The analysis and interpretation of relationships between biological molecules, networks and concepts is becoming a major bottleneck in systems biology. Very often the pure amount of data and their heterogeneity provides a challenge for the visualization of the data. There are a wide variety of graph representations available, which most often map the data on 2D graphs to visualize biological interactions. These methods are applicable to a wide range of problems, nevertheless many of them reach a limit in terms of user friendliness when thousands of nodes and connections have to be analyzed and visualized. In this study we are reviewing visualization tools that are currently available for visualization of biological networks mainly invented in the latest past years. We comment on the functionality, the limitations and the specific strengths of these tools, and how these tools could be further developed in the direction of data integration and information sharing. Bioinformatics has evolved and expanded continuously over the past four decades and has grown into a very important bridging discipline in life science research. The quantities of data obtained by new high-throughput technologies, including micro or Chip-Chip arrays, and large-scale "OMICS"-approaches, such as genomics, proteomics and transcriptomics, are vast and biological data repositories are growing exponentially in size. Furthermore, every minute scientific knowledge increases by thousands pages and to read the new scientific material produced in 24 hours a researcher would take several years. To follow the scientific output produced regarding a single disease, such as breast cancer, a scientist would have to scan more than a hundred different journals and read a few dozens papers per day.The underlying data sets show a growing complexity and dynamics and are produced by numerous heterogeneous application areas. The integration of heterogeneous types of data is therefore gaining in importance. Currently different biological types of data, such as sequences, protein structures and families, proteomics data, ontologies, gene expression and other experimental data are stored in distinct databases. Existing databases or data collection can be very specialized and often they store the information using specific data formats. Many of them also contain overlapping but not exactly matching information with other databases, which introduces another hurdle to combine the information. In order to gain insights into the complexity and dynamics of biological systems, the information stored in these data repositories needs to be linked and combined in efficient ways. To address these issues, data integration became a main issue in the past years .The challenge lies very often in the analysis of a huge amount of data to extract meaningful information and use them to answer some of the fundamental biological questions. Given the heterogeneity and the sheer amount of data it is a challenge to detect the relevant information and to provide a way to communicate the findings of the researcher in an efficient and appropriate way.The human brain has evolved remarkable visual processing capabilities to analyze patterns and images. Thus, an interactive visual representation of information together with data analysis techniques is often the method of choice to simplify the interpretation of data. A wide variety of tools was developed over the past years that map data on 2D graphs to visualize biological interactions or relationships between bio-entities.Graphs, as specified by graph theory, represent biological interactions in the form of extensive networks consisting of vertices, denoting nodes of individual bio-entities, and edges, describing connections between vertices. In the simplest example two vertices are linked by only one relationship, but connections can express different types of relationships between two elements, such as an evolutionary relationship, the existence of a shared protein domain, the fact that they belong to the same protein family or that two genes that are co-expressed in an experiment. Biological systems are complex and interwoven and in most cases single-line connections are insufficient to capture the whole range of information contained in a network, because components are often linked by more than one type of relationship. Two proteins might be connected because they act as part of the same protein complex, show similarities in their functional annotation, co-occur under certain conditions or are known to be evolutionary related. In such cases visualization tools based on multi-edged networks offer the possibility to link two vertices by multiple edges, every edge having a different meaning and information value.In this review we describe tools which try to simplify the analysis and interpretation of biological data by transforming the raw data into logically structured and visually tangible representations. The goal of all of them is to find patterns and structures that remain hidden in the raw unstructured datasets.Since the wealth of existing visualization tools makes an exhaustive collection and in-depth discussion of all available software tools impossible, we present a selection of several network viewers, which are broadly applicable. Our survey covers tools invented mainly over the past five years and discusses some of their advantages and shortcomings in order to aid researchers in choosing the most suitable visualization tool for their studies. Finally, we will highlight crucial gaps in the landscape of data visualization, discuss how existing tools could be improved to fill these gaps and lay out the perspectives and goals for the next generation of visualization tools.In the following section we discuss several widely used network visualization tools that have been developed over the past years. The presented tools are selected so as to cover the range of different functionalities and features crucial for data analysis and visualization. While the discussed tools are all broadly applicable, we will highlight their respective strengths and weaknesses if any, and comment on their specific features. Tools for analyzing protein-protein interactions, pathways, gene networks, heterogeneous networks or tools for studying evolutionary relationships between proteins have a very different scope and require a separate and much more detailed analysis which goes beyond the scope this review. Instead we try to provide a broad overview that can help guide users, especially those that are novices to the field of data visualization, towards the most appropriate tool for their research question and type of data.Criteria for the assessment of visualization tools include power, efficiency and quality of network visualizations produced, the compatibility with other tools and data sources, the analytical functionalities offered , limitations in terms of data quantity, broad applicability and user-friendliness. Following the detailed evaluation with respect to these criteria, a one sentence summary highlights the particular strengths of each tool.Medusa is a Java application and available as an applet. It is an open source product under the GPL license.Based on the Fruchterman-Reingold  algorithMedusa has its own text file format that is not compatible with other visualization tools or integrated with other data sources. The input file format allows the user to annotate each node or connection.Medusa is highly interactive. It allows the selection and analysis of subsets of nodes. A text search, which supports regular expressions, can be applied to find nodes. The status of a network can be saved and reloaded at any time but medusa is currently not connected to any data source.The tool was developed mainly to show multi-edge connections where each line represents different concepts of information. Medusa is optimized for protein-protein interaction data as taken from STRING  or proteCytoscape is a standalone Java application. It is an open source project under LGPL license.Cytoscape mainly provides 2D representations and is suitable for large-scale network analysis with hundredth thousands of nodes and edges. It can support directed, undirected and weighted graphs and comes with powerful visual styles that allow the user to change the properties of nodes or edges. The tool provides a variety of layout algorithms including cyclic and spring-embedded layouts. Furthermore, expression data can be mapped as node color, label, border thickness, or border color.Cytoscape comes with various data parsers or filters that make it compatible with other tools. The file formats that are supported to save or load the graphs are SIF, GML, XGMML, BioPAX, PSI-MI, SBML, OBO. Cytoscape also allows the user to import mRNA expression profiles, gene functional annotations from the Gene Ontology (GO) and KEGG. Users can also directly import GO Terms and annotations from OBO and Gene Association files.It is highly interactive and the user can zoom in or out and browse the network. The status of the network as well as the edge or node properties can be saved and reloaded. In addition, Cytoscape comes with a network manager to easily organize multiple networks. The user can have many different panels that hold the status of the network at different time points which makes it an efficient tool to compare networks between each other. It also comes with efficient network filtering capabilities. Users can select subsets of nodes and/or interactions and search for active subnetworks or pathway modules. It incorporates statistical analysis of the network and it makes it easy to cluster or detect highly interconnected regions.Cytoscape main purpose is the visualization of molecular interaction networks and their integration with gene expression profiles and other data. It also allows the user to manipulate and compare multiple networks. Many plug-ins created by users are available and allow more specialized analysis of networks and molecular profiles.3D is written in Java 1.5 and it uses the JOGL system for OpenGL rendering. It is released under the GNU Public License (GPL). A medium or higher range graphics card is necessary to run the software.BioLayout Express3D is a tool for layout, visualization and clustering of large scale networks in both 3D and 2D. It supports both unweighted and weighted graphs together with edge annotation of pairwise relationships. It mainly employs the Fruchterman-Rheingold layout algorithm for 2D and 3D graph positioning and display of the network. A variety of colour schemes render the network more informative and clusters can be easier visualized. Since BioLayout Express3D uses a graphics renderer it is limited in the size of networks it can process.BioLayout ExpressIt comes with a very simple input file format requiring the user to only provide a list of connections. The tool is compatible with Cytoscape and it supports layout, expression, yEd GraphML and sif file formats. It is currently not connected with data sources but in the next versions SBML support will make it compatible with various currently available databases.3D is highly interactive and the user can switch between 2D and 3D representations. Users can move around the current view, zoom in/out, rotate or move the network. In the latest version, the Markov Clustering algorithm (MCL) has become an integral part of BioLayout Express3D for clustering analysis. In this way data are automatically separated in distinct groups labeled by different color schemes.BioLayout ExpressExpress3D offers different analytical approaches to microarray data analysis.BioLayout Osprey is a standalone application running under a wide range of platforms. It can be licensed for non commercial use and but source code is currently not available.Osprey provides 2D representations of directed, undirected and weighted networks. It is not efficient for large scale network analysis but it provides various layout options and ways to arrange nodes in various geometric distributions. The layouts range from the relax algorithm over a simple circular layout to a more advanced Dual Spoked Ring layout that displays up to 1500 – 2000 nodes in a easily manageable format. The user can change the size and the colours