One of the consequences of the rapid and widespread adoption of high-throughput experimental technologies is an exponential increase of the amount of data produced by genome-wide experiments. Researchers increasingly need to handle very large volumes of heterogeneous data, including both the data generated by their own experiments and the data retrieved from publicly available repositories of genomic knowledge. Integration, exploration, manipulation and interpretation of data and information therefore need to become as automated as possible, since their scale and breadth are, in general, beyond the limits of what individual researchers and the basic data management tools in normal use can handle. This paper describes Genephony, a tool we are developing to address these challenges.We describe how Genephony can be used to manage large datesets of genomic information, integrating them with existing knowledge repositories. We illustrate its functionalities with an example of a complex annotation task, in which a set of SNPs coming from a genotyping experiment is annotated with genes known to be associated to a phenotype of interest. We show how, thanks to the modular architecture of Genephony and its user-friendly interface, this task can be performed in a few simple steps.Genephony is an online tool for the manipulation of large datasets of genomic information. It can be used as a browser for genomic data, as a high-throughput annotation tool, and as a knowledge discovery tool. It is designed to be easy to use, flexible and extensible. Its knowledge management engine provides fine-grained control over individual data elements, as well as efficient operations on large datasets. New experimental technologies and high-throughput analysis methods produce vast quantities of data with each experiment. Systems biology approaches investigate biological processes on a large scale, relying on the measurement and analysis of thousands of variables in order to elucidate the structure and behavior of complex biological systems. Online databases store an exponentially increasing amount of information, from raw DNA sequences to high-level observations on genotype/phenotype correlations. The shift from hypothesis-based to hypothesis-free research that is made possible by these technological and methodological advances opens up unprecedented new opportunities for studying biological systems on a large scale, at a low cost, and with a holistic perspective that promises to expand our understanding of biological processes and of their connections with clinically relevant outcomes.Modern biomedical research is an increasingly In order to take advantage of this paradigm-changing evolution, researchers will increasingly need effective, practical tools to handle very large volumes of heterogeneous data, both generated by their own experiments and retrieved from publicly available repositories of genomic knowledge . Integraad-hoc analysis methods, able to address the well-known problems related with the statistical significance of results based on a very large number of observations, it is apparent that all phases of the scientific discovery process  will have to be adapted to this new reality. The post-genomic era will increasingly require methods and tools able to automatically link new observations and findings to preexisting knowledge.While a large amount of work is under way to develop Finally, new data storage and retrieval systems will need to be developed and adopted in order to handle the unprecedented volumes of data and information being generated in an efficient and productive way. Knowledge and data are represented using nomenclatures, classification schemes and annotation formats that are constantly evolving and often incompatible with each other. Creating, storing and manipulating datasets consisting of hundreds of thousands of records, integrating knowledge from multiple heterogeneous sources, combining and mining data in novel ways for exploratory research, are all tasks that can represent a significant bottleneck for an average researcher who is not an expert in database usage or programming .The ability to effectively address the challenges outlined above will have a direct, dramatic impact on the speed, accuracy and effectiveness of scientific progress in all areas of the life sciences. We are therefore working on developing tools to facilitate the discovery process in high-throughput biomedical research, by providing high usability and effective automation of complex tasks through an easily accessible and intuitive interface. This paper describes Genephony, a powerful online tool designed to assist the non-technical user in creating and manipulating large datasets of genomic information.sets of biological objects. Sets can be created by providing identifiers or query terms, or can be derived from other sets through appropriate transformations. The system automatically keeps track of the relationships among sets, and allows the user to freely navigate through them via a simple, consistent and intuitive user interface. Genephony is designed to be highly interoperable with other online tools: it accepts a wide variety of common formats in input, it provides extensive data export capabilities, and it features a SOAP server interface [Genephony is a Web-based application whose main purpose is to allow the user to easily build nterface  that allGenephony is able to handle a wide variety of object types, including genomic entities , classifications and taxonomies , experimental identifiers (probesets for common gene expression and genotyping microarrays), computational predictions (e.g. transcription factor binding sites), and high-level genetic and phenotypic data .The system relies on a local, integrated database of genomic information that includes information about most of the object types mentioned above and, when practical, on real-time access to online resources. It is important to note that Genephony does not try to reproduce exactly the entire contents of all the source databases it uses: doing so would be extremely impractical and ultimately not very useful. Genephony's local knowledge base, instead, represents a selection of the most commonly used object types and data elements, a selection that reflects the needs and requirements of an "average" genomic study. Since the system is based on a modular and general architecture, the default knowledge base described here can easily be replaced with alternative ones that are focused on alternative domains, by defining new object types and new relationships among them.The choice of maintaining a local database implies an effort to ensure its contents are up to date, through scripts that periodically check the source databases for new data releases. On the other hand, the alternative solution of retrieving the data from the source databases in real time is not practical for a variety of reasons: to start, most online resources enforce a limit on the number and frequency of queries that they accept from a client, making it impossible to work on large volumes of data; not all resources provide interface to access their data in an efficient and machine-friendly way; and finally, accessing very large datasets over the Internet is usually too slow for practical uses.slots, each of which contains a single element of information. For example, SNPs (Single Nucleotide Polymorphisms) may be represented by an object containing slots for the SNP identifier (NCBI "rs" number), its genomic location (chromosome and position), its alleles, and its validation status. Each object possesses a unique identifier. Usually this will be the "natural" identifier of the entity being described, when available ; otherwise one will be internally generated by the system.Biological objects are internally represented as data structures composed of several set is a collection of objects of the same type. Sets are created by the user by entering query terms, by uploading files, or by performing operations on existing sets. There is no a priori limit on the number of objects that a set can contain, or on the number of sets that can be created, and the system is optimized to handle sets containing a very large number of objects. Sets can then be browsed, filtered, annotated and exported in a variety of ways. The next section provides detailed information on all the dataset operations available in Genephony.A To start working with Genephony, the user creates a session, giving it a unique identifier. No password is currently required, although one may be optionally used to protect data privacy. Once a session has been established, the user can populate it by creating new sets in one of the following ways:autodetect procedures that examine the supplied identifiers and determine their possible meanings (Table 1) Manually entering one or more identifiers. The system is able to automatically recognize a large number of common identifiers; this is accomplished by a set of 2) By uploading a file containing identifiers. The system accepts delimited text files and Excel spreadsheets, and handles both ZIP and gzip compression. The user needs only specify the column that contains the identifiers of interest; the identifiers are then parsed and translated into objects using the same procedure described in 1).Derive operation, a new set is generated using the data from a single existing one. For example, given a set of genomic regions, it is possible to generate the set of all SNPs belonging to them. In a Combine operation, the data contained in two existing sets is used to generate a new one.3) By deriving them from an existing set, or combining two existing sets. In a When a set is created, it is initially populated only with the identifiers of the objects it should contain; the object themselves are actually created the first time they are accessed, for efficiency . The data used to create the object are usually retrieved from the local database through highly optimized queries, although in general they could come from other sources as well .many-to-many relationships, and will allow the user to determine how an individual object was produced or how many derived objects were produced by an object in the starting set. These data structures are also used by the Annotate command as described below.All generated datasets are permanently stored in the current session. The system keeps track of how each set was generated, and of which other sets were generated by it. It is therefore always possible to reconstruct the path through which any individual dataset was produced, and the user has the option of navigating back to previously-generated sets at any time, in order to examine the data they contain or to generate new sets by following alternative Derive or Combine paths. Moreover, the system records the relationships among individual objects in datasets that are derived from each other. For example, when a set of SNPs is derived from a set of genomic regions as described above, the system will create a table associating each SNP with the genomic region (or regions) it belongs to, and each region with the SNPs it contains. In general, these will be Workspace, which lists all the sets in the current session. The user can "focus" any one of the sets in the session by clicking on its name in the Workspace panel; the currently focused set is shown in bold face. The top right panel displays information about the currently focused set: its name, a description, the number and type of object it contains, how it was generated, and how many other sets it is a parent of (the set's name and description are editable and can be changed by the user at any time). This panel also contains the buttons