these two conditions directly on the ontology.Additional file To reflect the up/down regulation property of an entire category or pathway, the ratio of the number of up-regulated genes to that of down-regulated genes is calculated and mapped onto the region's color. As a result, the reddish regions in are mainly up-regulated while greenish regions are mainly down-regulated. Other non-interesting regions  are left transparent to let the interesting ones stand out. It is clear that there are more interesting regions than the dozens listed in supplemental Figure superpathway of chorismate, gives a pop-up dialog with information about this pathway, and shows its details in the linked indented list. To get more information about the genes in this pathway one can select the "add genes" button, to add all its genes to a spreadsheet table and a parallel coordinate plot.After tilting the view  format. Users can then use their own data to generate values via statistical tools in R or exploRase , and visAnother drawback of MetNetGE is the lack of interactive modification of the drawing. Since Google Earth's COM API currently does not support dynamic creation and deletion of KML content, MetNetGE is constrained to show only static drawings and framed animations.http://www.metnetDB.org). Figure One example study in our group is to identify differentially expressed transcripts in transgenic Arabidopsis that were genetically engineered to accumulate PHB (polyhydroxybutyrate) compared to control plants . In thissugar degradation are down-regulated in transgenic line that was not found in previous analysis  and with multiple inheritances.MetNetGE also uses the 3D Aligned Tiered Layout to visualize individual pathways. This layout groups nodes into distinct layers based on node type or sub-cellular location. Instead of generating layouts for each layer independently, the nodes' positions are established on one major plane, and then the positions of other nodes are computed. This layout helps the user visualize cross-layer patterns as well as helping the main metabolic reactions to stand out. One of our ongoing works is to improve this layout to visualize more complex pathways which may contain both metabolic and signaling reactions. The natural distinction of layers can help distinguish the mass flow from the signaling and regulation in complex pathways.Pilot user testing shows that users prefer using the global ERSF approach to their current working solutions, which have been based on indented lists and node-link layouts. Future works will include evaluations of representations for non-tree edges along with linked views of the data including pathways and statistical plots. Initial user testing shows that using an ontology structure linked to statistical graphics is powerful in real world usage. Therefore, a larger quantitative user study about the effectiveness of ERSF and the linked views is planned in the near future.Since Google Earth's COM API currently does not support dynamic creation and deletion of KML content, MetNetGE can show only static drawings and framed animations. MetNetGE will be ported to JavaScript to utilize the Google Earth API , which nProject name: MetNetGEhttp://www.metnetge.orgProject home page: Operating systems: Windows Only. Since Google Earth COM API only supports Windows systems, our program can not be used on Mac OS, Linux and any other operating systems.Programming language: PythonOther requirements: Python 2.5 or higher; Google Earth, PyQt and other required libraries (listed in the documentation on project home page)License: Freely available under GNU GPL license.Restrictions to use by non-academics: NoneSource code: The program's source codes are submitted as the additional file Demonstration videos: The video for 3D tiered layout is submitted as the additional file MJ was the main developer of the tool. He also proposed the 3D aligned tiered layout and ERSF layout. SC proposed to map the differentially expressed genes with p-values on the ontology drawing, and tested MetNetGE with biological data. DR designed the framework of using PyQt and other libraries to build the program. ESW specified the requirements of the tool for biologists. JAD was the lead of the project and had the initial idea of using Google Earth to visualize biological data. All the authors read and approved the final manuscript.Supplemental pictures. This file contains 6 supplement pictures that help to illustrate the strength and unique features of MetNetGE.Click here for fileMetNetGE source code. This is the source code of MetNetGE. Please go to MetNetGE.org to find the latest version, dependency packages and tutorials.Click here for fileDemonstration video 1. Part 1: 3D tiered layout.Click here for fileDemonstration video 2. Part 2: View Pathway Ontology and Experimental Data.Click here for file


The complex yet flexible cellular response to pathogens is orchestrated by the interaction of multiple signalling and metabolic pathways. The molecular regulation of this response has been studied in great detail but comprehensive and unambiguous diagrams describing these events are generally unavailable. Four key signalling cascades triggered early-on in the innate immune response are the toll-like receptor, interferon, NF-κB and apoptotic pathways, which co-operate to defend cells against a given pathogen. However, these pathways are commonly viewed as separate entities rather than an integrated network of molecular interactions.Here we describe the construction of a logically represented pathway diagram which attempts to integrate these four pathways central to innate immunity using a modified version of the Edinburgh Pathway Notation. The pathway map is available in a number of electronic formats and editing is supported by yEd