of most Osprey objects such as edges, nodes, labels, and arrow heads.Data can be loaded into the tool either using different text formats or by connecting directly to several databases, such as the BioGRID  or GRID The tool provides several features for functional assessment and comparative analysis of different networks together with network and connectivity filters and dataset superimposing. Osprey also has the ability to cluster genes by GO Processes. Network filters can extract biological information that is supplied to Osprey either by the user or by instructions inside the GRID dataset. Connectivity filters identify nodes based on their connectivity levels. These are Minimum, Iterative Minimum and Depth. Finally, Osprey includes basic functions such as selecting and moving individual nodes or groups of nodes or removing nodes and edges.With its various filtering capabilities, Osprey is a powerful tool for network manipulation. The ability to incorporate new interactions into an already existing network might be considered the tool's biggest asset.ProViz is a standalone open source application under the GPL license.It comes with both 2D and pseudo-3D display support to render data. It can manipulate single graphs in large-scale datasets with millions of nodes or connections. Leveraging the Tulip  drawing Graphs are saved and loaded in Tulip, PSI-MI  and IntASubgraphs that are produced by selection, filtering or clustering methods and can be automatically organized into views. With ProViz it is possible to annotate each node and each edge with comments or merge different datasets into a single graph. Users can also enrich the networks by querying available online databases. ProViz uses a controlled vocabulary on bio-molecules and interactions, described in XML format.ProViz has its strength in the area of protein – protein interaction networks and their analysis using arbitrary properties, like for example annotations or taxonomic identifier. Its plug-in architecture allows a diversification of function according to the user's needs.Ondex is a standalone freely available open source application.Ondex provides 2D representations of directed, undirected and weighted networks. It can handle large scale networks of hundred thousands of nodes and edges. It also supports bidirectional connections, which are represented as curves. Moreover, different types of data are separated by placing them in different disks-circles interconnected between each other.Data may be imported through a number of 'parsers' for public-domain and other databases, such as TRANSFAC -20, TRANOndex integrates various filters that selectively add or remove connected nodes from the display according to user selectable rules of connectivity type like distance, level or equivalence. A SubTreeFilter can extract a tree-like sub-graph from a given node. Furthermore, the tool comes with a KnockOutFilter which can be used to determine the most important nodes at any given level. Data for integration is modeled as a suitable framework of concepts  and relations  describe the mapping between them. In addition, a powerful filter is available to import microarray expression level data to globally analyze the relations between the different genes being expressed.Ondex main strength is the ability to combine heterogeneous data types into one network. It is suitable for text mining, sequence and data integration analysis.Pathway Analysis Tools for Integration and Knowledge Acquisition) is a web based non-open source application publicly available for non-commercial use. It has its own license.PATIKA , present functional annotations, identification of homologs from the four species, and links to external web information pages. The protein data are stored in an MS-Access file, easily modifiable by the users to enter their own data.PIVOT can expand the network to display all proteins up to a specified distance, detect the shortest path of interactions or unfold the relationships among "distant" proteins, which respond similarly under a experiment's conditions. It can highlight dense areas of the map and use a window to visualize a subarea of a big networks. It is rich in features that help the users navigate and interpret the interactions map, as well as graph-theory algorithms for easily connecting remote proteins to the displayed map.PIVOT is best suited for visualizing protein-protein interactions and identifying relationships between them, for example homologs.Pajek is a standalone application. It is not an open source application but it is free for non-commercial use. It runs under Windows OS only.It offers 2D representations and pseudo3d representations and supports single, directed and weighted graphs. Pajek is suitable for large scale networks with thousands or even million of nodes and vertices. It comes with a great variety of layout options like circular layout using partitions, circular layout using permutation or circular layout using random coordinates layout algorithms. Direct forcing and energy free layout algorithms , such asIt comes with its own input file format, which is not compatible with commonly used XML formats. Neither is Pajek connected with any biological data sources. The status of the network can be saved any time and it allows export of information in EPS, SVG, X3D and VRML formats.The tool is highly interactive and it incorporates many clustering methods, of which many are not very widely used however. It supports abstraction by (recursive) decomposition of a large network into several smaller networks and it implements a selection of efficient subquadratic algorithms for analysis of large networks. Pajek can detect clusters  in a network, extract vertices that belong to the same clusters and show them separately, shrink vertices in clusters or show relations among clusters.Pajek's main strength is the variety of layout algorithms which greatly facilitate exploration and pattern identification within networks.The field of data visualization currently faces three major challenges: ever increasing quantity of data to be visualized and analyzed, integration of heterogeneous data and the representation of multiple connections between nodes with heterogeneous biological meanings.3D are well equipped to help overcome these limitations. When working with systems biology data, that is highly interconnected and often linked by multiple biological relationships, Medusa and other tools featuring multi-edged networks are the most suitable. Pajek on the other hand is ideal for pattern recognition and to study the properties, such as density, centrality and frequency of nodes, of simple networks with single connections. For biological questions with a comparative focus Osprey is advisable.As the survey shows, each visualization tool has specific features and thus the tools vary in how they address the outlined challenges. When heterogeneity of data is the major challenge, integrative tools like Ondex, Pivot or Medusa offer possible solutions. When the sheer mass, but less the heterogeneity, poses a problem, tools with high resolution and good scaling functionalities, like Cytoscape or BioLayoutExpressPartly, more specialized tools are VisLink , KnowledOne of the issues that most of the visualization tools have to address first is the complexity of the input data. Currently most of the network graph viewers come with their own input file formats to load and store the networks. This makes it difficult to use various tools for the same dataset since the user has to reformat the dataset every time according to the specific tool. This can be a strong limitation in cases where one would like to take advantage of the complementary strengths of different tools. We found that the missing interoperability between tools is one crucial bottleneck in exploring the variety of available methods.Moving towards data integration, a number of common file formats and standard languages to store biological information have been introduced. Datasets that are stored in a standardized format can easily be incorporated into a visualization tool that supports the same format without requiring reprogramming nor understanding of the file format itself. Computer science has identified Extensible Markup Language (XML) as an appropriate format for data visualization because it is readable by both, computers and humans. XML stores information in the form of hierarchical tree structures, which allows fast and efficient searching by humans as well as machines. One other main factor why XML is in many cases a very suitable format is the platform-independent text-based format, which supports Unicode and is based on international standards. A further advantage of XML is the forward and backward compatibility which are easy to maintain. Of course one of the drawbacks of XML schemas is that the inherent redundancy may affect application efficiency due to higher storage, transmission and processing costs.In the following section we discuss the most widely used file formats and standard languages in bioinformatics and chemoinformatics. Many open-source XML-based languages, most notably BioPAX, SBML and PSI-MI, rely on a leveled approaches, meaning that they contain various levels of complexity and specificity. It is a choice of the user to specify the level required to represent the information in their dataset.This "pathway language" is a collaborative effort to create a computer readable data exchange format for biological data. The language was developed to allow distribution, sharing and exchange of information between pathway databases in a standard format by using a specific controlled vocabulary for tagging. BioPAX is based on an ontology of concepts with attributes, which allows to make a more explicit use of the relations between concepts compared to other standards. It is most suitable for the description of protein-protein interactions, genetic interactions, gene regulatory, metabolic and signaling pathways. BioPAX is being developed in a series of levels incorporating different features in each round. The current version has the focus on metabolic networks and molecular interaction networks, were the next development level is trying to implement gene and DNA interactions, signal transduction and genetic interactions. BioPAX is the most expressive language and is based on a rich hierarchy, which as a trade-off can result in a high degree of computational complexity. Being a comparatively new language BioPAX is not yet supported by the majority of tools presented here.The acronym SBML stands for Systems Biology Markup Language and is a machine-readable format for describing qualitative and quantitative models of biochemical networks. The current version of SBML focuses on models for the analysis and simulation of basic biochemical networks. The next release will additionally incorporate the concept of model composition, the description of molecule complexes, layout information and spatial characteristics of the models. Many libraries and tools are available for parsing and editing SBML texts. Furthermore, several converters exist to convert SMBL into BioPAX and vice versa. Having started of as a language to describe biochemical reactions, SBML is now widely accepted and supported by over 100 different software systems worldwide, including systems for modeling and simulation, drawing and visualization tools and databases such as KEGG and BioCyc .PSI-MI stands for Proteomics Standards Initiative Interaction and is a machine readable format intended for the exchange, comparison and verification of proteomics data. There are many tools available for viewing and converting PSI-MI data. The main focus is the definition of molecular interactions such as protein-protein interactions, rather than the description of complete cellular models.CML ,70 is thCell Markup Language  is an XMThe Resource Description Framework, RDF ,74, is aBioPAX, SMBL and PSI MI are the three languages most applicable to biological data . Out of Nowadays, biological projects and experiments become more complex and bigger in scope and data that are produced are magnitudes higher than in the past. The increasing use of high-throughput technologies multiplies the amount of data generated per