The creation of accurate quantitative Systems Biology Markup Language (SBML) models is a time-intensive, manual process often complicated by the many data sources and formats required to annotate even a small and well-scoped model. Ideally, the retrieval and integration of biological knowledge for model annotation should be performed quickly, precisely, and with a minimum of manual effort.Here we present rule-based mediation, a method of semantic data integration applied to systems biology model annotation. The heterogeneous data sources are first syntactically converted into ontologies, which are then aligned to a small domain ontology by applying a rule base. We demonstrate proof-of-principle of this application of rule-based mediation using off-the-shelf semantic web technology through two use cases for SBML model annotation. Existing tools and technology provide a framework around which the system is built, reducing development time and increasing usability.Integrating resources in this way accommodates multiple formats with different semantics, and provides richly-modelled biological knowledge suitable for annotation of SBML models. This initial work establishes the feasibility of rule-based mediation as part of an automated SBML model annotation system.http://cisban-silico.cs.ncl.ac.uk/RBM/.Detailed information on the project files as well as further information on and comparisons with similar projects is available from the project page at  A quantitative model of molecular systems describes the dynamics of the interactions between biological entities involved. Such modelling is central to systems biology. Models and experiments are typically refined iteratively: models provide useful feedback to experimentalists, who in turn pass their results back to improve the model. The creation of systems biology models, such as those written in Systems Biology Markup Language (SBML)  or CellMSystems biology models can contain both the quantitative information required to run the simulation and biologically-meaningful annotation. However, while models often include the mathematical information required for simulations, they are not required to contain explicit information about the full biological context. Annotation provides a description of how a model has been generated and defines the biology of its components in a computationally-accessible fashion. While the presence of biological annotation aids efficient exchange, reuse, and integration of models, simulation does not require annotation and therefore such information is often limited or lacking. A well-annotated model is useful for simulation, as an input in other computational tasks and as a reference for researchers. A model with limited annotation is frequently useful only to the modeller who created it due to the often ambiguous naming schemes and lack of biological context ,4.Biological annotation in SBML is structured according to the Minimal Information Required in the Annotation of Biochemical Models (MIRIAM) specification . There aThe integration of life sciences data remains an ongoing challenge due to the multitude of data sources and formats differing both in syntax and semantics. Errors in data integration can occur when data sources do not describe their information with a shared semantics . The pro ontology -mapping, which maps between classes in two or more ontologies [mediator-based approaches, where a number of source ontologies are individually mapped to a single target, or core ontology. Usually, a core ontology is a description of the domain of interest rather than a description of the syntactic structure of the data. Previous work with ontology mapping and other semantic data integration methodologies in the life sciences [http://cisban-silico.cs.ncl.ac.uk/RBM/.An important concept in semantic data integration istologies . In the sciences ,14-18 arSystems biology model annotation can be time-consuming, as there are many resources for model annotation; the modeller may not even be aware of all of them. Accordingly, annotation is often slow and difficult. Current systems biology model annotation tools rely mainly on syntactic data integration methods such as query translation. While large amounts of data can be gathered and queried in this manner, the integration process is limited to resolving formatting differences without attempting to address semantic heterogeneity. While existing tools for model annotation are useful, semantic data integration methods can be used to resolve differences in the meaning of the data. syntactic ontologies which result from the conversion of a single data format into OWL constrained by Description Logics (OWL-DL) [Therefore, in the work presented here we have developed rule-based mediation as an approach to semantic data integration for systems biology. In rule-based mediation, we use a set of(OWL-DL) . These s(OWL-DL) . Rule-ba materialise the data in the core ontology by using a set of rules to translate the data from the syntactic ontologies to the core ontology. This avoids the complex query translation algorithms required by the BGLaV approach.In rule-based mediation, weIn short, rule-based mediation has a number of defining features:• the data from syntactically and semantically different data sources are materialised within a core ontology for easy access and stable querying;• the core ontology is a semantically-meaningful model of the biological domain of interest;• implementation can be performed using off-the-shelf tools and mapping languages.In rule-based mediation, syntactic heterogeneity is resolved through the conversion of disparate data formats into syntactic ontologies. The data from the syntactic ontologies is then mapped to the core ontology, which stores the data in a semantically-homogeneous way. The core ontology, which describes the biological domain of interest, can then be queried and reasoned over. There are three main parts to rule-based mediation: the syntactic ontologies for describing the disparate data formats, the core ontology, and the mapping rules for linking the syntactic ontologies to the core ontology.We have used standard languages for creating (e.g. Web Ontology Language (OWL) ), queryiAs with other mediator-based