graph editor software.The map presents a powerful visual aid for interpreting the available pathway interaction knowledge and underscores the valuable contribution well constructed pathway diagrams make to communicating large amounts of molecular interaction data. Furthermore, we discuss issues with the limitations and scalability of pathways presented in this fashion, explore options for automated layout of large pathway networks and demonstrate how such maps can aid the interpretation of functional studies. The innate immune response is executed at the molecular level by a complex series of interwoven signalling pathways. In this context, pathways may be defined as a network of directional interactions between the components of a cell which orchestrate an appropriate shift in cellular activity in response to a specific biological input or event. Whilst our ability to perform quantitative and qualitative measurements on the cellular components has increased massively in recent years, as has our knowledge on how they interact with each other, we still struggle to translate these observations into graphical and computationally tractable models. However without such models we can not hope to truly understand biology at a systems level.ad hoc and frequently included in reviews and original papers. Whilst they are clearly useful aids to understanding cellular events, even at their best, they are not sufficient by themselves, relying on extensive textual descriptions to explain what is shown pictorially. Recent years have seen considerable growth in the availability of public and commercial databases offering searchable access to pathways and interaction data derived from a combination of manual and automated (text mining) extraction of primary literature, reviews and large-scale molecular interaction studies. Using these tools it is possible to view a range of canonical pathway views or generate networks of interactions based on a given query. However, all of these efforts are let down by one or a number of key factors. The notation used in diagrams to depict one molecule's interaction with another is varied, often ambiguous and therefore limited in its ability to depict the exact nature of the relationship between components of a pathway. There is often a lack of direct access to the experimental evidence relating to the interactions depicted or to the dataset as a whole. Similarly, labelling of the pathway components often uses non-standard nomenclature or mixes protein names from one species with that of another, such that again the reader is left uncertain as to what exactly is being shown. Finally, pathway diagrams usually focus only on a small part of a biological system and one which often reflects the curator's bias, such that the 'same' pathway described by different individuals may share little in common. Whatever the source of these pathways and networks they generally suffer from graphically poor representation with ambiguity around the precise identity of what is being shown and the exact nature of their interaction. In order to address these issues the groups of Kohn and Kitano began to devise new approaches to pathway notation using many ideas adopted from the electronics industry , inactive [I], phosphorylated [P]. Interactions (edges) between components or transitions between one cellular compartment and another, are shown as arrows which either contact interacting partners via Boolean logic operators  and/or transition/annotation nodes that provide information as to the nature of the interaction or transition from one state to another. Attempts to depict molecular details of interactions and state transitions such as the exact site of a protein's phosphorylation, have generally been avoided. Whilst important, if depicted on a map of this size the information quickly clutters up the diagram rendering it inaccessible to the casual reader. However, in cases where such details are necessary to differentiate one component form from another they should be added. Finally, layout of the elements and interactions that make up the pathway should be such that it is relatively easy to follow the direction and nature of flow of information from the initial trigger to the eventual outcome. In an effort to achieve this, where possible interacting map components are drawn close together keeping edge lengths short and easy to follow, crossover of edges is kept to a minimum and every effort is taken to keep connecting edges separate, with a minimum number of changes in direction to get from one point to another.A list of interactions to be mapped was compiled [see Additional file The pathway map was drawn using the freely available program yEd graph editor . yEd is a general purpose graphical tool designed for the depiction of networks. Although not specifically designed for biological pathway depiction it has been used previously for this and similar purposes ,20 and hThe combined map of macrophage activation pathways described here Figure  is availExpress3D [As the complexity of maps increases and the interactions between components become evermore intertwined, manual organization of these events becomes time-consuming and difficult. However, the pathway diagrams have been specifically drawn as directional networks. As such layout of the pathway maps can be aided by use of various automated layout algorithms. The hierarchical layout and classic-orthogonal edge routing applications within the yEd software were the most effective in terms of providing an easily interpretable view of directional flow in the diagram Figure . Howeverxpress3D . Whilst 5 cells/well. To differentiate the cells from monocytes into primary macrophages, cells were then incubated for 7 days in DMEM-F12 growth media supplemented with 10% L929 cell