experiments and rapidly increases the sizes of the databases. In the analysis step we can identify the visualization of data as an already major bottleneck. The pure amount of data and their heterogeneity pose a challenge for efficient visualization tools. The main goal of the visualization tools should be the intuitive representation of data to provide an efficient interpretation and to allow a hypothesis driven planning of the next experiment.The tools represented in this review are applicable to a wide range of problems and their distinct features make them suitable for a wide range of applications. However, despite the continuous improvement of visualization algorithms, most existing visualization methods reach limitations in terms of user friendliness when thousands of nodes have to be analyzed and visualized. The majority of tools discussed in this review can cope with datasets of up to about 5000 nodes without compromising too severely on speed and user friendliness. Yet, results of large-scale systems biology experiments frequently exceed hundred thousands of data points today, and many existing visualization tools are not able to cope with these demands calling for a new generation of visualization tools.In order to improve the scaling for big datasets most layout algorithms follow a heuristic approach instead of an exhaustive implementation. Despite the wealth of existing algorithms, the layout problem still remains one of the crucial bottlenecks in network visualization. Faster and more efficient algorithms are needed to bring especially large-scale networks into a form that can be easily understood and interpreted by the human brain. One way to circumvent the problem is parallelization of layout, clustering and graph theory algorithms that they can handle large networks. One solution could be the implementation of web services or libraries that outsource most of the computational effort and calculations to distant, powerful machines that can run many parallel jobs would greatly speed up the process of visualization and reduce the computational load on local computers. Another solution would be the way that these layout algorithms are written to be written in such a way that they can take advantage of the multi-core CPU or GPU technologies.In addition, the extension of layout algorithms to encompass a third dimension would be one central step towards a new generation of visualization tools. This becomes more important in cases like pathway or heterogeneous data sets visualization. The extra dimension would allow a clearer structure and less cluttered views and could strongly facilitate a better navigation within the network. The extension of layout algorithms into three dimensions could thus render the representation of large-scale networks much more efficient, because 3D space minimizes the chance of crossover between two edges.The third dimension would also offer an opportunity to fill a crucial gap in network data visualization; that is the representation of time. Currently, most network tools do not attempt to visualize time series data  and thusThe rapid growth of data calls for the incorporation of powerful filters into visualization tools. Filters that reduce the noise in a dataset and restrict the user's attention to a core set of nodes of a particular interest could greatly improve visualization. Similarly, more efficient and interactive graphical user interfaces (GUIs) would allow the user to visualize and explore relevant sub networks or limited areas of a whole network without having to sieve through vast data masses. To increase the performance of visualization tools further, efficient handling and allocation of memory is essential. This can be achieved by loading only the necessary parts of the graph into memory. In this way, the amount of data and the taxonomies that can be visualized can be rapidly increased. Of course, Graphical Process Units (GPUs) hardware performance increases over time, something that allows visualization tools to employ more resource demanding algorithms like those handling advanced graphics calculations.The future generation of visualization tools should aim to reduce the gap between analysis and visualization. Most existing visualization tools only incorporate a limited number of data analysis functionalities, making it necessary to constantly switch between different applications. The user has to be aware of the variety of tools that are suitable to analyze his data and must switch between them. Information and data sharing between different tools has become a much simpler task due to standard file formats, which should be supported by newly developed visualization tools. Standard formats that are applicable to many different data types will be key features for the growing need to integrate heterogeneous data into a network. A true marriage of analysis and visualization, however, cannot be achieved merely by the support of multiple, standard file formats. Instead, future visualization applications should directly include several of the analytical functionalities that are available in the presented tools.Ideally, the next generation of visualization tools should be able to present very heterogeneous data coming from databases, experiments and text-mining applications. They need to be able to visualize multi-edged networks, incorporate widely used clustering techniques, pattern recognition algorithms and statistical analysis methods. While technology evolves the visualization tools could explore the wider use of autostereoscopic 3D displays, which allow seeing three-dimensional images without the need of special glasses. A visualization tool designed to integrate most of the aforementioned functionalities would greatly simplify large-scale research in molecular biology and would significantly cut down time and effort spent on data processing and analysis.In summary and to provide some concrete solutions to visualization tool challenges we suggest the following:• Visualization should be able to load and save data using worldwide standard file formats.• Incorporation of appropriate statistical analysis of the networks.• Algorithms that allow comparative analysis of different networks.• Implementation of libraries and services that allow layout algorithms to run in distant powerful computers.• Efficient layout algorithms that are able to use multi-core CPU technology.• Algorithms that implement rendering and graphical calculations in GPU.• Expansion of layout algorithms into 3D space especially for the visualization of pathway or heterogeneous data.• Visualization of the network behavior and its changes over time. Such animations are currently possible using Flash technologies.The authors declare that they have no competing interests.GAP was the main author of the paper. He collected all of the necessary information for the review article and he combined the knowledge by comparing different tools between each other to find their their strengths and their weaknesses.  A-LW was the main writer of the manuscript.  RS was the scientific supervisor of this study. He contributed strongly in correcting and writing parts of the manuscript.


WikiPathways provides a collaborative platform for creating, updating, and sharing pathway diagrams and serves as an example of content curation by the biology community. The exponential growth of diverse types of biological data presents the research community with an unprecedented challenge and opportunity. The challenge is to stay afloat in the flood of biological data, keeping it as accessible, up-to-date, and integrated as possible. The opportunity is to cultivate new models of data curation and exchange that take advantage of direct participation by a greater portion of the community.This combination of challenge and opportunity is especially relevant to the task of collecting biological pathway information. Pathways are critical to understanding the functions of individual genes and proteins in terms of systems and processes that contribute to normal physiology and to disease. Each biological pathway must be hewn from a mass of biological information distributed across multiple publications and databases.The particular challenge of pathway curation is amplified, because pathways are often presented as static images that are not amenable to computation, integration, or data exchange. Furthermore, pathway experts are distributed throughout the world, and most have limited time to learn about complex databases that need their expertise. This challenge can be met by taking the opportunity to develop a new community-based model for pathway curation.One way to engage the community is with a wiki model, as exemplified by Wikipedia . We see The research community is experiencing massive growth in biological data, from genome and metagenome sequencing to high-throughput assays and microarray studies. This growth has created a need for models of data storage and distribution that support a continuous stream of end-user submissions, frequent updates, integrated search across databases, and access to data formats (preferably community standards) that are amenable to computational analyses. By and large, the need is being met for certain types of biological data: sequences go to GenBank and European Molecular Biology Laboratory (EMBL)-Bank, protein structures go to the Protein Data Bank (PDB), and microarray results go to Gene Expression Omnibus (GEO) and ArrayExpress. But as the influx and complexity of biological data continue to grow, so will the challenge of organizing and maintaining these databases.http://www.doaj.org/), public databases [Escherichia coli . And WikiProteins aims to combine automated text mining with community curation to annotate biomedical concepts, including protein functions, interactions, and disease relationships [Fortunately, the biology community can provide an answer that will scale with the challenge: community curation. There is a growing tendency toward information exchange that supports open access, higher-order organization, community-defined data formats, and collaborative online environments. This trend is most apparent with the growing number of open-access journals . WikiPathways is an open, collaborative platform dedicated to the curation of biological pathways. WikiPathways thus presents a new model for pathway databases that enhances and complements ongoing efforts (see Kyoto Encyclopedia of Genes and Genomes (KEGG) at http://www.genome.jp/kegg, Pathway Commons at http://www.pathwaycommons.org/pc/, and [To facilitate the contribution and maintenance of pathway information by the biology community, we established WikiPathways . Buildihttp://www.pathvisio.org and unpublished data). PathVisio provides a basic palette of objects and annotations needed to represent biological processes. Gene, protein, and metabolite objects directly map to biological annotations from multiple public databases through an extensible identifier synonym database maintained at WikiPathways. The editing tool facilitates annotation with keyword search and auto-completion. Relationships between entities can easily be drawn using “smart connectors” that snap into place. Lines can even connect to other lines to intuitively represent catalysis or other mediated processes. Entities can be grouped to represent complexes and related collections of genes. The editing tool also makes it easy to annotate these entities and relationships with peer-reviewed literature references. The “Help” section of WikiPathways provides guidelines and tutorials for how to use the editor and how to best represent pathway information, as well as how to download and use the pathways in GenMAPP analyses.Each pathway at WikiPathways has a dedicated wiki page, displaying the current diagram, description, references, version history, and component gene, protein, and metabolite lists . Any patAfter editing, an updated pathway image is displayed on the wiki page along with the version history and list of components. Users can easily monitor and undo changes, compare differences, and search for overlapping pathways. Any registered user can add a pathway to their “watch list” so that they receive email when the pathway is changed. All changes can be