approaches, in rule-based mediation the data formats are converted to syntactic ontologies. In our approach, the purpose of syntactic ontologies is to describe the data formats as well as isolate syntactic variability so that the core ontology models only the semantics of the data being integrated. Though this method can use any ontological format, here a syntactic ontology is a conversion of a non-OWL format such as XML into OWL-DL. Information from one or more data sources sharing the same data format is loaded into that format's syntactic ontology. A syntactic ontology used in this way allows both the import of data to the core ontology and the export of new information to the underlying data sources.Often, mediator-based approaches build a core ontology as a union of syntactic ontologies rather than as a semantically-rich description of the research domain in its own right -27. In cBecause the core ontology is abstracted away from data formats, importing new data sources is made easier. Furthermore, the process of adding to the core ontology is simplified: each new mapping, class, or data import is incremental, without the need for large-scale changes. The richness of the core ontology depends on the type of biological questions that it has been created to answer; a detailed ontology may have higher coverage of the research domain, but may take longer to develop and reason over.In contrast to global-as-view or local-as-view approaches, where either the target or source ontologies are entirely described using views of the other, the methods used in rule-based mediation provide a way to decouple syntactic and semantic heterogeneity and allow the core and syntactic ontologies to remain distinct. Additionally, rule-based mediation maps and loads data directly into the core ontology, allowing simplified querying via the use of standard tools, mapping and querying languages. Since the core ontology is more than just the entailment of a set of syntactic ontologies, it could be used for more than just rule-based mediation. Being a semantically-rich model of a research domain, it could also be used as a standalone ontology for marking up data or as a reference model of its biological domain. Furthermore, due to its independence from the syntactic ontologies, it is much more flexible with respect to changes. antecedent for describing the initial conditions of the rule, and a consequent for describing the target of the rule. Whenever the antecedent of the rule is true, the conditions in the consequent must also hold. When mapping from the syntactic ontologies to core ontologies, the antecedent of the rule is derived from a syntactic ontology, while the consequent corresponds to the mapped concept within the core ontology. Once data have been integrated into the core ontology, the knowledge contained within the core ontology can be reasoned over and queried, and the query response formatted according to a suitable syntactic ontology. The quality of the data exported back to a syntactic ontology, and in turn to a base format, is dependent upon both the quality of the mappings to the syntactic ontology and the syntactic ontology's scope. If either the mappings are incomplete, or the underlying data format has no way of describing the new data, the export will lack information.The mapping in rule-based mediation is defined with the OWL-DL-based SWRL rule language. SWRL divides rules into aSQWRL is a rule language based on SWRL, and is used for querying an OWL ontology and formatting the query response. In a way analogous with how SQL SELECT statements are used in a relational database management system, SQWRL presents the user with the results of a query, but does not store those results back in the ontology. Within the context of this paper, SQWRL queries are used to test the structure of a query or rule, and to illustrate interesting, but indirectly-relevant, queries. If results should be stored, SWRL mapping rules will save the results within the ontology.Current systems biology model annotation tools have limited access to the semantics of the models. We have implemented rule-based mediation to address this limitation, creating a method of model annotation which imports information from data sources in a semantically- and syntactically-integrated way. This approach retrieves information that is tailored to the needs of the modeller and more relevant that that retrieved through syntactic methods. We have used a combination of existing tools, rule languages and novel mappings to perform proof-of-principle integration for two use cases.Figure In this paper we have extended a quantitative biological SBML model via rule-based mediation as a proof-of-principle for the methodology. To facilitate the use cases, we created a core ontology describing a subset of telomere biology. We then used three standard semantic web tools  as a syntactic ontology representing constraints from SBML, Systems Biology Ontology (SBO) and the SBML manual . MFO hasBioPAX was used in its native OWL-DL format. Information about the network neighbours of any given Pathway Commons entity can be accessed as a BioPAX document written in OWL-DL. Therefore, no additional syntactic ontology was needed since the populated BioPAX ontology returned from a nearest neighbour query could be used directly.The PSI-MIF and UniProtKB syntactic ontologies were created from their XML data formats. Ontology classes represent element and attribute types, while the data items themselves are represented with ontology instances. Figure  telomere ontology, which models the biology relevant to the Proctor et al. model [Once the syntactic ontologies are created, the second step in rule-based mediation is the creation or identification of a suitable core ontology. Unlike the syntactic ontologies, which are designed to be OWL-DL representations of the underlying data formats, a core ontology is an explicit description of the semantics of the research domain. The core ontology created for these use cases is thel. model  of telomhttp://cisban-silico.cs.ncl.ac.uk/RBM/.Rule-based mediation was performed for two use cases to show proof-of-principle for SBML model annotation. The mapping rules from the syntactic ontologies to the