suspension releasing the MCP-1 macrophage stimulating factor, with media changes on days 3 and 5. On day 7 the growth medium was replaced with DMEM-12/10%FCS medium containing 10 u/ml recombinant mouse interferon-gamma  and harvested 1, 2, 4 & 8 h following treatment or collected pre-treatment (0 h). Total RNA was harvested from the cells using an RNeasy Plus kit (Qiagen) according to manufacturer's instructions. RNA was quantified and quality controlled using a NanoDrop spectrophotometer (NanoDrop Technologies) and BioAnalyser 2100 (Agilent). Replicate 150 ng samples of total RNA derived from two separate wells per time point were labelled using the Affymetrix whole transcript labelling protocol and hybridized for 16 h at 45°C to Affymetrix mouse exon 1.0 ST arrays. They were then washed and scanned according to manufacturer's recommendations.Primary mouse bone marrow derived monocytes were prepared from male balb/c mice 10–12 weeks old. Cells were washed, resuspended in DMEM-F12/10% FCS/L929 medium and counted before being plated in a 24-well plate at a concentration of 5 × 103D [Data (ArrayExpress Ac. No: E-MEXP-1490) was normalized using the RMA package within the Affymetrix Expression Console software and annotated. Transcripts which might be considered to be differentially expressed were identified using either the Empirical Bayes function within Bioconductor  or using3D  using a 3D  set at aWe set out to use the EPN scheme as originally published . Howeveret al., (1997) report that both TNFRSF10A and TNFRSF10B recruit the protein FADD during apoptosis signalling [The pathway map described here Figure  consistsgnalling . A detaiIn order to check the integrity of the network each input (e.g. cytokine or pathogen molecule), was highlighted in turn and the logical flow of information from this input followed through the diagram. By following the flow of information from each pathway input, a different but expected output was observed, be that the activation of transcription or a process such as apoptosis Figure . This suExpress3D [de novo protein synthesis and activity following an increase in gene transcription) were highlighted  with interferon-gamma (Ifng). Using the network analysis tool BioLayout xpress3D  we constxpress3D . All of d Figure . In thisCXCL11, IFIT1, CXCL10, IFIT2, and TNFSF10). Moreover IRF5 was itself regulated at the later time points (4–8 hours) post-IFNG treatment. Another consequence of TLR-signalling is the activation of the NF-κB pathway and again the key constituents of this pathway (NFKB1 and RELA) were activated at the later time points as were some transcriptional targets of this complex. During the 4–8 hours period BID, an important amplifier of apoptotic input signals via the mitochondrial apoptotic pathway, was up-regulated  of the response to Ifng treatment only two genes, SOC3 and IER3, corresponded to pathway components shown in the diagram Figure . SOCS3 . Most nod Figure . BID canWe are acutely aware that the current pathway diagram covers only a relatively small number of the genes shown to be transcriptionally regulated following Ifng treatment. For instance none of the genes shown to be down-regulated by Ifng are shown in the diagram. However even with the current limited coverage we have been able to extrapolate some interesting observations by visualizing the changes and the possible downstream effect of the changes. It has been possible to appreciate the connectivity and co-dependency of the changes over time and using this approach the detail of how signalling in one region may have downstream effects on another signalling system can be hypothesized and in many examples here extracted.In constructing this integrated map of macrophage activation pathways we have attempted to represent events in a detailed, accurate and logical fashion. However, it must be emphasized that this map is by its nature a biased view of events. Its construction has been primarily driven by our interest in understanding signalling events in the macrophage and interpretation of the literature is an unavoidably flawed process; determining what constitutes good evidence for an interaction and what does not, is often difficult to judge especially for those who do not specifically work in the area. Furthermore, any view of what constitutes a given pathway is also highly subjective and is always being driven by an individual's perspective and scientific trends, as well as current knowledge. Even though pathway diagrams typically depict individual pathways in isolation of other systems, in reality it is well recognized that there is significant overlap in pathway membership and cross-talk between related pathways. Input from one signalling pathway can influence the outcome in another, underscoring the need to view the connections between various signalling systems. Indeed, when one searches for the known interactions of any well characterized protein using database tools such as String  or IngenIn the case of the signalling systems described here, the interaction data is derived from the available literature and is therefore dependant on the quality of that work, the biological system from which that information was derived and as already mentioned represents a subjective view of the information available. Seldom do signalling pathways operate independently of each other therefore analyzing only a subset of nodes known to belong to a particular pathway is unlikely to be insightful as to the activity of the system as a whole. With so many of pieces of the jigsaw missing and many aspects of the activity of these large integrated molecular networks still unknown, performing meaningful analyses on relatively small sections of what is otherwise an immense network of interacting proteins, is unlikely to deliver