through which the user can perform all available operations. Finally, the bottom panel is used to display information about the contents of a set, or about its relationships with other sets; it is also used to get input from the user when running certain commands. For example, when the user clicks on the button for the Derive command, the bottom panel will display the list of derive operations available for the current set.Genephony's main interface window, shown in Figure New command opens the initial page and allows for the creation of a new set. The Info command displays additional information about the current set that would not fit in the top panel, such as the complete list of sets that were derived from it. The Derive and Combine commands are used to generate new datasets from the current one as described above.The Browse command displays the contents of the current set as a table in which each row represents an object in the set and each column contains one of the fields of the objects. Several commands are available while browsing: the user may hide or show any column in the table, and sort the set contents by the value of any field by clicking on the header of the corresponding column. Clicking on a table row brings up a page containing detailed information about the object it contains, including the set of "parent" objects that generated it. For instance, considering again the example described above, the page for an individual SNP object would contain the complete list of fields with their values, and the list of genomic regions that contain it . The user may then choose to restrict the display to a manually-selected subset of the rows; the remaining objects in the set are effectively filtered out, as described below.The Filter command can be used to hide the objects in a set that do not meet a specified condition. Once a dataset is filtered, all subsequent operations only apply to its visible elements. For example, a set of regions may be filtered to display only the ones belonging to a specified chromosome. If the user then applies the Derive command to derive the set of SNPs they contain, the operation will be applied only to the visible regions, and the resulting set of SNPs will contain only the ones belonging to the regions on the specified chromosome. Filters can be inverted, to select only objects that do not meet the filter condition, and multiple different filters can be applied at the same time, in order to select the objects that meet all specified conditions at the same time . It is important to note that this feature works across any number of Derive steps, in both directions: a set can be used to annotate its parent, its parent's parent, its child, its child's child and so on. Combined with filtering and with the ability to select individual fields of the objects, the Annotate feature can be used to create richly annotated datasets in a few simple steps.A very powerful feature offered by Genephony is the Export command allows the user to retrieve the contents of a dataset in a variety of different formats, including tab- or comma-delimited text files, Excel spreadsheets, and HTML tables. The files can be directly downloaded or received by email, with optional ZIP or gzip compression, or submitted to the Galaxy online tool [The ine tool . DatasetIn order to facilitate the exchange of data with other applications, Genephony is designed to accept and to generate datasets encoded in the most common formats, including comma- and tab-delimited text files, Excel spreadsheets, and HTML tables. In addition, Genephony provides a SOAP server interface allowing external programs to use its capabilities independently of the Web interface. The SOAP interface is self-documenting and is fully described in the system's Help pages. Its WSDL definition is also provided to enable the automatic generation of client programs.which of these SNPs are located in genes that are known to be related to the phenotype. In the example described here, we used a dataset of 2,761 SNPs, and we chose Insulin-Dependent Diabetes Mellitus (IDDM) as the disease under study .In this section we present a detailed example of how Genephony can be used to perform a complex data integration and annotation task. Let us imagine we have performed a genotyping experiment on a large set of SNPs, and that statistical analysis of the results has identified a subset of SNPs that are significantly associated with the presence of a phenotype of interest. In order to better characterize our results, we would now like to determine Our strategy will be to query a database of genotype-phenotype correlations, such as OMIM or GAD, for genes contained in regions known to be associated with the disease, to extract the SNPs contained in their transcripts, and to intersect this set of SNPs with the original set. To start, we create a new session and upload the input file using the "Create Dataset" form, specifying that the identifiers are in column 1. The system automatically parses the "rs" identifiers contained in the file and creates an initial set of 2,761 SNPs, that can be displayed using the Browse command . To simplify the display, we use the "Choose columns" menu to select just the GeneID, Gene symbol, and Gene name columns. Then, using the "Annotate" command we annotate these genes with the set of SNPs they were derived from. To view the resulting annotated dataset, we select the set of 12 SNPs from the workspace window, browse it, and select the set of genes from the "Annotations" menu . Very complex sequences of data manipulations can thus be performed in just a few steps, and no knowledge of the structure of the underlying database is required.Compared to similar systems such as Galaxy , DAVID , or BioMGenephony does not currently offer graphical output capabilities, since its main focus is on knowledge and information management, but it provides flexible ways of exporting the contents of its datasets in standard formats for use in external visualization and data manipulation tools such as the UCSC Genome Browser and Galaxy. Although it is not an analysis tool, its rich knowledge base makes it suitable for scenarios ranging from basic genomic data annotation to translational research applications aimed at establishing links between the genomic level and medically relevant phenotypes.The manipulation and interpretation of very large datasets represents a significant bottleneck for researchers who are not experts in database technology and programming. By providing them with effective tools to perform these increasingly common tasks, Genephony has the potential to accelerate the process of turning experimental data into verifiable hypotheses and biomedically relevant findings. Genephony could also be used as a platform for the dissemination of domain-specific knowledge, since its modular nature facilitates the creation of customized knowledge bases. It can therefore be helpful in making biomedical information available and accessible outside the boundaries of research community, resulting in an added benefit for the general public.Project name: Genephony• Project home page: • Access policy: the system is freely available to anybody. Users are asked to enter a session identifier to start using the system. Using an e-mail address as the identifier is preferable, but is not required.• Operating system(s): Platform independent• Programming language: Common Lisp, Java• Any restrictions to use by non-academics: freely available.• AR designed the Genephony system and was responsible for its implementation. AN contributed to the development of the SOAP interface and other interoperability features. Both authors read and approved the final manuscript.


Network Tools and Applications in Biology (NETTAB)  is a serBiological data integration issues were already discussed in previous editions of this series of workshops, including topics such as “CORBA and XML: towards a bioinformatics integrated network environment” NETTAB 2001) [001 [2], The Seventh NETTAB workshop was held at the Computer Science Department of the University of Pisa, on June 12-15, 2007, having “A Semantic Web for Bioinformatics: Goals, Tools, Systems, Applications” as focus theme. Adjunct themes were “Algorithms in bioinformatics” and “Formal Methods for Systems Biology”.BMC Bioinformatics Supplement includes the best papers and posters – representing all the themes - from works presented at the workshop.This The quantity of biological information is increasing at an impressive rate. An integrated access to this huge amount of information requires complex search and retrieval software and automation of analysis processes. Automation of integration procedures mainly concerns how to link data, how to select and extract information and how to pipe retrieval and analysis steps. This automated approach to data analysis requires the adoption of new technologies and tools in the bioinformatics domain.Some reference points have already been assessed, or are emerging, towards this goal: the adoption of XML schemas for information models specification, the definition of XML based languages for data representation and exchange, the implementation of Web Services for automated access to analysis tools and data, the creation of computerised pipelines and workflows for the definition and the execution of basic and complex analysis . WorkfloHowever, while these first steps towards data integration and processes automation have been made, little has been made for supporting semantic integration. What is needed are shared definitions of knowledge domains, i.e. ontologies, association of biological concepts to existing data, metadata information describing information sources and search tools able to make the best use of this additional information. Databases and tools should be made available on the Web, or better on the Semantic Web , that isThe definition of ontologies and their application to software and database tools may be seen as a first, needed attempt to organize the information, overcoming heterogeneity of data structures. But the problem of associating the information sources and the huge amount of data with concepts defined in these ontologies is a big one. The addition of semantic contents in current databases would give an essential contribution to the best integration of distributed biological information.The development of metadata for biological information, on the basis of Semantic Web standards, and its definition for all information sources can also be seen as a promising approach for a semantic based integration of biological information.The Opening Lecture, entitled “Pathway Commons: A public library of biological pathways on the Semantic Web”, was given by Gary Bader, University of Toronto. This lecture was selected with the idea of offering an overview of the problems faced by bioinformaticians while developing new data integration tools, in a not yet semantic era, and the perspectives of adopting Semantic Web technologies.Sessions devoted to the focus theme aimed at getting together biologists, bioinformaticians, computer scientists and linguists trying to understand usefulness of a Semantic Web for bioinformatics, its possible goals, most promising standards, technologies and tools, with the final objective of devising which bioinformatics research problems can be solved by the Semantic Web and which are the short, medium and long term perspectives in applying Semantic Web technologies to bioinformatics.In the first session, the aims and perspectives for the development of a Semantic Web for bioinformatics were discussed. It included an invited lecture by Eric Neumann, founder and co-chair of the W3C Semantic Web Healthcare