reversed, restoring the pathway to an earlier version. Different versions of pathways can be compared side-by-side using an integrated difference–viewing tool, customized for graphical pathway information. Using the search feature, one can locate particular pathways by name, by the genes and proteins they contain, or by the text displayed in their descriptions and comments. One can also browse the collection of pathways with combinations of species names and ontology-based categories. Currently, WikiPathways contains 544 species-specific pathways for human, mouse, rat, zebrafish, fruit fly, worm, and yeast. The mouse pathways, for example, contain 3,741 unique genes (~13% of the mouse genome). The pathway collection was nucleated with GenMAPP pathways, which were collected over the past decade from GenMAPP users. Now at WikiPathways, the collection is growing and improving through new contributions and curation, at an unprecedented rate, which we expect to dramatically increase as community participation grows.http://www.pathvisio.org). GPML allows researchers to draw and identify the molecular participants in a pathway, as well as the relationships among the participants. GPML is a work in progress, and though it does not yet have the full expressiveness of BioPAX (see the BioPAX Wiki at http://biopaxwiki.org/cgi-bin/moin.cgi) or Systems Biology Markup Language (SBML) [The pathway content at WikiPathways is freely available for download in a variety of data and image formats, including GenMAPP Pathway Markup Language (GPML), which is a custom XML format that is compatible with pathway visualization and analysis tools such as Cytoscape , GenMAPPe (SBML) , it provTo assist pathway authors and curators, we are developing “bots” to survey the content and identify potential inconsistencies, redundancies, and incomplete data. The first of these bots identifies all the genes, proteins, and metabolites in any pathway that are not connected to a synonym database identifier. These reports along with additional curator tools will help contributors to submit high-quality content and make corrections where needed. We also plan to use standard biomedical ontologies to structure the content of WikiPathways and to provide organization that can scale with rapidly growing and interrelated information.Researchers interested in particular interactions or pathways can use WikiPathways as a resource for up-to-date pathway information and as a repository for their own findings that, in turn, are immediately available in multiple data formats for analysis as well as image formats for publication. WikiPathways can be used collaboratively to create, edit, and share pathway information with any colleague who has access to a Web browser. For sensitive content that is proprietary or must first be published as an original finding, pathways can be saved locally in the GPML format, ready to be uploaded and made public at a later time. Expert curators can use WikiPathways to monitor and update pathway information associated with their fields of interest. WikiPathways is also useful to students and professors of biology, providing pathways as educational materials and the editing history of a given pathway as an example of how scientific knowledge iteratively progresses.To encourage participation by the community we have built templates for “User pages” and “Portals.” User pages help users identify themselves and their work, whereas Portals help entire communities of users to identify themselves collectively and focus on particular pathway domains, such as diabetes-related pathways or plant pathways. By using the template, users can build a site within WikiPathways dedicated to their lab, organization, or area of interest within minutes. We are also organizing community curation events as a way to introduce new users to the curation tools and, at the same time, improve the quality of the pathway content. Future community curation events will focus on adding annotation, group representations, and literature references.E. coli and plant pathways currently being converted, and three new Portals under construction. The site has over 250 registered users (10% contributing users) and has attracted developers through the Google Summer of Code program.Even prior to this publication introducing WikiPathways, we have seen strong signs of community participation. Outside of the immediate group of developers, WikiPathways has already attracted ten new mouse pathways, nine new human pathways, six new zebrafish pathways, three new rat pathways, and one Portal for the micro-nutrients community. There are dozens of We envision WikiPathways being part of a broader effort to extend curation capacity to larger groups and communities. This effort does not replace current approaches involving centralized teams of curators, but rather it complements and extends them. Eventually, we would like to see wiki solutions such as WikiPathways used by current databases and curation sources. Our future directions include supporting “reference” pathways contributed by other pathway databases, and private workspaces for groups to collaboratively work on pathways before making them public. One could also imagine organizations installing local instances of WikiPathways for internal projects at research institutes or biotechnology companies. A journal, for example, could host a version of WikiPathways that only contributing authors can edit. Where the same wiki technology is used, there are opportunities for seamless integration and controlled sharing of content when it is ready to be published or released to the public site. We will continue to work toward supporting broad implementations of WikiPathways to promote contributions from established and diverse sources.WikiPathways is an experiment. We have considerable work ahead of us in developing the GPML data model, implementing critical features and, most importantly, building a community of users and contributors. The success of WikiPathways will depend on the overall quality of its content, which will be a function of the infrastructure and administrative principles we use in addition to community participation. Features such as database connectivity, automatic consistency checks, curation tools, reversible edits, the visual difference viewer, and support by literature references will assist in tracking and reverting errant contributions, stimulating curation by the community. We anticipate that lowering the entry barrier for participation will allow for a greater capacity of curation, broader consensus on content, and ultimately, higher quality control. We are confident that WikiPathways will be a powerful resource for the research community and a vital forum for pathway curation. And we are hopeful that it will serve as an example for how the continuing flood of biological data can be managed and utilized by the community to irrigate future hypotheses and discoveries.http://creativecommons.org/licenses/by-nc-sa/3.0/). All source code for WikiPathways and the PathVisio applet is available under the Apache License, Version 2.0 (http://www.apache.org/licenses/). You can download the code from:http://svn.bigcat.unimaas.nl/wikipathwayshttp://svn.bigcat.unimaas.nl/pathvisioWe are committed to open access and open source. All content is available under a Creative Commons License (


Pathway-oriented experimental and computational studies have led to a significant accumulation of biological knowledge concerning three major types of biological pathway events: molecular signaling events, gene regulation events, and metabolic reaction events. A pathway consists of a series of molecular pathway events that link molecular entities such as proteins, genes, and metabolites. There are approximately 300 biological pathway resources as of April 2009 according to the Pathguide database; however, these pathway databases generally have poor coverage or poor quality, and are difficult to integrate, due to syntactic-level and semantic-level data incompatibilities.We developed the Human Pathway Database (HPD) by integrating heterogeneous human pathway data that are either curated at the NCI Pathway Interaction Database (PID), Reactome, BioCarta, KEGG or indexed from the Protein Lounge Web sites. Integration of pathway data at syntactic, semantic, and schematic levels was based on a unified pathway data model and data warehousing-based integration techniques. HPD provides a comprehensive online view that connects human proteins, genes, RNA transcripts, enzymes, signaling events, metabolic reaction events, and gene regulatory events. At the time of this writing HPD includes 999 human pathways and more than 59,341 human molecular entities. The HPD software provides both a user-friendly Web interface for online use and a robust relational database backend for advanced pathway querying. This pathway tool enables users to 1) search for human pathways from different resources by simply entering genes/proteins involved in pathways or words appearing in pathway names, 2) analyze pathway-protein association, 3) study pathway-pathway similarity, and 4) build integrated pathway networks. We demonstrated the usage and characteristics of the new HPD through three breast cancer case studies.http://bio.informatics.iupui.edu/HPD is a new resource for searching, managing, and studying human biological pathways. Users of HPD can search against large collections of human biological pathways, compare related pathways and their molecular entity compositions, and build high-quality, expanded-scope disease pathway models. The current HPD software can help users address a wide range of pathway-related questions in human disease biology studies.HPD  While tSignal transduction events. Common in signalling pathways , these Enzymatic reaction events. Common in metabolic pathways , these events define chemical reactions that metabolites (as either substrates or products) and catalytic enzymes are involved in. Examples of enzymatic reaction events are catabolic reactions (breaking down of larger molecules to produce energy) and anabolic reactions .• Genetic regulation events. Common in genetic regulatory pathways , these events define the dependent relationships between regulatory entities, e.g., a transcription factor that binds to specific DNA binding motifs, and target entities, and a gene whose transcription is being regulated by a transcription factor. In addition to gene regulation events, regulatory pathways may also include sRNA and sRNA target gene regulation.• Collecting and modeling biological pathways are critical for interpreting "Omics" data . For exahttp://www.pathguide.org/today, these resources have been developed with variable degrees of data coverage, quality, and utility , and we currently use α = 0.8 to count varying degree of contributions from calculations based both on the overlap (left item SL) and the cover (right item SR).Here, subnetwork relationship ; top 100 genes/proteins ranked by frequency, and top 100 compounds ranked by frequency. Here the frequency of a molecule entity (i.e. gene/protein or compound) also includes times appearing in same pathways.Click here for fileThis additional file describes the pathway entity-relationship (ER) data model for HPD pathway integrations in detail.Click here for file