accurate or biologically representative predictions for some time.Express3D recently developed by us [The current notation system used for the pathway presented here arguably works well up to this size of pathway and the end result we hope will serve as a useful reference for biologists interested in these systems. However, scalability of pathway diagrams is an important issue especially when a compromise must be reached between presenting a human readable map with one that captures the extensive interaction data now available for many molecules. Although we intend to continue to consolidate and add interactions to the current map we are aware that this could prove difficult in number of respects. When new components are added, in order to place them near to the site of their interacting partners the layout of the entire graph sometimes needs to be manually altered to make space. Furthermore, as functional units of an integrated pathway network frequently share components, proteins often referred to as hubs, it is often impossible to place a component near to all its interacting partners requiring edges (interactions) to span large distances across the map. One method of reducing long edge lengths is to depict individual components more than once within a given cellular compartment. However, this in turn adds to the issue of scalability as the additional nodes consume more space, add more complexity and the visual link between components of the pathway are lost. We have therefore been exploring alternative approaches to overcome the issue of scalability in pathway depictions. One approach is to use automated layout algorithms to draw the relationships between pathway components. Certain layout algorithms are very effective at displaying connectivity between components with little or no need for manual intervention Figure . This aled by us  we have With the majority of the components of life defined, at least at some level, there is an increasing desire to put the parts together in order to construct models of biological systems which can be tested and refined. In this respect, the value of logically presented pathway diagrams is becoming ever more apparent given the growing need to systematically organize and describe the interactions between the various components that make up a cell. Pathway diagrams serve several purposes; they can be used to capture a large amount of information, provide a point of reference for researchers with an interest in the pathway or particular member of that pathway, and can be used to aid the interpretation of systems level analyses. The pathway presented here is by no means a comprehensive view of all the pathways involved in macrophage activation, but acts a worked example of how a number of key pathways might be represented in what we hope is a logical and unambiguous fashion. However, with the visual modifications to the EPN scheme we believe we have fulfilled the primary objectives of providing a graphical notation that is both useable by biologists and which could still serve as the basis for computational model development. So whilst others have gone some way to address the issue of human readability of their pathway diagrams we believe that we have derived an elegant yet simple notation scheme that better addresses the needs of biologists. The mapping process is a continuing effort and during the next steps we aim to consolidate and expand the content of the diagram. This in turn may require refinements to the notation system as issues in depicting the relation between components and the cellular components in which they are active arise. As we enhance our understanding of individual signalling pathways and how they integrate with others this will aid understanding of immunological disorders at a molecular level. Building pathway diagrams or networks of interactions from the existing knowledgebase is one of the milestones towards the application of pathway and systems biology to the field of medicine.3D visualisation of the pathway diagram. PG originally conceived the EPN scheme and has supported its continued development. TF oversaw and contributed to the pathway construction, orchestrated the development of the EPN scheme, conceived the Ifng time course study, and its analysis and drafted the manuscript.SR constructed the integrated pathway diagram, contributed to the development of the notation system, participated in the expression profiling and analysis, and helped to draft the manuscript. KR contributed to the pathway development efforts and standardisation of pathway data collection and storage. PL contributed to the construction of pathway, collection of molecular interaction data and development of the notation scheme. DP set up the Ifng time course study. AE developed BioLayout ExpressPathway interaction list. Interactions included in the pathway map are listed (in no particular order) in this data file. Official HGNC (human gene nomenclature committee) gene symbols are used to name the interacting components along with a brief description of the type of interaction and its cellular location. Entrez gene IDs of interacting components are also provided as are the PubMed IDs of the reference(s) supporting each interaction.Click here for fileDiagram of signalling pathways central to macrophage activation. This file can be opened, viewed and edited by users using the freely available graph-editor software yEd . This can be downloaded at  where fuClick here for fileBibliography of references supporting interactions on the integrated pathway diagram. Within this document a list of the 282 different references supporting the interactions on the pathway map are provided in alphabetical order (by author name).Click here for fileDescription of the biological content of the