and Life Science Interest Group (HCLSIG) . The W3CThe second session discussed Semantic Web technologies and tools. Two invited lectures were respectively given by Antoine Isaac, University of Amsterdam, and Olivier Bodenreider, National Library of Medicine. Isaac presented ongoing activities within the W3C Semantic Web Deployment (SWD) Working group , that isThe third session focused on applications. A joint invited lecture was given by Michael Schroeder, Biotec TU Dresden, Albert Burger, Heriot-Watt University, and Robert Stevens, University of Manchester, who introduced Sealife, a Semantic Grid Browser for the Life Sciences .Both sessions devoted to adjunct themes, selected by local organizers, had renowned invited speakers from the University of Pisa. The session on “Algorithms in bioinformatics” was opened by Fabrizio Luccio, who presented, in a homonymous talk, a global historic survey of the relationships between Information Theory and Biology. The session entitled “Formal Methods in Systems Biology” hosted an invited talk by Pierpaolo Degano, who discussed problems and perspectives of the application of formal languages to the description of biological systems.The NETTAB 2007 web site includes almost all presentations that were given at the workshop .Besides outlining the promising features of the Semantic Web in bioinformatics, the workshop also intended to support as much discussion as possible through open discussions and, especially, a final panel discussion on “Goals and perspectives of a Semantic Web for Bioinformatics” that was participated by invited speakers and chairs.From the discussion, it emerged that the promises of the Semantic Web can really be of a paramount importance for bioinformatics, but undoubtedly there is still a lot to do. The current phase can still be considered as a pioneer one, in which scientists are getting familiar and becoming aware of the possibilities and possible scenarios that are offered by this new concept. Furthermore, related technologies still need to be improved and adapted or tuned. Recommendations should be provided by the World-Wide Web Consortium (W3C) and successful examples could be spurred by the HCLSIG.Controlled vocabularies and ontological frameworks already acquired a wide diffusion in biomedical sciences. Although, during the discussion, it was reinforced that the HCLSIG has not the scope to develop them, there are other groups and institutions that can support development, and effectively are developing, biomedical ontologies, like the National Center for Biomedical Ontology (NCBO) . Now, onActually, scientists should be urged to expose their data and should be instructed on how to present these to the world, and on how to identify and represent them. Data sharing in the community was restated to be a major necessity. In a first approximation, data can also be kept as they are, while semantic layers and links can be built upon them by the community itself. Semantics and the Semantic Web have been explicitly recognized as “complicated”, while end users would like to have friendly tools and to find everything “on their desktop”.Other important concerns about the data, coming also from the audience, were trust and provenance and, in general, transparency.An interesting observation was that now semantics is actually embedded in the software that manages and analyses the data. In order to facilitate the advent of the Semantic Web, this knowledge should be removed from the code and put just in the data contents, therefore shifting from “intelligence in the software” to “intelligence in the data”.The take-home message from the workshop was that “we are not there yet , but stiBMC Bioinformatics. These papers are extended and improved versions of the best oral presentations and posters of the NETTAB 2007 workshop. In the following paragraphs, we briefly review them. The complete proceedings of the workshop are also available [After a selective review process, performed by the Program Committee and some external reviewers, twelve articles have been accepted for publication in this Supplement to vailable .The session on Semantic Web tools and applications is represented in this Supplement by five contributions.et al [Ontologies are one of the pillars on which the Semantic Web vision is built. The paper by Alexopoulou et al  concernset al[Also based on ontologies is the paper by Coulet et al. Authorset al [The tissue microarray database described by Viti et al  offers iet al [Another pillar of the Semantic Web are mediators, which make possible to individuate suitable resources. Navas-Delgado et al  present et al , aiming The paper by Splendiani  aims at et al [et al [Formal methods, which are widely used in theoretical computer science to formally define and analyze complex software systems, have found a natural application in modelling and simulation of biological systems and processes. Even though a specific formal language able to describe all the properties of biological systems has not yet been designed, some properties of formal languages can be very useful to describe and manage some interesting aspects, like, e.g., managing stochastic behaviours and asking simulated systems some “logic questions”. Selected papers addresses one of these two aspects. Bracciali et al  introducl [et al  apply teet al [A bridge between Systems Biology and the session on Algorithms in Bioinformatics is represented by the paper from Francesconi et al . Authorset al [The session on Algorithms for Bioinformatics was represented by other two contributions. The article by Ferro et al  introducet al [In the paper from Brunetti et al , authorsThe session on the general theme (Network Tools and Applications in Biology) is here represented by two contributions. These were selected among five presentations that were submitted under the common subtitle “From components to processes” and were aimed at presenting results of the German HOBIT (Helmhotz Open Bioinformatics Technology)  project.et al [The paper from Margaria et al  introducet al [An application of the Bio-jETI platform for service modeling and execution is presented in the paper by Lamprecht et al . AuthorsThe authors declare that they have no competing interests.


The Research Collaboratory for Structural Bioinformatics Protein Data Bank is part of an international consortium that works not only to make the data describing the 3-D shape of biological macromolecules available worldwide, but also to increase their usefulness in science, medicine, and education.  Knowing the shape of a protein can tell you its story—what it does and what molecules it binds with. Accordingly, understanding the structure of a biomacromolecule can help to create drugs designed to interact with a protein, either to block its access to other molecules or to prevent it from functioning. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is part of an international consortium that works to make the data describing the 3-D shape of biological macromolecules available worldwide. Additionally, the RCSB PDB creates resources and participates in outreach efforts to ensure that these data are used by scientists, students, and teachers, both inside and outside the structural biology community.A new era of scientific research began when the 3-D shape of the oxygen-storing protein myoglobin was revealed ,2. Usingx-y-z coordinates describing the location of every atom viewed in the molecule. In the 1970s, structural biologists thought that they could benefit by sharing this information with one another, and the PDB archive, featuring seven structures, was born [http://www.wwpdb.org/) [The types of experiments used to solve these structures produce a large amount of information, from the conditions and methods used to determine the molecule's structure to thousands of was born ,4. Sincedb.org/) .http://www.pdb.org/ [The online archive now contains more than 50,000 structures, which exhibit an amazing diversity of size, complexity, and function. Just a small and random sample of the biological macromolecules found in the PDB includes RNA, insulin, anthrax, influenza proteins, ribosomes, and prions. Rather than simply offering a data bank developed and used only by structural biologists, each wwPDB site offers resources and tools to access, view, and understand these data. The RCSB PDB maintains the master copy of the FTP archive for the wwPDB, and provides a comprehensive database and Web site at pdb.org/ . These vdeposit@deposit.pdb.org), Web site, tutorials, and tools. In developing our resources, it is very important to consider the types of information depositors can provide to the archive. Our services are customized by experimental type to facilitate the deposition process and to make sure that we are capturing the most data possible, as accurately as possible. To keep current, we collaborate with experts in various structural biology disciplines at meetings, in the development of data dictionaries, and on a variety of projects. As new methods in structure determination are being developed, we will meet and have workshops with the scientists who are generating these data, along with representatives of our data users to develop data content standards.While there isn't a “typical” user of the RCSB PDB resource, there are broad, consistent groups of people who are looking for different types of tools and resources. For example, our depositor community—the scientists who provide the data—use RCSB PDB tools and resources that aren't known to a significant portion of people who come to our Web site. Depositors are provided with their own help desk (info@rcsb.org) answers queries from our non-depositors. Input from this help desk, in addition to other ways of gathering feedback, is used to develop tools that help the wide variety of people studying the structures of biological macromolecules and their relationships to sequence, function, and disease. A goal of the project is to develop resources that meet the needs of all users—from the structural genomist to the systems biologist and beyond.Another category, our “users,” includes the many people who use the RCSB PDB to access information, generate reports, and visualize structures. They could be searching for a structure that was recently published in a journal; looking for a group of structures that all bind to the same drug; or wanting to make an image containing the active site of a particular structure. Knowing that our users come to the site with varying degrees of expertise and knowledge, we try to provide a lot of information to guide any query up front; a help system is available from every page of the resource, along with printed and online Flash tutorials. An additional help desk  are pretty: Using the many amazing visualization programs available, we can see the beauty inherent in these structures. We exploit this fact when developing all materials, but specifically in our traveling Art of Science exhibit. By presenting large-scale images of proteins in the context of “art,” we have been able to informally educate students and teachers about the PDB resource.Molecule of the Month series  and run in New Jersey by the RCSB PDB. At this event, students build a model that highlights what makes the structure interesting using the Molecule of the Month entry, the PDB file, and visualization tools.Beautiful representations of structures are also a feature of the h series . WrittenProteins are related to health and disease: Though nutrition is the first thing that comes to mind when many people hear the word “protein,” we can build upon students' existing familiarity and interest in health and disease by selecting proteins related to these topics to exemplify