Making data available as Linked Data using Resource Description Framework (RDF) promotes integration with other web resources. RDF documents can natively link to related data, and others can link back using Uniform Resource Identifiers (URIs). RDF makes the data machine-readable and uses extensible vocabularies for additional information, making it easier to scale up inference and data analysis.This paper describes recent developments in an ongoing project converting data from the ChEMBL database into RDF triples. Relative to earlier versions, this updated version of ChEMBL-RDF uses recently introduced ontologies, including CHEMINF and CiTO; exposes more information from the database; and is now available as dereferencable, linked data. To demonstrate these new features, we present novel use cases showing further integration with other web resources, including Bio2RDF, Chem2Bio2RDF, and ChemSpider, and showing the use of standard ontologies for querying.We have illustrated the advantages of using open standards and ontologies to link the ChEMBL database to other databases. Using those links and the knowledge encoded in standards and ontologies, the ChEMBL-RDF resource creates a foundation for integrated semantic web cheminformatics applications, such as the presented decision support. The current scientific data deluge, in which datasets grow faster in size and complexity than scientists can keep up with, defines several new challenges for information systems. Scientists wish to discover new, unique, and significant patterns in datasets that explain biological phenomena not yet understood. The discovery of new patterns showing the causes and effects of various biological phenomena is often beyond the scope of single datasets. For example, systems biologists integrate micro-array differential expression datasets to biological pathways, using various other datasets to provide evidence for the links . AnotherChEMBL contains descriptions for biological activities involving over a million chemical entities, extracted primarily from scientific literature. This provides a unique resource for drug researchers ,6. It isthings very generically [linked data[Using the right to download, modify, and redistribute the ChEMBL data, two independent teams have previously mapped the ChEMBL relational database to the Resource Description Framework (RDF), resulting in Chem2Bio2RDF  and ChEMerically . Things nked data. RDF is This paper presents an update on the ChEMBL-RDF data set, including details of the latest structures and ontologies used to map ChEMBL to RDF, along with new use cases, showing links to other datasets using their RDF linked data URIs to further support research in the life sciences.The ChEMBL version used in this paper is ChEMBL 13, which was released on 29 February 2012. The SQL data dump provided by ChEMBL was inserted into a local MySQL database. A set of SQL queries were then executed to generate RDF triples using a set of PHP scripts. The scripts use SQL to query the database and output triples using print statements. Custom PHP scripts have been used as a lightweight solution to provide a Linked Data API via a Apache webserver, as well as to create data files with triples. These scripts are Open Source and are available from the source code hosting service, GitHub . This prHowever, the previous ChEMBL-RDF conversion used custom predicates and classes to represent RDF triples, defining an ad hoc ontology . This imhttp://linkedchemistry.info resource. The Kasabi platform [To expose the ChEMBL-RDF data two approaches have been adopted. First, a SPARQL endpoint is being hosted at Uppsala University, using the Open-Source Edition of the Virtuoso software . Usage iplatform  was usedhttp://rdf.farmbio.uu.se/chembl/onto/\#, and are referenced here using the prefix: chembl.Although many parts of the ChEMBL-RDF 13 dataset now use community based ontologies, some terms from the previous ad hoc ontology are still used when a community alternative is not yet available. These terms were created under the URI namespace We present here the updated ChEMBL-RDF, including a description of the mapping process and the identification of links to other datasets. Because the primary purpose of this paper is to expose the ChEMBL data as linked data, less focus has been placed on ontologically capturing the fine details of the pharmacological literature. This is the reason why many concepts are not mapped to existing ontologies, though also the lack of ontologies with a clear open license, as defined by the Open Definition , restricFor each of the common resource classes, a triple pattern was defined, following the data available in the relational database. Unless the ontological meaning of the data in the database was very clear, the classes are annotated with concepts as implicitly defined by the relational database. For example, the activity concept in the triples merely follows the activity table in the database. As such, the concept of chembl:Activity is implied by the content of that table in the relational database.However, where the database content is well-defined, specific ontologies have been used. For example, where the database specifies literature from which data were extracted, the CiTO ontology is used. The choice of the ontologies is driven by the use cases and compatibility with other Linked Data projects, rather than formal ontological considerations. We have not observed ontological contradictions based on these choices.Figure More than five thousand different activity types, represented by the chembl:type predicate, are captured by the ChEMBL database. The top five types are “Potency” (43%), “IC50” (13%), “MIC” (4.6%), “Inhibition” (3.7%), and “Ki” (3.6%). The activity types in ChEMBL-RDF are currently not available as, or linked to, an ontology. The BioAssay Ontology could be a future option .The activities themselves are measured against assays, which make up a second important resource type. Various assay types are found in the database:Each assay measures activities against one or more particular biological targets. To each target the ChEMBL database associates a confidence score (see the Compound Selectivity example). Because this score is specific for each target, the following construct is used:pro:PR_0000001, verbchembl:ADMET, chembl:CELL-LINE, chembl:NUCLEIC-ACID, chembl:ORGANISM, chembl:SUBCELLULAR, chembl:TISSUE, chembl:UNCHECKED, and chembl:UNKNOWN. The latter two are currently defined as explicit types, thus effectively implementing a closed-world approach.The ChEMBL database recognizes various target types: A target specification can then look like:For drug discovery, the drug-like compounds themselves are the main topic of study. ChEMBL contains many different compound types, mostly small molecules, but also peptides, proteins, antibodies, oligosaccharides, oligonucleotides, and even cells. ChEMBL-RDF follows the approach used by the CHEMINF ontology, and encodes drugs as classes, rather than instances, and they subclass other classes defined by several ontologies for these types. For example, protein drugs subclass the Protein class from the PRotein Ontology (PR_000000001), instead that these drugs are of the Protein type. Each drug is a class itself. Likewise, small molecules subclass the Chemical Entity class from the CHEMINF ontology (CHEMINF_000000) , and oligosaccharides and oligonucleotides subclass their respective matches in the CHEBI ontology (CHEBI_50699 and CHEBI_7754). To ensure that we can look up all entities for which activities are reported, we subclass everything from the BFO 1.1 class MaterialEntity , which iIt should also be noted that all drug-like entities in ChEMBL are not ontologically subclassing a drug class. Instead, we give the entities the role of drug, and only within a particular context would they be a drug. If not used as a medication, they are not a drug. Moreover, the triples only attach this drug role to approved drugs, as defined by the ChEMBL database. The role is triplified using the OBO and ChEBI ontologies in the following manner, where CHEBI_23888 is the ontological entry for “drug role” in ChEBI:The name and synonyms for each drug are provided as labels. If SMILES, InChI, or InChIKeys are given for a drug, then these are provided via the CHEMINF formalism. The CHEMINF ontology has been used here because of its flexibility in defining descriptor types and their origin and meaning. By allowing an ontology of descriptors, we enable the comparison of values between databases semantically. Differences in, for example, the SMILES string may then be explained by the software used for that descriptor.Example output for ChEMBL406142 looks like:Similarly, the properties of small molecules are available from ChEMBL, and as of ChEMBL-RDF 13 these too are exposed in triple format. Like the InChI and InChIKey, these are provided using the CHEMINF ontology. Here is, for example, the logP value :The literature from which all this data was extracted, is replicated from the database, but only a subset of the possible information about that literature is provided. In all cases the PubMed Identifiers (PMIDs) and Digital Object Identifiers (DOIs) are used to link to an authoritative database, as outlined later in this paper. The properties are provided using the BIBO ontology, which is an open source ontology for bibliographic information, compatible with the CiTO ontology:Each release of the ChEMBL database is accompanied by a set of release statistics which help provide a concise content overview of the release in question. The ChEMBL 13 release counts are as follows: • 8,845 targets• 1,143,682 distinct molecules• 6,933,068 activities• 617,681 assays• 44,682 documentsThese counts are important as firstly they show continued growth in ChEMBL data, as seen in all previous releases. Secondly, these counts can help validate any transformations, which have been applied to the relational data model traditionally used to store the ChEMBL data. The following SPARQL queries attempt to provide a high level validation of the ChEMBL-RDF by regenerating the ChEMBL 13 release statistics.A similar SPARQL query, replacing chembl:Target with bfo:MaterialEntity, returns 1,143,682 entities for which activities are captured in ChEMBL, and by using chembl:Assay, the query returns the total of 617,676 ChEMBL assays. It should be noted that this last query returns five entries less than the SQL database, because the triple generation requires the assay type to be defined, which is not the case for those five assays. With chembl:Activity and bibo:Article, we count 6,933,068 ChEMBL activities and 44,681 documents, respectively. The document count is one fewer than appears in the ChEMBL 13 release notes [bibo:Article.The following SPARQL query returns 8,845 ChEMBL targets:se notes , as the Reproduction of the ChEMBL release statistics through querying ChEMBL-RDF with SPARQL provides end users with a high degree of confidence that the ChEMBL-RDF contains the same core content as the source relational database. The development of a validation test suite is being discussed but not yet implemented; testing criteria other then the summarizing counts presented here, would change with each release due to curation events. Interactive, manual curation of the conversion process as employed by the authors already is more effective at this moment.To integrate ChEMBL-RDF with RDF versions of other scientific datasets, we link out to various resources. These links are shown in Figure rdfs:seeAlso, skos:exactMatch, owl:equivalentClass, and owl:sameAs. These relations are ordered according to their increasing level of implication: ranging from “you may also be interested in” with rdfs:seeAlso to “both objects are identical and all properties apply to both objects” for owl:sameAs.The predicates used to capture the links to these databases are The Bio2RDF project provides both resolvable Linked Data URIs using a generic Linked Data server  and SPARFor resources from the biomedical with PubMed identifiers we also link out to Bio2RDF:ChemSpider  is a freskos:exactMatch predicates, while the ChemSpider identifiers are also available via a CHEMINF representation:Link mappings are provided with  Notice here the use of owl:equivalentClass since in CHEMINF molecules are defined as classes rather than instances.The InChI is a unique identifier for  organic molecules, and has been previously used to define unique URIs for molecules ,25. WhilIn addition to the PubMed identifiers used to link from literature references to Bio2RDF, ChEMBL provides DOIs, which we use to link out to the RDF provided by CrossRef :res:r2032 owl:sameAs <http://dx.doi.org/10.1016/0960-894X(96)00111-4>.ChEMBL-RDF can be used to explore interrelated scientific datasets, and we present here five applications, describing how the SPARQL endpoint can be used, the combination of the ChEMBL data with other life sciences databases, the use of the bibliographic information in calculating citation statistics, and how to integrate ChEMBL-RDF with ChemSpider to provide an extension for the decision support platform in Bioclipse.http://localhost:8080/chembl/, then a request