pathway. A description of the signalling of the four pathways  depicted on the integrated pathway diagram is provided here. The interconnectivity of these pathways and their significance in innate immune signalling is also discussed in this section.Click here for fileInterferon gamma regulated genes. A summary of the analysis of the 58 genes present on both the pathway map and in a transcriptional network of differentially expressed genes following Ifng stimulation. A transcriptional network of all differentially expressed genes (above 1.6 fold change) was constructed and clustered using the graph-based clustering algorithm MCL set at an inflation value of 2.2. This resulted in 26 different clusters, which were then assigned a description of the co-expression pattern they represent over the time course. The cluster numbers and the descriptions of co-expression pattern are shown in this data sheet for the genes present on the pathway diagram. 3 of these genes did not appear in any cluster. Also included in this table is a summary of the gene expression changes according to annova and Empirical Bayes calculations. RMA normalized expression values are included for each gene across the time course as are gene descriptions and GO (gene ontology) annotations for the 58 genes.Click here for file


Scientific data integration and computational service discovery are challenges for the bioinformatic community. This process is made more difficult by the separate and independent construction of biological databases, which makes the exchange of data between information resources difficult and labor intensive. A recently described semantic web protocol, the Simple Semantic Web Architecture and Protocol  offers the ability to describe data and services in a semantically meaningful way. We report how three major information resources  used SSWAP to semantically describe selected data and web services.de novo community ontologies. We used SSWAP to express these offerings in OWL Web Ontology Language (OWL), Resource Description Framework (RDF) and eXtensible Markup Language (XML) documents, which are appropriate for their semantic discovery and retrieval. We implemented SSWAP services to respond to web queries and return data. These services are registered with the SSWAP Discovery Server and are available for semantic discovery at http://sswap.info.We selected high-priority Quantitative Trait Locus (QTL), genomic mapping, trait, phenotypic, and sequence data and associated services such as BLAST for publication, data retrieval, and service invocation via semantic web services. Data and services were mapped to concepts and categories as implemented in legacy and A total of ten services delivering QTL information from Gramene were created. From SoyBase, we created six services delivering information about soybean QTLs, and seven services delivering genetic locus information. For LIS we constructed three services, two of which allow the retrieval of DNA and RNA FASTA sequences with the third service providing nucleic acid sequence comparison capability (BLAST).The need for semantic integration technologies has preceded available solutions. We report the feasibility of mapping high priority data from local, independent, idiosyncratic data schemas to common shared concepts as implemented in web-accessible ontologies. These mappings are then amenable for use in semantic web services. Our implementation of approximately two dozen services means that biological data at three large information resources  is available for programmatic access, semantic searching, and enhanced interaction between the separate missions of these resources. Glycine max, (L.) Merr.), it contains many of the same data classes as the Gramene database, including information on agronomically important plant phenotypes (traits) and genetically mapped quantitative traits, commonly referred to as QTLs (Quantitative Trait Loci). Soybeans are not grasses; they are legumes. Thus while SoyBase shares data types with Gramene, it shares data and evolutionary relevancy with LIS. SoyBase contains QTL and genetic information for soybean, Gramene includes QTLs identified for numerous agronomic traits in the grasses with information on associated traits and coordinates for their loci on various genetic maps, and LIS includes cross-legume comparative data. All three major information resources afford their users the ability to go to their respective websites and search or browse sequences, genes, traits and other data from major cereal crops (Gramene) or legumes , yet cross website scientific integration is laborious and largely unstructured.SoyBase was origTwo challenges that SoyBase, Gramene, and LIS face today are discovery of relevant external web services and the integration of external data sources into integrated presentations for their users. To illustrate the discovery challenge from the perspective of a user, consider a researcher searching the web for QTL services, where--other than going to particular resources already known to the researcher such as SoyBase or Gramene--one has few discovery options other than beginning a search with web search engines such as Google. However, the results returned by Google when searching on the string 'QTL' or similar keys are varied in their context and relevancy. So instead of searching for web resources with the string 'QTL' with its lack of contextual relevancy, we seek a method to provide users with a way to find services that operate on formal QTL objects and other data types based on well defined data models. When we offer such contextualized services, they can be invoked directly by users with appropriate front-ends as well as integrated by us in our own informatic offerings. Additionally and importantly, analysis of our requirements