structure–function relationships. In working with middle school and high school students, we have focused on the shape of virus structures in presentations and lessons. When building 3-D models of viruses, we discuss colds and what happens at the molecular level when we get sick. Viruses are particularly interesting, because their icosahedral shape easily lends itself to discussions about geometry and symmetry. At the collegiate level, we have explored and mapped structures in the context of their location in the body, the biological processes they participate in, and related diseases and disorders.The RCSB PDB resource is used by scientists worldwide: When using the RCSB PDB, students are accessing the same Web site, database, and data used by researchers around the globe. Resources on the Web site, including the Molecule of the Month, visualization tools, and links to other authoritative scientific sites, provide interactive opportunities to increase scientific and computing literacy and understanding of how researchers view structural data. An early introduction to these tools and information can get students to start thinking like the scientists who use them.We are always trying to think of new ways to interest teachers and students. After speaking with teachers at workshops, conferences, and other events, we recognized that hands-on activities and lesson plans would be useful tools to start teaching about structure. The addition of these features has been received enthusiastically, and has led educators to suggest additional materials. We are able to spark student interest with temporary tattoos and pins of collagen and other structures that are given away at school events and conferences, and then go a step further by teaching key points about molecular structure, having students use the tools and resources available at the Web site and build hands-on 3-D models of what they can see online.With so many varied types of outreach, we internally emphasize tracking the reactions of all of our audiences. We've seen a growing sense of familiarity with our resources at the various professional society meetings, and are excited that many students are able to list proteins that they'd like to learn more about.info@rcsb.org.Based on feedback from our users, selected future plans include packaging our lesson plans for a larger distribution and creating materials to introduce the nonstructural biologist to PDB data. If you have comments about our resources, things that you would like to see at the RCSB PDB Web site, or questions about the 3-D structures of proteins and nucleic acids, please let us know at


NetPath, a novel community resource of curated human signaling pathways is presented and its utility demonstrated using immune signaling data. We have developed NetPath as a resource of curated human signaling pathways. As an initial step, NetPath provides detailed maps of a number of immune signaling pathways, which include approximately 1,600 reactions annotated from the literature and more than 2,800 instances of transcriptionally regulated genes - all linked to over 5,500 published articles. We anticipate NetPath to become a consolidated resource for human signaling pathways that should enable systems biology approaches. Complex biological processes such as proliferation, migration and apoptosis are generally regulated through responses of cells to stimuli in their environment. Signal transduction pathways often involve binding of extracellular ligands to receptors, which trigger a sequence of biochemical reactions inside the cell. Generally, proteins are the effector molecules, which function as part of larger protein complexes in signaling cascades. Cellular signaling events are generally studied systematically through individual experiments that are widely scattered in the biomedical literature. Assembling these individual experiments and putting them in the context of a signaling pathway is difficult, time-consuming and cannot be automated.The availability of detailed signal transduction pathways that can easily be understood by humans as well as be processed by computers is of great value to biologists trying to understand the working of cells, tissues and organ systems . A systeNetPath  is a resTo facilitate annotation of pathway data, we first developed a tool called 'PathBuilder' . PathBuiProtein-protein interactions could be binary when two proteins directly interact with each other - 'direct interaction' - or when the proteins are present in a complex of proteins - 'complex interaction'. Both types of protein interactions are comprehensively collected from the literature. We provide PubMed identifiers, experiment type and host organism in which the interaction has been detected.Enzyme-regulated reactions such as post-translational modifications  are annotated as catalysis interactions. For each catalysis or modification event, the upstream enzyme, downstream targets and the site of the modification for a protein are annotated, if available. Proteins that translocate from one compartment  to another  are represented as transport events. For all reactions, a brief comment describing the reaction is also provided.The homepage of any given pathway contains a brief description of the pathway, a summary of the reaction statistics and a list of the molecules involved in the pathway. Reactions in a pathway are provided under three distinct categories, including physical interactions, enzyme catalysis and transport. Furthermore, the pathway data are also provided in PSI-MI, BioPAX and SBML formats, which can also be visualized through other external network visualization software, such as Cytoscape .In addition to the above pathway annotations, information on genes that are transcriptionally regulated is provided in NetPath. This is important because addition of most extracellular growth factors or ligands leads to an alteration in the transcriptome of the cell. Often, some of the transcriptionally regulated genes are used as 'reporters' in biological experiments where the pathway is being studied. We have cataloged a number of genes that are up- or down-regulated by the particular ligand involved in each pathway. These up/down-regulated genes can be considered as 'signatures' for that particular pathway. We have incorporated both microarray and non-microarray , and so on) experiments for gene expression. In each case, the type of experiment  used to obtain the data is indicated. Additionally, we have also annotated the transcription factors that are responsible for transcriptional regulation of the downstream genes where such information is available. Given the large number of transcriptionally regulated genes for each pathway, we have also developed a query system that permits users to search such genes using gene symbol or accession numbers. This feature will be valuable for shortlisting genes that are common to several pathways or specific to any given pathway.At present the 10 annotated immune signaling pathways comprise 703 proteins and 1,572 reactions. The reactions can be grouped into 740 molecular association events, 727 enzyme catalysis events and 105 translocation events. Our pathways provide a list of 2,004 and 889 genes that are up- or down-regulated, respectively, at the level of mRNA expression. Including 10 cancer signaling pathways that are also available through Cancer Cell Map , NetPathAlthough over 310 resources  provide One of the best studied immune signaling pathways is the interleukin (IL)-2 signaling pathway . IL-2 isIL2RG gene result in X-linked severe combined immunodeficiency disease  (as described in ) was an high) and low LPS responders (lpslow), based on the concentration of cytokines produced in response to LPS. Gene expression analysis was done using the Affymetrix U95AV2 human oligonucleotide arrays. Impact factor analysis was carried out using this dataset on all ten immune signaling pathways. Impact factor scores for IL-1 and IL-6 NetPath pathways in the lpshigh group have high values whereas impact factor scores for lpslow do not show any significant perturbation of NetPath pathways. The scores are consistent with experimental results showing upregulation of IL-1 and IL-6 ligands in the lpshigh group. The impact factor gives the insight that not only are the ligands upregulated, but the pathway also seems to be highly affected. It should be noted that impact factor is not the only method to measure the biological impact of perturbation of pathways and other methods will continue to be developed and could be applied to such pathway data.Another dataset selected [GEO:GDS1407] (described in ) was a pIn addition to keeping these pathways updated on a regular basis, we will also add additional pathways to NetPath. We also hope to involve the biomedical community by allowing researchers to provide feedback as well as to volunteer to become 'pathway authorities' on specific pathways, similar to the successful contribution model of the BioCarta resource . In thisWe have developed a resource for integration of human cellular signaling events. These pathway-specific protein-protein interaction data can be used to generate larger physical networks of protein-protein interactions that, when coupled with data on genetic interactions, could help in defining novel functional relationships among proteins. In addition, genetic interactions can functionally link proteins that belong to unconnected physical networks. These pathways could also be used to interrogate gene expression signatures in cancers and other human diseases to better understand the mechanisms or to obtain profiles for diagnostic or therapeutic purposes. There is a large amount of known information about different cellular signaling pathways controlling a variety of cellular functions, which is difficult to collect by one group. We support the vision of many data providers collecting data of interest and making them freely available in standard formats as a scalable way to represent all known pathway information in databases for comprehensive analysis. Overall, we hope to engage the biomedical community in keeping the NetPath pathway resource up to date and as error-free as possible.The initial annotation process of any signaling pathway involves gathering and reading of review articles to achieve a brief overview of the pathway. This process is followed by listing all the molecules that arereported to be involved in the pathway under annotation. Information regarding potential pathway authorities are also gathered at this initial stage. Pathway experts are involved in initial screening of the molecules listed to check for any obvious omissions. In the second phase, annotators manually perform extensive literature searches using search keys, which include all the alternative names of the molecules involved, the name of the pathway, the names of reactions, and so on. In addition, the iHOP  resourceBioPAX: Biological Pathways Exchange; GEO: Gene Expression Omnibus; HMGB1: high mobility group box protein 1; HPRD: Human Protein Reference Database; IL: interleukin; KEGG: Kyoto Encyclopedia of Genes and Genomes; LPS: lipopolysaccharide; PSI-MI: Proteomics Standards Initiative - Molecular Interactions; SBML: Systems Biology Markup Language.SM1, RR, SK, GSSK, AKV, DT, DJN, SM2, CP, SKG, SGT, SM3, HP, YS, RG, HKCJ, JZ, RS1, VN, SB, RS2, YLR, BAR, TSKP and JL collected the data. JCDH, SD1, JR, SC, OO, TH, MK, SS, WJL and AP serve as pathway authorities. KK, SM1 and AP wrote the manuscript. KK and SM2 developed the software. KK, AKV, DJN, SKG, PK and SD carried out the impact factor analysis. KK, GDB, CS and AP participated in the study design. All authors read and approved the final manuscript.