telomere ontology were run prior to performing the queries over the data. These rules populated the telomere ontology with the instance data from the syntactic ontologies. The telomere ontology, the syntactic ontology, the SWRL mappings, SQWRL queries, and the SBML model before and after annotation in these use cases are available on the project page at Saccharomyces cerevisiae. In Use Case 1, we annotate a single SBML species with information relevant to the gene RAD9. Adding information to existing SBML elements at an early stage aids model development and extension prior to publication or deposition. In Use Case 2, we retrieve possible protein-protein interactions involving the RAD9 protein. This approach resulted in the identification of model elements as well as a putative match for an enzyme that was not identified in the original curated model. These examples show how rule-based mediation works in a systems biology setting. The use cases also show that rule-based mediation successfully integrates information from multiple sources using existing tools, and that it would be useful to expand the implementation of this method to larger biological questions and automated model annotation.In this paper, we consider two methods for enriching an existing model of telomere uncapping in  S.cerevisiae query gene RAD9. Queries for proteins possessing 'rad9' as a name or synonym are stored within the telomere ontology as SWRL rules. An example rule is shown below. This rule is one of those used to collect all matching telomere ontology proteins and further declare them specifically as RAD9 proteins:Use Case 1 demonstrates the addition of basic information to the species such as cross-references, SBO annotations, compartment localisations and a recommended name. For this use case we queried the telomere ontology for information about thetuo : Protein(?rules : someEntity) Λ tuo : synonym Λ swrlb : containslgnoreCase → tuo : Rad9(?rules : someEntity) S.cerevisiae.The rule queries the synonyms of the protein names within the telomere ontology for a string matching “rad9”, and declares any matches as RAD9 proteins. In use, this and related rules uncovered three instances, each from a different data source: one from Pathway Commons, one from UniProtKB, and one from BioGRID. These three instances were then declared equivalent. The final step of this use case is to restrict the instances of RAD9 to the organism of interest, et al. SBML model. In order to export the relevant knowledge to MFO, 14 mapping rules with telomere ontology classes in the antecedent and MFO classes in the consequent were created. Four of the 14 were used specifically to create MIRIAM cross-references and SBO terms. The two Proctor et al. RAD9 species originally contained a single reference to UniProtKB, which was confirmed by the information retrieved from the telomere ontology. In addition, an SBO Term and five other MIRIAM annotations from Intact, UniProtKB, Pathway Commons and SGD were exported.Once integrated, the information contained within these three instances is then sent out, using MFO, to a new version of the Proctor et al. model has four interactions between RAD9 and other proteins, as described in the Proctor et al. column of Table Use Case 2 shows how possible protein-protein interactions can be retrieved to annotate models. Specifically, interactions involving RAD9 were retrieved from the telomere ontology, confirming those already present in the curated model as well as discovering novel interactions. The Proctortuo : Rad9(?rules : rad9instance) Λtuo : plays Λtuo : hasParticipant →sqwrl : selectThe above query identifies interactions involving RAD9 instances, and displays the name of the instance as well as the process in which it is involved. The use of SQWRL here rather than SWRL allows the more than 270 returned interactions to be viewed. These results are deliberately not saved in the asserted ontology as many are not meaningful for the model under study. Instead, the SQWRL query was used to determine the full set of results, and then SWRL rules were used to filter the interactions according to those species already present in the model. Protein instances were classified in a similar way to Use Case 1. The example SWRL query shown below classifies CHK1 protein instances:tuo : Protein(?rules : someEntity) Λtuo : synonym Λswrlb : containsIgnoreCase →tuo : Chk1(?rules : someEntity)The combined results from the rule above together with 12 associated rules allowed four proteins of interest to be classified. Specific interactions of interest were then identified. The OWL axioms on the telomere ontology class for interactions between RAD9 and RAD53 are shown below as an example. This class is one of four defined classes which filter the RAD9 interactions.Rad9/Rad53 Interactions :tuo : hasParticipant some (tuo : playedBy some tuo : Rad9) Λtuo : hasP articipant some (tuo : playedBy some tuo : Rad53)Table There are a total of four interactions involving RAD9 in the curated model. Two of these interactions, with RAD53 and with CHK1, were confirmed. The RAD53 interaction was found in both BioGRID and in Pathway Commons, while the CHK1 interaction was only in Pathway Commons. The other two RAD9 interactions present in the model include placeholder species, which were created by the modeller to describe proteins whose identity was unknown. Because these proteins had no defining features, they could not be directly identified with the same methods used for RAD53 and CHK1. We provisionally identified one of those placeholder species, marked as 'Rad9Kin' in the model, as the protein MEC1 using data originating in BioGRID.In the original curated model, the 'rad9Kin' species does not have a UniProtKB primary accession, as the model author did not know which protein activated RAD9. However, MEC1 is shown in the telomere ontology as interacting with RAD9 and is present elsewhere in the curated model reactions. Further, UniProtKB reports MEC1 as a kinase which phosphorylates RAD9. From this information, the model author now believes that MEC1 could be the correct protein to use as the activator of RAD9 . RAD17 iFinally, the rules to export the new information from the telomere ontology to MFO, as