shows that the term "users" needs to be interpreted broadly: we seek not just the capability to allow people to better find data and services, but to allow computers, without human assistance, to both discover and engage such resources. With this capability, SoyBase, Gramene, and LIS can deploy automated programs to discover, engage, assess, assimilate, and variously integrate disparate data and services to provide more productive resources for our users.An immediate and well-known difficulty in discovering and engaging disparate data and services is the non-standard, idiosyncratic structure of data resources and the idiosyncratic ways common data is described. Under normal circumstances, a database administrator that wanted to transfer data between databases would first have to examine the external database schema, looking at the sometimes cryptic labels for the fields in each of the external database's tables, and try to determine if there is an analogous field in one's own database before populating data. This process would, of course, have to be repeated with each external database the administrator wished to integrate. Documentation helps direct this process, but utilizing that documentation as human readers is low-throughput and non-automated. Web service application programming interfaces (APIs) can alleviate some of the low level issues in data retrieval and transfer, but they do not standardize discovery and invocation across providers.ad hoc semantic that either does not lend itself to formalized reasoning, or is promising yet not sufficiently developed from research to production grade.Major information resources such as NCBI eUtils  and EMBLThe issue of semantics and ontologies is non-trivial. It is long noted that it is difficult to agree on what to call something as (seemingly) simple as plant anatomy or genes. Terms may become overloaded, such as "locus" or "marker," yielding different meanings in different contexts. Ontologies and the discussions surrounding their creation have helped alleviate some of the ambiguity, or at least aided in the establishment common terms for the purpose of knowledge classification and data exchange. Yet the nature of classification itself raises substantial conceptual challenges beyond simple agreement on terms . It is uad hoc conventions of simple lexical token matching.To address this, we sought a system that is based on a simple, REST-based architecture  using iSSWAP (Simple Semantic Web Architecture and Protocol) ,19 uses SSWAP recognizes that ontology content generation is best done by the community, for the community. Similarly, it recognizes the immense value in legacy work, even if those ontologies cannot be used for semantic web services in their current format. Thus SSWAP-compliant ontologies address the structural issues relevant to implementing a semantic web services system. Many legacy ontologies, such as those of the Open Biomedical Ontology (OBO) foundry  can be rSSWAP's use of ontologies is also aimed at addressing the social limitations often associated with ontologies and web services. How do we get agreement on the use of a term? SSWAP does not insist that all parties use the same term. Instead, SSWAP enables a marketplace of ontologies whereby anyone can put an ontology on the web for use in semantic web services. If such ontologies share nothing in common, then indeed there is no logical or semantic connection between terms. But in practice, ontology creation is laborious, and there is a strong incentive to reuse the work of others and extend it only when needed to address a local requirement. Thus SSWAP enables a "web of ontologies", where providers such as the OBO foundry and the Gene Ontology offer their ontological models, and users can mix, match, and extend terms as dictated by their specific requirements. Because all ontologies used in SSWAP are in OWL DL, the resultant admixtures are amenable to consistency checking and reasoning. There is no requirement for global consistency, thus locally created admixtures may be used ephemerally on a transaction-based model without breaking global behavior. SSWAP's use of OWL means that ontologies may be extended and used by third-parties under a formal semantic, without requiring explicit coordination.Ontologies for each of the information resources of Soybase, Gramene, and LIS were constructed in consultation with each other to minimize work and identify shared concepts, but actual ontology creation was done independently at each site using the Protégé editor . Where ae.g., how to differentiate or equate terms such as myOntology:DNASequence from yourOntology:DNASequence or other tags). In SSWAP, RDG's are 100% OWL DL. Semantic web service definitions are self-describing logical statements of what the service is, and what it performs. The RDG structure is grounded on the reserved classes and predicates of SSWAP, establishing the protocol [i.e., dereferencing all URIs and embedding the resultant OWL RDF/XML) can be automated to expand the RDG into hundreds of statements. This closure can in turn be sent to a reasoner, whereby implied inferences are made explicit. This can result in thousands of statements. This process is available for inspection using the on-demand validation and publication tool at the SSWAP web site [Semantic web services use SSWAP to define themselves on the web in a simple document called a Resource Description Graph (RDG). An RDG is only superficially similar to the web service WSDL (Web Service Definition Language)  documentprotocol . The proweb site . To prepweb site .http://sswap.info for this purpose. Upon receiving a publication notification, the Discovery Server executes a HTTP GET on the URI and reads in the content. If the content is a valid SSWAP graph, then the Discovery