In an effort to better understand the molecular networks that underpin macrophage activation we have been assembling a map of relevant pathways. Manual curation of the published literature was carried out in order to define the components of these pathways and the interactions between them. This information has been assembled into a large integrated directional network and represented graphically using the modified Edinburgh Pathway Notation (mEPN) scheme.The diagram includes detailed views of the toll-like receptor (TLR) pathways, other pathogen recognition systems, NF-kappa-B, apoptosis, interferon signalling, MAP-kinase cascades, MHC antigen presentation and proteasome assembly, as well as selected views of the transcriptional networks they regulate. The integrated pathway includes a total of 496 unique proteins, the complexes formed between them and the processes in which they are involved. This produces a network of 2,170 nodes connected by 2,553 edges.The pathway diagram is a navigable visual aid for displaying a consensus view of the pathway information available for these systems. It is also a valuable resource for computational modelling and aid in the interpretation of functional genomics data. We envisage that this work will be of value to those interested in macrophage biology and also contribute to the ongoing Systems Biology community effort to develop a standard notation scheme for the graphical representation of biological pathways. Macrophages and other antigen presenting cells (APCs) are present in high numbers in all tissues. They act as a first line of defence against pathogenic organisms playing a crucial role in co-coordinating the innate immune response to infection. Furthermore, it is being increasingly recognized that they not only play a central role in tissue homeostasis and development, but also in the aetiology and maintenance of pathological processes that underpin all infectious, inflammatory and malignant disease ,2. WhilsOur primary interest has been to further our understanding of the macrophage signalling and effector pathways that orchestrate this cell's pivotal role in infectious and inflammatory disease. As with many systems, certain macrophage pathways are very well characterized whereas little is known about many others. Even where pathway domain knowledge does exist however, it is generally fragmentary and subjective. Therefore we set out to generate an integrated model of macrophage pathways of interest to us and in doing so we have faced one of the central challenges in pathway biology: How does one construct clear concise pathway diagrams of the known interactions between cellular components that can be understood by and useful to a biologist?ad hoc basis. If we are to escape this gene-centric view of biological systems, we must develop better ways to order and display our knowledge of protein interactions and the systems they form. Formalized diagrams act as a visual representation of the interactions between cellular components and provide a valuable resource for modelling network structure and the dependencies between components [Decades of research on the functional activity of individual proteins and genes has revealed many insights into how these cellular components interact with each other to form the metabolic, signalling and effector effecter pathways that underpin life. Much of this work however remains locked inside the literature where specific insights into pathway function are subject to the semantic irregularities that come with their description by different authors. As a result, the details of a given pathway have traditionally been known only to a few experts in the field whose research is often focused on a single protein and its immediate interaction partners. Pathways are understood more generally by their description in reviews and diagrams produced on an mponents . In addimponents -10, for mponents -15 and fIn recognition of the importance of pathways, many efforts have been made to collate pathway knowledge, together with information derived from large-scale interaction studies and literature mining, into public and commercial databases -25. Theshttp://www.mepn-pathway.org[Over the past four years we have been constructing process diagrams  of pathwthway.org. The macThe work described here follows in the footsteps of earlier efforts to construct process notation diagrams of macrophage pathways ,32. In thttp://www.mepn-pathway.org and in Freeman et al., 2010 [http://www.macrophages.com.The pathway diagram presented here Figure  is a conl., 2010  and thisThe pathway diagram Figure  incorporMacrophages are equipped with a complex array of pattern recognition receptors (PRRs) that bind a varied assortment of pathogen-associated ligands. Perhaps the best studied of these are the membrane-associated toll-like receptors present on the cell's surface (TLRs 1/2/4/5/6/10) and lining their endosomal compartments (TLRs 3/7/8/9) ,35. The + efflux, DNA, ATP or membrane damage, often collectively referred to as danger-associated molecular patterns (DAMPs). Once activated by DAMPs the receptors form oligomers with inflammatory caspases (CASP1/5) and in doing so activate the cleavage of the caspases [Also represented here are 9 non-TLR cytoplasmic PRRs, including the NOD (nucleotide-binding oligomerization domain)-Like Receptors and RNA helicases, whose role it is to detect endogenous stress signals and intracellular pathogens. NOD-like receptors (NLRs) can be broadly divided into various classes, the NODS, NALPS and other types of NLRs, based on their protein domain structures. Bacterial associated PAMPs (e.g. flagellin and various classes of peptidoglycans) are recognised by NOD1, NOD2 and NLRC4 (IPAF1) which activate the NF-κB and MAPKinase pathways. NOD1 can also lead to the cleavage of IL1B into its active form. The NALPS  detect a range of stress signals such as Kcaspases . The actcaspases ,51. Finacaspases  and acticaspases -56.IFNB is perhaps one of the best studied genes in the whole genome in terms of its transcriptional regulation [The diagram also describes a number of the main cytokine signalling systems active in macrophages. These include the interferon (type 1 - IFNA/IFNB and type 2 IFNG), interleukin 1B (IL1B), TNF, TNFSF10 (TRAIL), TNFSF13B, FASLG and CD40LG signalling pathways. In each case these have been depicted starting from their interaction with their receptor complexes Figure  through gulation ,57-59. IA potential output of the innate immune response is to culminate in host cell suicide (apoptosis) thereby potentially limiting further reproduction of pathogenic organisms such as viruses. Two major routes of apoptosis execution have been identified, termed the intrinsic and extrinsic pathways. The intrinsic pathway is activated as a result of stress signals detected within the cell, for example, penetration of a viral pathogen into the cell or UV light induced DNA damage. Extrinsic apoptosis on the other hand is triggered by extracellular death-signalling ligands , TNF which are also members of the cytokine activation pathways) binding to the cell membrane receptors. Both intrinsic and extrinsic pathways activate a number of the caspase family of cysteine proteases. The initial caspases to be activated are categorized as initiators, (CASP2/4/6/8/9/10) and are capable of cleaving downstream executioner caspases, specifically CASP3 and CASP7. Caspases 3 and 7 initiate the series of events that directly lead to the morphological changes in a cell associated with apoptosis by the cleavage or inactivation of an array of molecules including, structural proteins, DNA repair proteins, and anti-apoptotic proteins.The NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) family of transcription factors are pivotal in the regulation of a wide variety of biological processes -63. ThisAs mentioned above, phosphorylation is a key element in the activation process of many of the NF-κB complexes. In the pathway, dimers of the core proteins may bind to NFKBIA, NFKBIB or NFKBIE, a group of I-kappa-B or NF-κB inhibitor proteins. When bound to their inhibitor, the NF-κB complexes are restricted to the cytoplasm. Upon stimulation the I-kappa-B proteins are phosphorylated, leading to their ubiquitination and eventual degradation, and the release of the active NF-κB complex. Dissociation of the inhibitors exposes the nuclear localization domain on the NF-κB complex causing it to translocate to the nucleus where it can modulate transcriptional activity of target genes ,67. OtheThe pathway diagram presented here also includes preliminary views of some of the kinase signalling pathways known to be associated with macrophage activation. These include the MAP2K1/2 (MEK1/2)-MAPK3/1 (ERK1/2) cascade known to be activated by TLR signalling and the MAPK8 (JNK)-JUN (AP1) and MAPK14 (p38) Figure  cascadesAntigen presentation is not exclusive to macrophage biology but equally central to it. We have attempted to depict the MHC class I and II pathways from either the degradation of cellular proteins or the phagocytosis of pathogens, to the presentation of cellular/pathogen peptide antigens to CD8 cytotoxic T-cells or CD4 T-helper cells, respectively. In order to achieve this however, we found it necessary to construct diagrams of the ubiquitination pathway (due to it role in tagging cellular and pathogen proteins with ubiquitin) and proteasome formation (due to the role of the proteasome in the digestion and/or processing of ubiquitinated proteins). These latter two systems are also crucial to many aspects of cell biology, being responsible for the activation and/or degradation of many cellular and pathogen proteins alike. Depiction of these systems proved to be challenging. In the first instance, whilst our view of phagocytosis is greatly simplified, we had to find ways to show the passage of key molecules in the antigen presentation pathways from their assembly in the endoplasmic reticulum Figure  to theirExpress3D, a network analysis tool co-developed by our group [http://www.mepn-pathway.org). With the ever increasing amounts of interaction data it becomes more evident that an extra dimension will be valuable for the visualisation of large pathways and eventually an in silico cell. The BioLayout Express3D interface also supports the follow through of connectivity in pathways such that the parent or children nodes of a given selection can be highlighted and selected nodes hidden or isolated.The pathway model presented here is primarily designed to function as a computation resource. Its size as well as the fact that additional information is available through mouse-over or hyper-linked from it, means that it is best viewed on a computer. The diagram has been constructed using the freely available program yEd graph editor , a general purpose tool designed for the depiction of network-based diagrams. The standard file format used with this program is .graphml which is also supported by other network/pathway editors -76. In tur group . This prur group . A notatur group  , although clearly the modelling of other systems and ideas from others may present a case for further modifications.The pathways described here are of central importance to understanding macrophage biology and therefore innate immunity, and the diagram provides a consensus view of these systems. This is not to say that the model is either viewed as complete or necessarily even correct, but only as a working model. As such it has been designed with the idea that it will need to be modified and expanded based on new publications, experimental observations or deeper insight into specific systems. All of the pathways depicted are reasonably well characterized and as such there is a relative abundance of information on them from a wide variety of sources. What we lacked prior to this work was a way of collating our understanding of these pathways and integrating this view with the abundance of data generated on these cells by ourselves and others. Our aim has therefore been the creation of a pathway diagram that graphically reflects the current view of a pathway system in a visibly intuitive manner. In so doing we wished to create a resource for data integration, pathway modelling and hypothesis generation. In order to achieve these objectives we found it necessary to modify both the PDN  and EPN l., 2010  and httpExpress3D, a network analysis tool developed by us, provides a powerful approach to visualize and analyze 'omics data from a variety of sources [Express3D and the tool now supports the visualization of pathway diagrams as 3D or 2D networks [Pathway diagrams are a well established tool in our effort to interpret and explain results from functional genomics investigations. Overlay of results, usually from studies of the difference between one biological state and another, on top of pathway diagrams allows the investigator to visualize and link observations to defined pathways. BioLayout  sources . We havenetworks . A parsenetworks ,19,90 thIFNB expression via IRF3 [IFNB expression via IRF3 is also utilized by ZBP1, DDX58 (RIG-I) and IFIH1 (MDA5) cytosolic DNA/RNA receptors [IFNB undoubtedly contributes to the transcriptional signature observed when these systems are activated. Further complexity still is evident if we consider that these or indeed other immune responses are all clearly modulated by feedback control [Socs1/3, Prdm1, Nfkbiz. At the same time many feed-forward loops are being established by the up-regulation of transcriptional regulators which go on to activate genes associated with effector systems/pathway modules. Our integrated model of macrophage signalling pathways would therefore suggest extensive cross-talk between pathway modules and transcriptional networks with a high degree of feedback and feed-forward control taking place. It would also predict that many proteins/genes contribute to the networks functional activity and robustness. This is born out by the fact that pathogens are known target many different molecules in their effort to evade or subvert the immune response [As a note of interest on pathway topology, it has been suggested that many metabolic and signalling pathways, such as the TLR system, possess a 'bow-tie' or 'hourglass' structure ,80,91-93via IRF3 ,98. Henceceptors ,99-101.  