for the article with identifier “a31863”, http://localhost:8080/chembl/article/a31863, will be resolved using the webapp from the database using the full original URI, http://linkedchemistry.info/chembl/activity/a31863. If the user requested an RDF document using content negotiation, the original URIs will be unchanged, however, if the user requested an HTML document, the results will contain both the original RDF triples, represented using RDFa, with links that resolve using the user’s local machine.This first application shows how ChEMBL-RDF can be integrated into other linked data services. The Linked Data server used by Bio2RDF has been reconfigured for ChEMBL to provide URL based services for standard URI resolution, along with textual and link-based searches . A Java http://localhost:8080/chembl/linkstonamespace/-targetns/originalns:identifier, and backward links, originating in other databases, such as LODD, Bio2RDF , and Chem2Bio2RDF. These services are vital to efficiently navigate the Linked Data web, as it is both impractical and inefficient to require users to crawl the entire web before they can discover relevant resources. These services are currently only supplied as web services from ChEMBL and Bio2RDF, but it is hoped that similar services will be provided by other scientific Linked Data providers in the future. Datasets that are available in SPARQL endpoints can be queried for links efficiently using simple queries as demonstrated in the ChEMBL web application.These link services enable the ChEMBL application to derive both forward links, originating in ChEMBL, e.g. IC50 < 10 μm). The top two most common targets of these drugs are HERG and Serotonin transporter. HERG is a well-known off-target accounting for cardiovascular problems, while the activity of Serotonin transporter may also be associated with significant cardiovascular adverse effects [http://chem2bio2rdf.wikispaces.com/Integrate+with+ChEMBL and can be executed in the SPARQL endpoint [A second resource in the linked open data network into which ChEMBL-RDF was integrated is Chem2Bio2RDF. Chem2Bio2RDF is a single RDF repository covering over twenty public data resources pertaining to drugs, chemical compounds, carcinogens, protein targets, genes, diseases, side effects, pathways and their relations . The ent effects . The tarendpoint .http://www.citedin.org) [Using the SPARQL endpoint, it is easy to integrate data from ChEMBL into other web resources, such as CitedIn (din.org) . While tdin.org) . ChEMBL din.org)  which isDetails button that links back to a ChEMBL webpage providing a Document Report Card showing in more detail what targets, assays, compounds, and activities in the database are linked to that paper.The CitedIn summary for ChEMBL also provides a The use of the RDF open standard also simplifies the integration of data resources with scientific software, as we have shown previously ,37. We hBioclipse Decision Support (Bioclipse DS)  is a useIn this study we extended Bioclipse DS with remote access to both ChEMBL-RDF and ChemSpider. This enables users browsing chemical structures in Bioclipse to look up nearest neighbors in ChemSpider via the ChemSpider Web API (SOAP), and for the found compounds to be used to query ChEMBL-RDF for available interaction data. The results are presented alongside predictive models in Bioclipse  and low affinity to protein B .Designing a molecule that is selective in binding to one target rather than another is often considered a successful outcome in a drug design process. If the target in question is a protein, high sequence similarity between the binding site and that of other proteins will make such selectivity more challenging. That is, the molecule being designed may also bind to the equivalent binding site in the closely related protein target, and this may lead to undesirable consequences. The ChEMBL data model links molecules to targets using different activity types recorded in the literature. Using activity types that act as a measure of binding affinity, such as The following SPARQL query identifies a set of 65 molecules that selectively bind Human Cyclin-Dependent Kinase 2 (UniProt: P24941) over Human Cyclin-Dependent Kinase 4 (UniProt: P11802), based on data curated from the literature and stored in the ChEMBL-RDF data model.Analysis on the set of molecules returned by this query can be used to help identify small molecule features, which may increase target-binding specificity. For queries which link small molecules to targets, by traversing bioactivity data in the ChEMBL database, it is also important to consider the parameters associated with the assay to target mappings. These additional parameters include a relationship type, a multi flag (for poorly defined targets) a complex flag (for protein complex targets) and a curation level. These different factors are summarized in the ChEMBL confidence score, which ranges from 9 (direct single protein target) to 0 (uncurated). In order to return the largest possible dataset, the confidence score has been ignored in this example compound selectivity use case.This paper shows the evolution of the ChEMBL-RDF resource in standardizing access to the ChEMBL data in a machine-readable manner using semantic web technologies. We showed here that the conversion into RDF allows a number of novel integrations with other resources. It should be clear that these applications can be made using other technologies too, but using the RDF standards it is easier to implement them. More importantly, the solutions will be generally applicable to any system using these open standards. However, a number of choices need discussion.Much could be argued about the choice of ontologies, and the lack of an ontology in places where we decided to use custom classes and predicates. Most of the choices are based on pragmatic approaches to enable solutions to the range of use cases we describe here. It should be noted, however, that the ChEMBL database captures a wide variety of experimental studies and that semantics are not fully specified in the primary source from which the ChEMBL data was extracted. As it is not possible to infer the original intentions in these cases, it is our opinion that it is better to leave some semantics undefined. During the development of the applications some incorrect assumptions have been discovered and fixed, but in the main we have tried to avoid making assumptions if they are not generally known to be valid. Therefore, the goal of this work is the conversion of the ChEMBL data into RDF, and not an ontological modeling of the domain.One case where Linked Data publishers must avoid implying strong semantics in many cases is in relation to links to other databases. When linking between two databases based on a shared identifier, Linked Data publishers make assumptions including syntax assumptions that the identifiers are correct and present in the target database, along with semantic assumptions inferring that the that entries identified in the target database have a scientifically meaningful relationship with the current database. It is for this reason that the ChEMBL-RDF dataset uses different predicates for linking to various databases. This problem, however, is general, applying also to other cases where databases link out to third-party sources using identifiers .Also debatable is the choice of ontologies used to describe the dataset in RDF. As an example, in this release of the ChEMBL-RDF data, we adopted the cheminformatics ontology, CHEMINF. Using this ontology we can encode numeric and non-numeric descriptors of chemical entities in a consistent manner. This method is used to consistently expose identity descriptors such as the SMILES string, InChI, and InChIKey, and molecular properties including the logP, rotatable bond count, and hydrogen bond donor and acceptor counts. However, the design of this annotation is more general than may be needed for a translation of the data in the ChEMBL relational database into RDF. Although this results in more complex SPARQL queries, it allows generic queries that may not otherwise be easy to construct. While the selection of ontologies used was found to fit well with our applications, we anticipate that we will select more expressive ontologies in future iterations of the project.50 and K i endpoint are closely related. Similarly, measurement units are currently provided as string literals, making it difficult to compare concentrations when some are given in nM and others in mM. In the future it may also be possible, relying on some assumptions, to crossmatch activities expressed in μg/ml with query concentrations in nM. These additional semantics are necessary to translate raw experimental data into semantically useful, machine-readable information.Some information in the databases has not yet been annotated using standard ontologies. For example, the activity types in ChEMBL, such as “IC50”, “EC50”, and “Potency”, are not exposed in the RDF dataset, meaning that it is not yet possible to reason or group over similar activity types. When the activity types are exposed, queries could be constructed based on the knowledge that the ICTwo ontologies were recently developed that may address these issues for ChEMBL-RDF in future. Firstly, the BioAssay Ontology, which addresses many of our remaining concerns around making the experimental data machine readable. For example, it provides an ontology of activity types. Secondly, the Quantity, Unit, Dimension and Type (QUDT) ontology which makes it possible to express measurements in a standardised machine readable way . Both apTaking these points into account, we believe that the adoption of RDF and community based ontologies allows novel use cases to be addressed using the ChEMBL data that would not otherwise be achievable. More importantly, the adoption of explicit semantics simplifies integration with other resources, as others can more easily understand the intent and purpose of each part of the ChEMBL dataset. This is a requirement which is essential to the efforts of cheminformatics researchers wishing to infer knowledge from ChEMBL as well as other datasets.The ongoing ChEMBL-RDF project is Open Source making it possible to others to convert ChEMBL to RDF too. Technically, the choice of PHP makes it easy to share functionality between the here described conversion scripts, and PHP-enabled web services, used to provide the Linked Data interface to the ChEMBL-RDF data, as is fast enough for practical uses. These scripts do not use a library for handling RDF data. While using such libraries increases the dependencies for the scripts, they also ensure the generation of syntactically correct RDF triples. Continued development of the ChEMBL-RDF scripts, however, has started using RDF libraries for this purpose specifically for triple generation of compound names, removing code in the scripts to escape special characters, like double quotes.Finally, the ChEMBL project will provide RDF versions of ChEMBL, while others continue exposing ChEMBL as RDF too . In the future various versions may arise, exposing the data in different ontologies, supporting current community needs, however, convergence is expected after a period of experimenting with ontologies that may not prove to be practically useful. The availability of Open Source scripts ensures that the community can contribute to the process of choosing a practically useful set of ontological terms.The authors declare that they have no competing interests.http://linkedchemistry.info/ host. OS, AJW, and VT developed the nearest neighbor application of Bioclipse and ChemSpider. JH supported the project with CHEMINF representations. PA integrated ChEMBL-RDF into Bio2RDF. BC, DW contributed the Chem2Bio2RDF use cases. All authors contributed to the continued development of ChEMBL-RDF, as well as to the writing of the paper, and approved the final version. All authors read and approved the final manuscript.EW initiated the project, created the initial RDF version of the ChEMBL data, and encouraged the use cases. AW extended CitedIn to support citation info in ChEMBL-RDF and supported setting up the