Server parses and reasons over the document and adds the content to its knowledge base for immediate on-demand semantic searching. If the document is not a valid graph, then the Discovery Server recognizes that the URI is not a SSWAP resource, and updates its knowledge base accordingly. Thus publication and deregistration are simply a matter of having the Discovery Server's internal model reflect the reality of the web. Architecturally, the model is amenable to spiders and web bots, whereby no active publication notification on the part of the provider is necessary, though we do not currently traverse the web for resources in this manner. Like virtually all systems on the web that use a snapshot of the web for backend processing, the model is subject to latency errors, whereby the Discovery Server's results are dependent on how it reflects the actual state of web resources at any given time. In this manner, the validity of the SoyBase, Gramene, and LIS service graphs were checked for format and consistency using the SSWAP "Validate Resources" service [Providers of data and services describe their offerings to the world by hosting RDGs on their web site Figure . To allo service . When eaFor browser-based, point-and-click interaction, clients may discover and engage services via the semantic search front-end at the SSWAP home page . For non* subclass of the query graph's sswap:Resource, and* superclass of the query graph's sswap:Subject, and* subclass of the query graphs's sswap:Object.Thus, discovered resources are exactly those semantic services which are guaranteed to be able to process and return the types of data requested by the client. It returns services that are as specific, or more specific, than the service class requested; (and) services that accept data types as general, or more general, than the input data type of the client; (and) returns data that is as specific, or more specific, than the return data sought by the client. For service invocation, the client sends the service a graph with the appropriate input data in the graph called a Resource Invocation Graph (RIG). Depending on the construction of the RIG, the data itself does not need to be serialized in the graph, but can be referenced indirectly by URLs. The client can also engage services directly without the Discovery Server, in a manner analogous to how anyone can use a search engine such as Google to find web sites or visit them directly.SSWAP's knowledge base of statements underlying the Discovery Server is implemented in PostgreSQL 8.2. This is accessed by Hewlett-Packard's open source semantic middle layer, Jena , as a Jae.g., 'name', 'symbol', 'synonym', etc.) based on the existing Gramene databases and their underlying query interfaces. At a later stage in collaboration with SoyBase, LIS, and SSWAP developers, we developed more general QTL, 'trait', 'map' and 'marker' ontologies. These ontologies are available via the SSWAP ontology portal [http://sswap.gramene.org/vpin/ontologies/qtl.In order to describe the data available from Gramene, QTL, trait, and map ontologies were developed in OWL DL using the ontology editor Protégé. We also reused and extended other ontologies available at the SSWAP ontology portal . Initialy portal . The ontGramene acts as a service provider under the SSWAP semantic web services platform. Gramene currently offers 10 QTL resources allowing searching for QTLs by different means, such as by QTL metadata  as well as by trait , species (common or scientific name), and linkage group  or the Discovery Server at http://sswap.info.LIS offers numerous variants on two basic semantic web services: 1) sequence retrieval (implemented via two complementary services); and 2) BLAST analyses (implemented as a single service for both DNA and RNA queries)  First tier, generic ontologies, often previously published. These ontologies encompass the main concepts regarding such areas as sequences, taxonomy, and genetic map features;2) Second tier, institutional ontologies. These are created via subclassing from the top level ontologies. These cover the institutional idiosyncratic requirements not covered by other published ontologies. To the degree that separate providers share these lower level requirements, groups may use these terms or subclass them too. This process incrementally builds a de facto community standard.The ability to use subsumption semantics demonstrates some of the sociological aspects of SSWAP's architecture. Instead of having to develop a single ontology for each community in which all stakeholders have to agree, SSWAP allows developers to define classes or properties independently or in socially small groups, yet still under a formal semantic. Thus SSWAP allows the encapsulation of data present in any database into an RDF/XML OWL graph for data transport, without the necessity for alterations to the underlying database structure or programming and while enabling a mechanism for semantic integration. Since each ontology is by default published and accessible on the web, any actor can use the terms and concepts to describe their services as appropriate.In all three cases presented here , SSWAP service construction was largely dictated by existing database functionalities. However, there may be differences in what information is returned for a seemingly similar search. For example, SoyBase and Gramene each provide a service to look up data for QTLs. Both databases return QTL data, but the type of data returned from a simple QTL request varies dramatically   and PlanQtlReportService and LocusReportService take as input the map symbol for a soybean QTL or Locus and return a report back for the bulk of the data concerning the map symbol present in SoyBase. In order for other databases or researchers to find out what symbols