control . Indeed,response ,104, andresponse -108. Furresponse . TherefoThe task of assembling this diagram has been time consuming and laborious involving 1,000's of hours of work. On the other hand, it summarizes the results of investigations that have taken many times that amount of time to perform and it is difficult to envisage how one could précis this body of work in any other meaningful way. To gain a systems level view of these pathways is to gain an insight into the molecular networks that regulate normal immune function and whose malfunction or manipulation underpins inflammatory and infectious disease. Greater understanding of the overall architecture of the immune system and its susceptibility to deregulation by pathogens and other disease causing agents, should ultimately lead to new strategies and targets for therapeutic intervention. Apart from summarizing decades of research, pathways depicted with formalized graphical notation schemes should aid the communication and comparison of biological data. During a thorough process of internal critiquing sections of the pathway were presented to others who were familiar with the notation scheme but not involved in constructing the pathway presented to them and asked to interpret the biology shown. This process ensured that the interactions of the pathway were not ambiguous in their depiction. Another major incentive for generating pathways with standard notations is to permit the conversion of graphical models into computationally tractable ones, suitable for analysis and simulation. For this purpose we have been exploring the use of signalling Petri nets (SPN)  for modeFor us the exercise of pathway construction has provided a resource for training, pathway modelling, literature/data interpretation, hypothesis generation and as such is now central to our ongoing investigations of macrophage biology. Importantly however, the pathway model presented here also serves as a worked example of how pathways might be represented in a logical, unambiguous and biologist-friendly fashion, whatever the system of interest. What we would like to see and believe is essential, is the support of the wider community in assembling and editing such diagrams. Such efforts are already underway -24 and aThe formalised depiction of biological pathways is increasingly recognised as a crucial requirement for the exchange of pathway data, modelling of their activity and systems level interpretation of biological data. However, there are just a handful of worked examples of large pathway diagrams constructed using a formalised graphical modelling language. The model of macrophage signalling and effector pathways presented here is to our knowledge the most comprehensive pathway of its kind published to date. As such it offers a worked example of how large pathway and has also proved to be a testing ground for the mEPN system . When prAlthough a time consuming and laborious exercise, the act of converting literature derived knowledge into a formalised computational models is essential if we wish to truly gain a systems level understanding of any cellular system. The macrophage model presented here summarizes the results of years of investigations and has allowed the thorough testing of the notation system used to depict it. The hope is that this work will provide a useful resource for others interested in the macrophage and the pathways depicted, and will help contribute to the development of standard graphical depiction in biology.Ongoing analysis of macrophage-related datasets and an interest in consolidating our knowledge of a number of signalling pathways directed our choice of pathways to be mapped  and later the pathways were integrated using the same software. In order to make these diagrams an information-rich vehicle for conveying details about pathway components and the reactions between them, PubMed IDs supporting the interactions are stored on appropriate edges or nodes within the .graphml version of the diagram, as are URL-links to Entrez gene for each protein or gene component in the pathway and notes from the curators. Due to the nature of pathway construction edges are often moved, deleted then redrawn to optimise the pathway layout and for this reason annotation may also be linked to an appropriate process and/or edge annotation node.All pathways have been constructed as directional networks. Interactions between pathway components are drawn using the principles laid down by the mEPN scheme  and diag., 2010,  and httpFollowing an initial development period, the focused diagrams went through extensive editing in attempt to unify their notation usage, stylistic qualities and overall appearance. All aesthetics of the pathways  were standardized between the diagrams. The original pathway diagram  was thenExpress3D. Mouse BMDM preparation, cell culture, and cytokine (Ifn-β) treatment was performed as described in the data sets GEO submission; accession no GSE20403. Expression data  was normalized using the RMA package within the Affymetrix Expression Console software and annotated. Transcripts which might be considered to be differentially expressed at each time point compared to 0 h were identified using the Empirical Bayes function within the Bioconductor [R statistical program using a 1.5 fold cut-off and a p-value of 0.05. According to this analysis 2,300 transcripts were considered to be differentially expressed. Data corresponding to these transcripts was then loaded into the network visualization tool BioLayout Express3D [Express3D using the "import-class-sets" functionality of the tool. Where genes are present in clusters of co-expressed transcripts and are also present in the pathway they can then be visualized and highlighted on the pathway.Analysis of a timecourse of interferon-beta (Ifn-β) treatment of mouse bone marrow derived macrophages (BMDMs) was used as an example of data integration onto the macrophage signalling pathway using BioLayout onductor  package xpress3D ,77 usingxpress3D  set at aExpress3D to enhance its capabilities to support these pathways for visualization and data integration; PG started manual curation of pathways and the original development of the EPN scheme and supported the current development; finally TCF oversaw and contributed to the pathway construction, orchestrated the development of the mEPN scheme, has directed the development of improved computational resources for these pathways and drafted the manuscript. All authors read and approved the final manuscript.SR was instrumental in the development of the mEPN scheme, oversaw the assembly of TLR signalling and integrated pathway diagrams, contributed to the development of the notation system, performed the Ifn-β timecourse study, oversaw the labelling of the RNA for microarray processing, and was a primary author of the manuscript; NM played a major role in the integration of the final pathway diagram and together with SM, helped redefine the TLR signalling pathway; PAL constructed the initial interferon pathways and oversaw the construction of the non-TLR pathogen detection by GE and MO; KR and SW contributed to development of the pathway notation and standardization of pathway data collection; YC and MC assembled the NF-κB pathway; AT (A Turnbull) assembled the antigen presentation, ubiquitin and proteasome pathways; AT (A Theocharidis) has been developing the program BioLayout Integrated Map of Macrophage Pathways_graphml-Version. This file can be opened, viewed and edited by users using the freely available graph-editor software yEd . yEd can be downloaded at http://www.yworks.com/en/downloads.html#yEd where full downloading instructions are described. PubMed IDs supporting the interactions of the pathway are stored on appropriate edges and nodes within this .graphml file. Once an edge is selected the PubMed ID may be viewed within the descriptions tab of the properties box for that edge or node. Individual proteins have a url link to their Entrez gene entry. Furthermore some (complicated) processes also have additional notes to describe the events and can be found under the description tab of properties and when hovering over the node.Click here for fileList of Pathway Interactions and Supporting Publications. Interactions included in the pathway map are listed. Official HGNC (human gene nomenclature committee) gene symbols are used to name the interacting components and Entrez gene IDs are also provided. Interacting components are named as individual genes/proteins and also as they appear on the pathway where they may be interacting as part of a protein complex or with a modification (e.g. Phosphorylation). Interactions are described as they appear on the pathway, sub-cellular location and the NCBI-PubMed ID for supporting literature is provided. There are also columns to incorporate additional information  where possible.Click here for fileIntegrated Map of Macrophage Pathways_pdf-Version. A high resolution .pdf file for viewing the integrated map of macrophage pathways.Click here for fileIntegrated Map of Macrophage Pathways_html-Version. A high resolution flash .html version of the integrated macrophage pathway for viewing in a web browser.Click here for fileIntegrated Map of Macrophage Pathways in 3D-or-2D View. The integrated macrophage pathway can be viewed in the network visualisation tool BioLayout Express3D, in both 2 and 3-dimentions and also using the original graph co-ordinates as the .graphml file. By choosing the appropriate class-type (found within class tab of graph properties box) the nodes can be coloured according to assigned classes. By default nodes are coloured and shaped according to "node-type"  using a 3D-notation scheme described at http://www.mepn-pathway.org. Nodes may also be coloured according to sub-cellular location by assigning the "Cellular location" class. It is also possible to import other class-sets of interest; for example information about a components regulation/behaviour in a dataset. Within BioLayout Express3D one can easily follow the flow of information through the pathway by selecting parent or children nodes of a component of interest to highlight them. BioLayout Express3D software is freely available and can be downloaded at http://www.biolayout.org/Click here for fileOverlay of Ifn-β Regulated Targets onto the Integrated Map of Macrophage Pathways. Components within clusters of co-ordinately expressed transcripts following Ifn-β stimulation were exported as class sets from a mouse expression graph generated in BioLayout Express3D and imported onto the 'human' macrophage pathway diagram (.layout file) within BioLayout Express3D. Pathway components that are regulated by Ifn-β are enlarged and coloured according to their behaviour within the dataset and all other nodes are coloured grey, in this way allowing the easy identification and visualisation of regulated components. BioLayout Express3D software is freely available and can be downloaded at http://www.biolayout.org/Click here for file