Arabidopsis thaliana is one of the most popular experimental plants. However, only 40% of its genes have at least one experimental Gene Ontology (GO) annotation assigned. Systematic observation of mutant phenotypes is an important technique for elucidating gene functions. Indeed, several large-scale phenotypic analyses have been performed and have generated phenotypic data sets from many Arabidopsis mutant lines and overexpressing lines, which are freely available online. Since each Arabidopsis mutant line database uses individual phenotype expression, the differences in the structured term sets used by each database make it difficult to compare data sets and make it impossible to search across databases. Therefore, we obtained publicly available information for a total of 66,209 Arabidopsis mutant lines, including loss-of-function (RATM and TARAPPER) and gain-of-function (AtFOX and OsFOX) lines, and integrated the phenotype data by mapping the descriptions onto Plant Ontology (PO) and Phenotypic Quality Ontology (PATO) terms. This approach made it possible to manage the four different phenotype databases as one large data set. Here, we report a publicly accessible web-based database, the RIKEN Arabidopsis Genome Encyclopedia II , in which all of the data described in this study are included. Using the database, we demonstrated consistency (in terms of protein function) with a previous study and identified the presumed function of an unknown gene. We provide examples of AT1G21600, which is a subunit in the plastid-encoded RNA polymerase complex, and AT5G56980, which is related to the jasmonic acid signaling pathway. Arabidopsis thaliana is one of the most commonly used experimental plants, and many techniques, tools and detailed genomic data are available for working with it. Many studies have been performed on this species. The Arabidopsis Information Resource  provides a compilation of such information and has enhanced gene annotations by literature curation (curation . TAIR prcuration  includincuration . Howevercuration . To enhaActivator (Ac)/Dissociation (Ds) transposon system makes it possible to generate mutants with a high proportion of single-copy transposon insertions. This system requires the production of a large number of mutant lines to obtain genome-wide coverage. Nonetheless, the single insertion site in each line can be easily determined, thereby simplifying the production and subsequent genetic analysis of single-gene knockout series , houses a total of 4,000 transposon insertion lines, each of which contains a homozygous Ds transposon mutation in a gene or promoter region; these genetic lines have been examined systematically for visible phenotypes at various growth stages of the plant (http://genetrap.cshl.org/) from Cold Spring Harbor Laboratory, contains data from 16,000 lines that each carries a unique insertion of a gene trap (GT) or enhancer trap (ET) transposable Ds element that both disrupts gene function and can be used to monitor gene expression (http://nazunafox.psc.database.riken.jp/) and RiceFOX (http://ricefox.psc.riken.jp/) at RIKEN contain a total of 14,000 and 18,000 plants, respectively, that overexpress introduced fl-cDNAs from Arabidopsis and rice based on the FOX hunting system , which includes information on plant anatomy and development stage (http://obofoundry.org/wiki/index.php/PATO:Main_Page), which includes phenotypic annotations  is an effective way to enrich the gene annotation. Furthermore, mapping phenotypic descriptions onto ontology terms may enable the comparison of phenotypes across different species as well as among genes of a single species . In factnt stage , and pheotations .Ds transposon mutant lines  by mapping descriptions into PO and PATO. We have developed an updated version of the RIKEN Arabidopsis Genome Encyclopedia (RARGE), a database of fl-cDNAs and nt lines , which hDs transposon mutant (RATM) lines  lines , 16,337 M) lines , 14,069 M) lines  and 18,6M) lines   and 5,628 (16.9%) disrupted genes, respectively, and the AtFOX and the OsFOX lines showed 2,976 (8.9%) and 4,177 (12.5%) induced genes, respectively. Consequently, data sets were obtained in which loss-of-function lines had 12,558 (37.7%) disrupted genes and gain-of-function lines had 6,382 (19.2%) induced genes. There were 16,015 (48.1%) genes that were disrupted or induced by either loss-of-function or gain-of-function mutations, respectively, and 2,925 (8.8%) genes that were disrupted and induced by both loss-of-function and gain-of-function mutations, respectively .Table 4Supplementary Tables S1–S4).All four resources used in this study include data obtained from the observation of plants. To integrate these phenotypic data, we mapped each phenotype description to a pair of PO and PATO terms (whole plant (PO:0000003), seedling (PO:0008037), root (PO:0009005), vascular leaf (PO:0009025), stem (PO:0009047), flower (PO:0009046), fruit (PO:0009001), seed (PO:0009010), trichome (PO:0000282), 0 seed germination stage (PO:0007057), whole plant flowering stage (PO:0007016) and sporophyte vegetative stage (PO:0007134)] and 10 subclasses . Note that throughout the paper, ontology terms are in italics. PATO terms were organized into 39 classes (Supplementary Table S5). For example, cauline leaf, rosette leaf, and stipule belong to vascular leaf as a higher class.Although PO is structured as a multilevel hierarchy, we expressed information on each tissue or developmental stage using up to two PO level terms. As a result, the phenotypic descriptions in the data set were mapped to PO terms organized within 12 classes  was obtained from http://pato.googlecode.com/svn/trunk/quality.obo.The data sets of mutant line information were obtained from the public databases detailed in Transposon flanking sequences were subjected to a BLASTN search against the Arabidopsis genome sequence from the TAIR10 genome release . The posThe partial sequences of the introduced fl-cDNAs were subjected to a BLASTN search against Arabidopsis fl-cDNA sequences  or rice seed (PO:0009010) and the PATO term present in fewer numbers in organism (PATO:0001997).We manually mapped the original phenotype descriptions to ontology terms paired with PO  and PATOhttp://www.perl.org/) with the web application framework Catalyst  and the web server interface PSGI/Plack (http://plackperl.org/). The relational database management system was MySQL (http://www.mysql.com/). This web application system is capable of running on a UNIX-like operating system such as Linux (http://www.linux.org/). The web pages were written in HTML, cascading style sheet and Javascript with the YUI library (http://yuilibrary.com/).The integrated phenotypic data were stored in the database and implemented in the web application. All programs for manipulating data, such as those used to query the database and generate web pages, were written in Perl (Supplementary data are available at PCP online.Japan Society for the Promotion of Science [a Grant-in-Aid for Young Scientists (B) (18700106 to T.S.)].This work was supported by the


With the continued growth in the volume both of experimental G protein-coupled receptor (GPCR) data and of the related peer-reviewed literature, the ability of GPCR researchers to keep up-to-date is becoming increasingly curtailed.We present work that integrates the biological data and annotations in the GPCR information system (GPCRDB) with next-generation methods for intelligently exploring, visualising and interacting with the scientific articles used to disseminate them. This solution automatically retrieves relevant information from GPCRDB and displays it both within and as an adjunct to an article.This approach allows researchers to extract more knowledge more swiftly from literature. Importantly, it allows reinterpretation of data in articles published before GPCR structure data became widely available, thereby rescuing these valuable data from long-dormant sources. Recent decades have seen a massive increase in the amount of data available to researchers. Revolutionary high-throughput technologies have enabled data production on an unprecedented scale in many areas of the life sciences. To accommodate this increase in experimental data, we have also witnessed a database explosion, with both the number and size of specialised biological repositories growing enormously over the years . Most ofGPCRDB  is one oMuch of the routine practice of communicating scientific knowledge is conducted through the process of reading and writing scientific papers, an invaluable method that has been used successfully for hundreds of years. However, it is now widely acknowledged that this approach imposes considerable constraints upon the type and quantity of biological information published: specifically, the data used to generate hypotheses and to perform experiments are not readily accessible to fellow researchers who use the results of their peers' publications.Relating information between databases and literature has become increasingly difficult with the growing quantities of data and documents available, and it is evident that new solutions are required to address this problem ,6. When et al. [de facto standard format of scientific literature: Adobe's Portable Document Format (PDF). This is by far the most popular format for peer-reviewed science communication, for reasons that include the benefits of their permanence, device independence, and ubiquitous support and good readability. It therefore makes sense to exploit the PDF; the software tool Utopia Documents [With the goal of bridging the gap between data and scientific articles, several initiatives have explored how to enrich scientific literature with repository data, either by manual annotation -9 or by et al.  system, ocuments ,13 was dWe report here a technology that allows on-the-fly annotation of GPCR-related PDF articles. By fully integrating the Utopia Documents PDF reader and the GPCRDB information system, we can present to the scientist, in a non-intrusive way, all possible data and information related to the topics discussed in the article at hand. This makes possible a paradigmatic change in the relationship between the PDF-reading scientist and Internet-based data resources, alleviating the troubles associated with navigating the many links between existing data and information in this field. The scientist neither struggles to get access to information related to topics within an article, nor is swamped by unnecessary information that still needs disambiguation; only data and information relevant to the topic of the article is made available.The system comprises three parts: the Utopia Documents reader; the GPCRDB; and a module that performs the communication between the two. Users can annotate a document with GPCR-specific information with a single click, but, behind the scenes, the PDF annotation is a multi-step process:1. Extracting the text from the PDF document2. Identifying concepts 3. Creating relevant annotations based on GPCRDB data4. Adding the annotations to the documentet al. [1 and 4 are carried out by Utopia Documents, while 2 and 3 are performed by GPCRDB  must be unambiguously identified to be able to present users with relevant information. Clearly, it is important to ensure that terms in the PDF are correctly mapped to terms in GPCRDB. Concepts must be found in the text and coupled to a unique database identifier, a process referred to as lization ,15. In trounding -18: e.g.It is beyond the scope of this article to provide an exhaustive explanation of all algorithms and heuristics involved in normalisation and grounding. However, at the heart of both tasks lies the use of a metric based on word distances, in which combinations of terms that are close together in the text are favoured over more distant combinations. The distance metrics are combined with a number of heuristics, filters and validations. Some of the heuristics have been described elsewhere ,18,19, wProtein identification is the non-trivial task of pinpointing words that represent a gene or protein. We use a dictionary-based approach. Dictionaries are populated with gene identifiers, protein identifiers and protein descriptions from all GPCRs in GPCRDB that are present in UniProtKB:Swiss-Prot . UniProtIdentifying point mutations and residues involves two subtasks. First, it