are available, other services were created. The SoyBase LocusTypeService was created to deliver a list of all the "Types" of loci in the database. The LocusByTypeService was created to take in a locus type and return a list of all map symbols of that type. With this list one could then systematically retrieve all data from the locus class held in SoyBase. Similarly, the QtlLGService was created to take as input a soybean linkage group identifier and return a list of all QTL on that linkage group. This list could then be used to systematically retrieve data for each QTL.As an example, the SoyBase services qtl-by-accession and qtl-by-symbol services provide detailed information about specific QTLs. The information includes traits  from the OBO-style trait ontology (TO) hosted at Gramene, as well as species (common and scientific name), linkage group, map name, and location on a map (start and end position).Gramene offers several types of services, where each service allows searching for QTLs and associated information, based on a particular search. The qtl-by-trait-accession, qtl-by-trait-symbol, qtl-by-trait-name, and qtl-by-trait-synonym allow searching for QTLs related to a specific trait. Traits in Gramene are identified by the Trait Ontology [qtl-by-trait-category service allows retrieval of QTLs associated to traits that belong to one of these trait categories.The so-called 'trait' services, namely, the Ontology  and are qtl-by-species-common-name, qtl-by-species-scientific-name and qtl-by-linkage-group retrieve a list of QTL accession IDs and symbols that belong at a particular species or linkage group. This information may then be used to retrieve further details about a particular QTL using the above mentioned qtl-by-accession-id or qtl-by-symbol services or any other semantic web service that provides information given a QTL accession ID and/or symbol.The getSequenceForIdentifier, getSequencesForMarkerSymbol, and blastSequences) demonstrate a number of SSWAP architectural features: 1) they allowed for the development of a local, lightweight SequenceServices ontology to be built to address the lack of appropriate ontology terms for sequence comparison parameters for semantic web services, while also allowing it to be used in aggregation with the established Nucleic Acids Research categories [blastSequences service, a legacy capability at LIS was simply wrapped to turn it into a new SSWAP-compliant service; 6) the SSWAP services were designed for programmatic access and analysis with LIS's more than two million sequences, but they are wrapped with web front-ends to provide point-and-click engagement; 7) the services are discoverable and evocable outside of their providers' web front-ends via discovery and invocation directly from the third-party Discovery Server [The three LIS services  are deeply veined within the language, thus offering both power and opportunities for abuse. In other cases, seemly simple requirements such as setting a specified number of required parameter values for a service are not easily modeled by OWL. The greatest benefit, though, probably comes to the end user. They are protected from these development issues and see only the relative easy way in which they can discover data and services or access web pages that themselves rely on the benefits of these underlying technologies.SSWAP provides a flexible method for semantically describing data and services. This is evidenced by the ability to accommodate varied but related interactions with three major yet independent information resources. The model provides the framework to semantically associate independent services regardless of the fact that the underlying data schemas share little in common.Looking forward, cross-species comparisons are important for all three information resources . Integration of controlled vocabulary terms is underway at Gramene and is now being implemented at SoyBase. When incorporation of Gene Ontology and Plant Ontology controlled vocabulary terms into SoyBase are complete, it will be possible to retrieve a list of loci, QTL or genes from either resource based on common GO (Gene Ontology) or PO (Plant Ontology) accession numbers. This will further facilitate cross-species genetic and genomic comparisons by providing another level of semantic equivalence between taxa.LIS: Legume Information System; OWL: Web Ontology Language; PO: Plant Ontology; QTL:Quantitative Trait Loci; RDF: Resource Description Framework; RDG: Resource Description Graph; RIG: Resource Invocation Graph; RQG: Resource Query Graph; RRG: Resource Response Graph; SSWAP: Simple Semantic Web Architecture and Protocol; TO: Trait Ontology; WC3: World Wide Web Consortium; XML: eXtensible Markup Language.The authors declare that they have no competing interests.RTN carried out and prepared the SoyBase SSWAP ontologies and services and participated in the drafting of the manuscript. SA carried out and prepared the Gramene SSWAP ontologies and services and participated in the drafting of the manuscript. DDG created the SSWAP protocol and directed the research, participated in the design of the study and creation of the LIS services, and helped to draft the manuscript. RCS, DW, and GDM oversaw the implementation and conception of the SoyBase, Gramene, and LIS services respectively. All authors read and approved the final manuscript.Table 1. Listing of semantic web services offered by the Gramene Database with their associated input and output data types.Click here for fileTable 2. Listing of semantic web services offered by the SoyBase Database with their associated input and output data types.Click here for fileTable 3. Listing of semantic web services offered by the LIS Database with their associated input and output data types.Click here for file