In recent years, there has been a huge increase in the amount of publicly-available and proprietary information pertinent to drug discovery. However, there is a distinct lack of data mining tools available to harness this information, and in particular for knowledge discovery across multiple information sources. At Indiana University we have an ongoing project with Eli Lilly to develop web-service based tools for integrative mining of chemical and biological information. In this paper, we report on the first of these tools, called WENDI (Web Engine for Non-obvious Drug Information) that attempts to find non-obvious relationships between a query compound and scholarly publications, biological properties, genes and diseases using multiple information sources.We have created an aggregate web service that takes a query compound as input, calls multiple web services for computation and database search, and returns an XML file that aggregates this information. We have also developed a client application that provides an easy-to-use interface to this web service. Both the service and client are publicly available.Initial testing indicates this tool is useful in identifying potential biological applications of compounds that are not obvious, and in identifying corroborating and conflicting information from multiple sources. We encourage feedback on the tool to help us refine it further. We are now developing further tools based on this model. In common with most scientific disciplines, there has in the last few years been a huge increase in the amount of publicly-available and proprietary information pertinent to drug discovery, owing to a variety of factors including improvements in experimental technologies , improvements in computer technologies (particularly the Web), funded "grand challenge" projects (such as the Human Genome Project), an imperative to find more treatments for more diseases in an aging population, and various cultural shifts. This has been dubbed data overload  Significintegrative fashion [One of the next great challenges is how we can use all of this information together in an intelligent way, in an  fashion . We can  fashion  allow stChemBioGrid, which has made a multitude of databases and computational tools freely available for the first time to the academic community in a web service framework [At Indiana University, we are tackling this problem from several angles. We recently developed a Cyberinfrastructure for cheminformatics, called ramework . Of partramework , and theramework . We are aggregate web services which call multiple "atomic" web services and aggregate the results, and Semantic Web languages for the representation of integrated data.With this infrastructure in place, we have investigated a variety of strategies for integrating the chemical and biological data from different sources in the infrastructure, in particular of (i) the application of data mining techniques to chemical structure, biological activity and gene expression data in an integrated fashion , (ii) thWENDI (Web Engine for Non-obvious Drug Information) that is designed to tackle a specific question: given a chemical compound of interest, how can we probe the potential biological properties of the compound using predictive models, databases, and the scholarly literature? In particular, how can we find non-obvious relationships between the compound and assays, genes, and diseases, that cross over different types of data source? We present WENDI as a tool for aggregating information related to a compound to allow these kinds of relationships to be identified.In this paper we describe one of the first products of this work, a tool called Of course, the power of this kind of integration comes from identifying truly non-obvious but yet real relationships between these entities. Our aim in this work is to allow a rapid differentation between known relationships , and unknown relationships . There is clearly some fuzziness in this, and this makes evaluation of a tool like WENDI for non-obviousness difficult. However, we do present it as a useful tool based on qualitative feedback from existing users, and we are currently devising ways of a more quantitative evaluation (as described in the concluding section).ChemBioGrid infrastructure to be the primary data source for WENDI. Additionally, for WENDI we have introduced the idea of aggregate web services that call multiple individual, or atomic, web services and aggregate the results from these services in XML. For example, the main web service used by WENDI takes as input a SMILES string representing a compound of interest, and outputs an XML file of information about the compound aggregated by calling multiple web services. This XML file can then be parsed by an intelligent client to extract information pertinent to compound properties. The overall architecture uses a four layer approach which we described previously [We have since extended the eviously  that incWeb services either follow the Simple Object Access Protocol (SOAP) standard  or REpreOur SOAP-based services are deployed in a in Tomcat 5.5 application container, which allows us to maintain these services easily and provides a high level of integration with our development environments, and with the service developed by Java 1.6.0. Our Web service layer is handled by the AXIS libraries 1.6 , which aOur infrastructure contains a large number of compound-related databases, including mirrors of existing databases (such as PubChem), databases derived from these (such as 3D structures of PubChem compounds), and completely new databases (particularly those derived from the literature). Our databases are housed on a Linux server running the PostgreSQL database system, with gNova CHORD  installeA mirror of the PubChem Compound database, containing compound ID's (CIDs), InChI, SMILES, compound properties, and 166-key MACCS-style fingerprints  generateA mirror of the PubChem Bioassay database containing AIDs (assay ID's), CIDs of compounds tested, and bioassay outcomes and scoresDescriptions of all PubChem bioassaysA similarity-searchable database of minimized 3D structures for PubChem compoundsA mirror of the DrugBank dataset  containiAn implementation of the Maximum Recommended Therapeutic Dose (MRTD) set  includinPubMed IDs of papers indexed in Medline, with SMa matrix of predictions of gene-disease relationships based on known relationships mined from the literature and machine learning predictions .cross-species chemical-gene/target interactions and chemical-disease relationships derived from experimental sets and the literature .an encyclopedia of human genetic variation in health and disease .a database of bioactive drug-like small molecules, containing 2-D structures, calculated properties  and abstracted bioactivities  .tanimoto function applied to the 2D public 166 keys, an implementation of the popular MACCS keys. Without indexing, it runs very effectively for a single query or on a small dataset, but the speed reduces significantly for large datasets. We have 56,911,891 compounds in our PubChem Compound table as the time of writing. To speed up the searching, we implemented a method described by Swamidass & Baldi to reduce the subset of molecules that need to be searched in similarity calculations [2D Tanimoto similarity searching of these datasets is made available by the gNova CHORD ulations . The metIn addition to 2D similarity searching, 3D similarity searching is provided on Pub3D database using 12-dimensional molecular shape descriptors  calculatWe have made available a variety of predictions through our web service framework, particularly:Tumor cell line predictions. We created 40 Random Forest models for prediction of human tumor cell line inhibition, trained using data from the NCI Developmental Therapeutics Program Human Tumor Cell Lines [• ll Lines . These pToxicity prediction. We implemented a special modified Web service implementation of ToxTree [•  ToxTree  for predGene-disease relationships. We have implemented a table of predictions of gene-disease relationships extracted from the PhenoPred tool developed at Indiana University [• iversity . Also weWe have created a main WENDI aggregate web service, and a web-based client that employs the web service. The web service takes a query SMILES string as input (through a SOAP or REST interface), and calls a variety of web services and database searches using the query. Results are returned as an aggregate XML file with sections delineated according to the atomic web service that was called. Additional XML tags are added by the web service, in particular, Gene Ontology terms in the PubChem Bioassay descriptions, Drug descriptions (from Drugbank) and paper titles and abstracts, are extracted and tagged with Gene Ontology ID's (GOID's). These permit associations to be made between genes and assays, drugs and papers.The client permits the user to input a SMILES string, or to draw a structure in using the JME editor , and theThe primary way that the databases are employed in WENDI is through similarity searching: finding compounds in the databases that are similar to the query, which have some known property: for example, we retrieve compounds that are similar (>0.85 Tanimoto) to a query molecule that are active in a given bioassay, are known drugs, or are referenced in a journal article. Based on the similar property principle  we can aThe WENDI interface is organized into six major sections:Predictive models results presents the predicted probability of activity of the compound in 40 Human Tumor Cell line assays, organized by panel type  and color coded according to probability of activity . Confusion metrics are also presented to allow the validity of these models to be assessed. Also presented are the results of a ToxTree analysis, particularly the classification according to Cramer rules [er rules  and a brActivities of similar compounds presents a list of similar compounds  in PubChem that have been tested in bioassays, and shown to be either active. A link to the bioassay along with the bioassay name is given, and an additional column uses the extraction of Gene Ontology terms from the bioassay description along with the PhenoPred predictions of gene-disease relationships to list possible related diseases. The DrugBank and MRTD sets are also similarity searched with the results presented in a similar fashion; in the case of DrugBank, drug usage descriptions are given along with predictions of diseases extracted in a similar way to the PubChem sectionSimilar compounds from chemogenomics data presents a list of similar compounds  from CTD, ChEMBL data that include the relationships with compounds and genes/diseases.Similar compounds from Systems data presents a list of similar compounds  from KEGG data that include the relationships with compounds and Pathways/Enzymes.Similar compounds in the literature lists journal articles in Medline where the title or abstract contains compounds with a Tanimoto similarity >0.85 to the query. Links are given to the Journal articlesInactivities of similar compounds presents the same informations as Activities of similar compounds sections, except for all of the similar PubChem compounds found that have been tested in bioassays and shown to be inactive.Finally, a link is given to the raw XML file, and PDF file for download.On submission of a query, WENDI generally returns results within a minute. We have tested WENDI with a variety of query compounds with known biological activities, one of them is described below. It can be simply tested by the reader by visiting the WENDI site. As an example, a screenshot of the first results returned for Doxorubicin are shown in Figure Doxorubicin is an anthracyclin antibiotic that is used primarily as a nonspecific tumor inhibitor . The mechanism of action is not fully understood, although it is thought to be a DNA intercalator.WENDI identifies several corroborating pieces of evidence for the biological actions of Doxorubicin. In particular, it (i) predicts that the compound has a high probability of activity in all but one of the tumor cell line screens (red) and a medium probability in HCT-15 (colon cancer); (ii) predicts that the compounds has toxic effects by our Toxicity prediction service, corroborated by descriptions from DrugBank; (iii) identifies specific tumor-related bioassays in which compounds similar to Doxorubicin  were found to be active ; (iv) identifies a wide variety of assays in which compounds similar to Doxorubicin are inactive; (v) identifies several similar drugs to Doxorubicin  along with descriptions corroborating the nonspecific anti-tumor activity; (vi) identifies numerous publications linking Doxorubicin and related compounds to a variety of tumor activities Figure A chemical compounds recently submitted to PubChem, but not collected in our database yet, were also used as queries for WENDI. The results and some interpretations are given in the Table In this paper, we present a integrative data mining tool for drug discovery using aggregate web services. WENDI aims to build a full picture of potential biological activities of a chemical compound through the aggregation of data from web services that represent diverse multiple sources . WENDI allows the identification of corroborating or conflicting information: for instance, a compound might be predicted active in a breast cancer cell line, and similar compounds might show active in a PubChem BioAssay related to breast cancer, or be co-located in a paper abstract with a breast cancer related gene. We are now deveoping a next generation of tools based on WENDI and our recent Chem2Bio2RDF system  for explProject name: WENDI (Web Engine for Non-obvious Drug Information)https://cheminfov.informatics.indiana.edu:8443/WENDI_PUBLIC/WENDI.jsp• Project home page: https: • Operating system(s): Platform independent• Programming language: Java• Other requirements: Java browser-embedded plugin• License: None. Any restrictions to use by non-academics: NoneThe authors declare that they have no competing interests.QZ carried out the whole implementation of WENDI, supervised by MSL and DJW. DJW made the examples in the result section, MSL and DJW modified this paper from the draft written by QZ. All authors have read and approved the final version of the manuscript.