is necessary to identify the mutation or residue terms discussed in an article. Second, the identified residues and mutations must be grounded.Three entities in the text need to be related to identify point mutations: the wild type residue, the mutant residue, and the position in the sequence where the substitution occurred. Point mutations are represented in the literature in a variety of ways. The most common representation is in the form 'XnY', where × is the single-letter code for the wild-type residue, n its location and Y the single-letter code for the substituted residue . However, there are many variations on this theme, ranging from the use of three-letter amino acid codes, as in 'Asp98Phe' and 'Asp98 → Phe', to more exotic notations, like 'Asp98-Phe98', 'D98 to phenylalanine' and 'Asp 98 was mutated to phenylalanine'. Regular expressions are used to identify residues and mutations.Articles about GPCRs refer to mutations and residues using one of four numbering schemes. The most frequently used are the standard sequential numbering scheme and the scheme of Ballesteros-Weinstein . Other aSome articles discuss residues and mutations using an article-specific numbering scheme. Often, standard sequential residue numbers are used that do not exactly map to the protein sequence in the database. This mismatch can arise owing to inclusion of signal peptides in the sequence, different isoforms, etc. In these cases, there is an offset by which the residue numbers must be corrected to obtain a valid mapping to the reference sequences in the database. A regular expression-based approach is taken to calculate the right offsets. When multiple offsets are possible , the offset is chosen that is closest to the residue numbers mentioned in the text.et al. [Residues and mutations are grounded mainly based on proximity to proteins mentioned in the text, favouring combinations that occur within the same sentence. Validation of these combinations is performed by comparing the identified residues and mutations to the sequences of the candidate protein mentions, using similar approaches to those discussed by Horn et al. . Where gGPCRDB contains a substantial amount of information, but for this to be useful, users require more than just large collections of 'data'. For the three concepts for which we provide annotations , we have selected data, links and knowledge that we believe will benefit users when reading an article, allowing them to link effortlessly from the text to all relevant information in GPCRDB. Once a paper is annotated, red marks on the side of the pages indicate where annotated concepts are located, as shown in Figure Protein annotations contain links to a number of sources Figure . A link Protein annotations also contain links to UniProtKB for a detailed report on the protein, and, if available, a link to Ensembl for the genomic view. We provide a list of synonyms for the protein names. Structural information on GPCRs is still limited, but we provide links to structures when they are available. If oligomerisation information is available, we provide links to pages that contain the relevant details.Residue annotations contain various links and a selection of data pertinent to that specific residue Figure .et al. The sequential residue number is also shown. Sequential residue numbers in GPCRDB annotations may not be identical to residue numbers described in an article. If the software has detected a mismatch and made the appropriate corrections, this will be noted in the annotation. A one-line description of the location and interactions of the residue is given, based on manual analysis of currently available protein structures. A three-dimensional cartoon image of a GPCR structure is provided, in which the position of the residue is highlighted. This allows readers to directly place the information in the article in a structural context. If the residue is located in a structurally conserved region of the Class A GPCRs, and thus has a general residue number in GPCRDB, a link is given that will create a YASARA scene containing the homology model of the protein with the residue highlighted in the structure. If the residue is part of a reported oligomerisation interface, links to more detailed descriptions of that interface are included. For each residue, a link is provided to a GPCRDB page summarising all known data and information for that residue, including information about available mutations at that position, and mutations at equivalent positions in other proteins.The protein name is linked to its protein detail page in GPCRDB. The residue type is displayed, together with its residue number in several numbering schemes. General residue numbers are provided in two different schemes: the commonly used Ballesteros-Weinstein scheme, and that of Oliveira The mutation annotations also link to the protein detail page. If GPCRDB contains information about the same mutation from other literature, links to the relevant PubMed pages are provided. This way, users can immediately look up literature that discusses the same mutation. If GPCRDB contains mutations at the same position in the same protein, but with a different mutated residue type, links to relevant GPCRDB pages are provided.A link to a page that displays mutations at the same  position in other proteins is always present. In addition to links to other literature or other mutations, GPCRDB contains a large amount of descriptive information about the effects of mutations - such information has been culled, over the years, from painstaking analysis of the literature, and manually added to the knowledgebase in the form of short summaries. Where such information is present in the GPCRDB for the mutation at hand, this information is provided automatically in the annotations displayed in the side-bar. If the mutation is located at a position in a structurally conserved region of the Class A GPCRs, and thus has a general residue number in GPCRDB, a link is given that will create a YASARA scene containing the homology model of the protein, with the wild-type and mutated residues highlighted in the structure. In addition to mutation-specific information, mutation annotations also contain general information about the mutated residue. This residue-specific annotation is identical to the information displayed within residue annotations Figure .With this work, we take the integration of literature data in GPCRDB one step further. Using the PDF reader, when a mutation is identified in a scientific article that is not yet known to the GPCRDB, this mutation is automatically integrated into the GPCRDB, making this information available from both the GPCRDB website as well as from the annotations within Utopia Documents. Thus, when a user reads another article discussing a mutation at the same position, links to the newly integrated article will appear as an integral part of the annotation display, allowing immediate navigation to the appropriate literature.In addition, behind the scenes, for all articles annotated using the reader, sentences are extracted in which mutations are mentioned. This information, which effectively provides an at-a-glance summary of the article with respect to that mutation, is stored in the GPCRDB and made available from the GPCRDB website. Because we strive to include only the most highly relevant data and information in the annotations offered by Utopia Documents, this automatically extracted text is not displayed with the PDF reader, and is only accessible through the GPCRDB website.The integrated view of literature and data offered here is invaluable when reading articles published before the first X-ray structure of a GPCR, that of bovine rhodopsin , became By contrast, the annotation indicates that this residue, located in TM IV, points towards the membrane and possibly interacts with cholesterol Figure . This isThe point here is not that the Wieland model is incorrect; as stated before, most models were wrong before the structure of bovine rhodopsin was published. What is important is that this software can be used to detect these types of historical artifacts, relieving scientists of the task of validating the results of 'old' literature against current knowledge. It is important to emphasise that the published experimental data are still valuable, but need new interpretation: in a sense, the software rescues old data from articles that contain interpretations that we now know to be incorrect. With this tool, therefore, scientists can now focus on providing new views on experimental data in the light of current knowledge. For example, knowing that Trp161 is conserved and interacts with cholesterol in some receptor structures allows for the hypotheses that this residue is functionally important for dimer formation, potentially through cholesterol. Other hypotheses can be imagined too, but the only thing that seems certain is that Trp161 is not directly involved in ligand binding.The system presented here is likely to be useful in a number of different scenarios. For example, for biocurators, it offers a rapid means of accessing reliable, contextualised GPCR-relevant data: this would not only benefit future curators of GPCR-specific resources like GPCRDB, but would also facilitate the work of those who annotate more general repositories, such as UniProtKB:Swiss-Prot or InterPro . For jouThe work presented here was developed in response to the need to achieve tighter coupling between published articles and their underlying data as a crucial step towards knowledge discovery. The strength of our approach lies in its ability to present users  with current, integrated, validated, internally consistent data and information in the context of literature being read. The annotations provided are extensive and detailed, and, importantly, include concepts such as 'residue A interacts with the ligand', 'residue B is implicated as playing a role in oligomeric interactions', 'residue C is located in TM4 and points to the lipid environment', 'residue D is conserved in its sub-family', etc. A substantial amount of knowledge is sequestered in such annotations. Taking mutation annotations as an example, although trivial at first glance, the mapping of sequential residue numbers to those of general residue numbering schemes saves researchers large amounts of time in looking up the residue mentioned in the database and finding its associated general residue number. In order to reliably transfer information between different proteins, this is a necessary but very time-consuming process. The structural information consists of data from a variety of different manually- or automatically-generated sources, and provides scientists with a structural perspective on the concepts in the text, making it much easier to critically assess the authors' interpretations of experimental results, which is crucially important when reading old literature. Linking mutation data to other articles in which similar mutations have been discussed saves users considerable effort. This is an onerous task to perform by hand, owing to the necessary conversions between various numbering schemes and residue numbering offsets. To summarise, this work helps GPCR researchers to optimally extract new knowledge from scientific literature by automatically putting data from a PDF article in the wider context of the total body of knowledge related to GPCRs.Project name: GPCR-specific PDF readerhttp://www.gpcr.org/7tmProject home page: Operating systems: Mac OS (10.5 or newer), Windows (XP or newer), Ubuntu linux (10.4 or newer), Debian linux (6.0 or newer)Programming language: C++, Java, PythonOther requirements: Nonehttp://www.getutopia.com/documents/License.txtLicense: Any restrictions to use by non-academics: NoneMCSIS: Molecular Class-Specific Information System; GPCR: G Protein-Coupled Receptor; PDF: Portable Document Format; SOAP: Simple Object Access ProtocolBV drafted the manuscript and wrote the GPCRDB-specific software. BV, DT, PM and SP participated in the writing of the Utopia Documents plugin. DT, PM and SP wrote the Utopia Documents software and adapted it to be used with the GPCRDB plugin. GV supervised the project. GV & TA helped write the manuscript and draft Figure