The analysis of information in the biological domain is usually focused on the analysis of data from single on-line data sources. Unfortunately, studying a biological process requires having access to disperse, heterogeneous, autonomous data sources. In this context, an analysis of the information is not possible without the integration of such data.KA-SB is a querying and analysis system for final users based on combining a data integration solution with a reasoner. Thus, the tool has been created with a process divided into two steps: 1) KOMF, the Khaos Ontology-based Mediator Framework, is used to retrieve information from heterogeneous and distributed databases; 2) the integrated information is crystallized in a (persistent and high performance) reasoner (DBOWL). This information could be further analyzed later (by means of querying and reasoning).In this paper we present a novel system that combines the use of a mediation system with the reasoning capabilities of a large scale reasoner to provide a way of finding new knowledge and of analyzing the integrated information from different databases, which is retrieved as a set of ontology instances. This tool uses a graphical query interface to build user queries easily, which shows a graphical representation of the ontology and allows users o build queries by clicking on the ontology concepts.http://khaos.uma.es/KA-SB, which uses the BioPax Level 3 ontology as the integration schema, and integrates UNIPROT, KEGG, CHEBI, BRENDA and SABIORK databases.These kinds of systems (based on KOMF) will provide users with very large amounts of information (interpreted as ontology instances once retrieved), which cannot be managed using traditional main memory-based reasoners. We propose a process for creating persistent and scalable knowledgebases from sets of OWL instances obtained by integrating heterogeneous data sources with KOMF. This process has been applied to develop a demo tool  The need for data integration started when the number of applications and data repositories began to grow rapidly. The first approaches appeared in the 80s, and formed the basis for the research in this area. The evolution continued over mediator based systems, such as AMOS II , DISCO , TSIMMISThe rapid growth of the Internet has provided users with access to an unprecedented number of heterogeneous information sources. This huge amount of information and the complexities of handling it have given rise to a lot of research concerning practical approaches to the Semantic Web.Semantic Web searches have been based on existing systems, and the proposed approaches offer a limited amount of information for agents. Search engines cannot interpret all the information available because many documents have not yet been semantically annotated. We propose the use of an ontology-based mediator framework  to access varied information from diverse biological databases . KOMF haThis application can be used to extract integrated information from the set of databases included in the system, information which is retrieved as a set of ontology instances. However, the analysis of these instances is still limited in KOMF. In order to apply analysis tools it is necessary to store the instances appropriately to facilitate their access. However, the sheer number of instances that must be retrieved make the use of a traditional reasoner unfeasible , 12. ThuIn summary, the goal of this paper is to present a user query system based on combining a data integration solution with a reasoner, to boost the analysis potential for the knowledge obtained in response to user queries. The combination of a data integration system with a reasoner is a novel approach that opens up new ways of analyzing the information based on the knowledge. This is also the way to obtain a mediator which can reason on the integrated knowledge.http://khaos.uma.es/KA-SB showing how the BioPax Level 3 ontology can be used as the integration schema to integrate UNIPROT  negatively regulates Phosphorylation of ChREBP at Thr(666) by AMP kinase' has a control type inhibition, and so its classification as a Catalysis will be resolved as an annotation error.However, once the knowledgebase is created the set of instances may contain errors. The use of the reasoner will solve this problem. If an interaction is retrieved from the mediator, it is classified as Catalysis. However, if the control type is "INHIBITION", the reasoner infers that this is an inconsistency in the ontology. For example, the interaction named Another example is the physical entity, where we can find that an entity P is an instance of Protein and Complex classes (in two different databases). In this case the reasoner also infers that the knowledgebase has inconsistencies (Protein and Complex are defined as disjoint classes in this ontology). For example the protein complex "Cytochrome b6f Complex" may be annotated in one database as a Protein and as Complex in a different database. Thus, this inconsistency will be detected by the reasoner, and the application using this information can act to resolve this inconsistency.In this paper we have presented a novel system that combines the use of a mediation system (KOMF) with the reasoning capabilities of a large scale reasoner (DBOWL) to provide a way of finding new knowledge.The study of data integration systems has allowed us to determine their main elements, and thus to extract the pattern for building this kind of system.mediator [global schema of the whole heterogeneous database system , the external schema of the component databases, and statistics for query optimization.HERMES , DISCO  and TSIMmediator . The medIn the specific field of biological data the following examples exist: TAMBIS , BioDataDiscoveryLink  is one sOpenLink Data Spaces platform, which is said to be able to integrate numerous heterogeneous data from distributed endpoints.VIRTUOSO , comprehOBSERVER  presentsModel-Based Mediation  is a parDBOWL is an OWL reasoner. As OWL is based on DL, we must study DL reasoners. Of these, RACER  is the mThe presented tool uses a graphical query interface to build user queries easily. This interface shows a graphical representation of the ontology and allows users to build queries by clicking on the ontology concepts. Protégé  is a welHowever, the proposed system can be improved in many ways. The main drawback of this proposal is the configuration of KA-SB, which because it requires performing some manual steps, it is difficult for non-expert users:Firstly, the development (or search) of a domain ontology is necessary. This is an important issue in all the proposals for using semantics to improve any kind of process. This could be solved by designing new tools for scientists who are not semantic Web experts. In this sense, some systems have been proposed .Secondly, the configuration of KOMF requires the development of some steps that cannot be achieved by non-expert users. The first difficulty is that for accessing the information it is necessary to implement data services. This problem has several solutions: either to develop automatic tools for creating a data service  or to dhttp://khaos.uma.es/KA-SB, which provides a lot of opportunities to take advantage of the integrated information by means of a user interface for testing different queries. The reasoner allows users to exploit the results to search for new knowledge or to perform analyses. However, in a context like Systems Biology it is important to provide tools able to deal with a large amount of information, as the OMIC era has produced an enormous amount of freely available data.The combination of data integration solutions with reasoners to provide analysis tools in biology is a novel approach that opens up new possibilities in domains such as Systems Biology. The process described by KA-SB shows a way to use retrieved instances from user queries in a reasoner. This process has been applied to develop a demo tool Our approach can be useful for real Systems Biology applications, especially for those aiming to provide end-user interfaces with extended capabilities. In this sense, we will study how to apply our proposal to extend a real application in Systems Biology (developed using KOMF), the System Biology Metabolic Modelling Assistant , which i