described in Use Case 1, were executed. As a result, new species, reactions, and MIRIAM annotations were added to a new version of the original SBML model. Existing SBML species gained MIRIAM annotations, names and SBO terms. Two new reactions were also added: RAD9 with RAD17, and RAD9 with MEC1. The former was chosen to demonstrate how the formation of a protein complex is mapped back to MFO, and the latter was chosen as an example of the mapping of an interaction about which less is known. Furthermore, since MEC1 is a candidate for the value of the 'Rad9Kin' species in the curated model, a skeleton reaction in the annotated version of the curated model acts as a placeholder for manual curation. Further work by the modeller could then fill in the base reaction, species and annotation, including parameters, initial values and rate constants.We have created a new method of semantic data integration called rule-based mediation that reduces the amount of manual effort required to annotate a systems biology model, and which allows access to the underlying semantics of the data. Rule-based mediation makes use of a semantically-rich core ontology together with mappings to and from syntactic ontologies that represent the data sources of interest. New data sources can be easily inserted without modifying the biologically-relevant core ontology. Separation of syntactic integration and semantic description of the biology is robust to changes to both the syntactic ontologies and the core ontology. The use of existing tools decreases development time and increases the applicability of this approach for future projects.Rule-based mediation contains aspects of both global-as-view and local-as-view strategies. Global-as-view mapping defines a core ontology as a function of the syntactic ontologies rather than as a semantically-rich description of the research domain in its own right, though the level of dependence of the core ontology can vary -27. Howe et al.[Syntactic heterogeneity was resolved by converting the four data formats into OWL-DL. When performing this step, issues of scalability, scope and provenance were considered carefully. For instance, XMLTab is not an ideal choice for long-term use, as it does not scale well, and cannot load multiple XML files at once into a single syntactic ontology. Additionally, it is not clear that the XMLTab project is in active development. However, tools such as the SWRLAPI's XMLMapper  or Krext et al..Secondly, there are limitations in describing data provenance using the SBML standard syntax. When new MIRIAM annotations are added to an SBML model, there is no way of stating which procedure added the annotations, or the date they were added. At a minimum, the addition of the software type, version number and date on which the information was added should be included. The authors have been in discussions with the SBML community since the 2009 Hackathon to determine how best to solve this problem. Until this issue is resolved, no provenance information is included in the newly-annotated models.The final issue to consider is scope: many data sources are able to export in multiple formats, but such formats may not provide the same set of information. Even though a particular format might be available, it may not contain the complete set of information present in the data source. For instance, BioModels data are available in many formats including BioPAX and SBML, and models from BioModels could be loaded into the telomere ontology via BioPAX. However, the native format for most BioModels is SBML, and conversion from this format is not lossless: for example, quantitative data such as rate laws are not supported in BioPAX. Therefore if BioModels database entries are to be annotated, input via MFO is best. Because the choice of data format can influence the type of data ultimately entering the core ontology, this syntactic phase of integration can also influence the semantic resolution step of rule-based mediation.Semantic heterogeneity in rule-based mediation is resolved through precise mapping rules and a well-written core ontology. The biological domain of interest must be modelled appropriately, and the incoming data must be linked to appropriate concepts in the core ontology. However, semantic data integration is not simple, and there are a number of issues to be addressed when performing this step.owl:sameAs construct. This method is ill-suited to automated procedures. However, tool and reasoning support for OWL 2, now a W3C Proposed Recommendation [hasKey [Firstly, in order to assert equivalences between instance data from the various data sources, the authors manually applied the endation , is grow [hasKey  construcSecondly, rules linking syntactic ontologies to the core ontology can be quite complex. Many rules in the work presented here have 14 or more clauses, but once a rule is in place, its execution is efficient and does not often need to be repeated. However, there are some limitations in the rule languages used. For instance, SWRL allows the creation of new ontology instances during rule execution. While this is useful, it can lead to unexpected results such as multiple identical individuals being created each time the rule is run. These limitations can be ameliorated through more sophisticated usages of OWL and SWRL tools. One such tool already used within the MFO project is the OWLAPI , which aA type of semantic heterogeneity that can be difficult for any integration system to address is the use of two distinct data items from the one data source to describe a single concept from another, e.g. where a protein is described by two UniProtKB entries but only a single SBML species. In rule-based mediation, we represent this heterogeneity rather than reconcile it. However, such cases have not been created by this work, only present via imports from existing sources. The opposite case, where a single data item is used to annotate multiple concepts, is common in SBML, for example with active or inactive species. This is specifically allowed by the telomere ontology.speciesType or species elements for the concept of a protein, and speciesReference for the interactor. Within BioPAX level 2, this is exemplified by physicalEntity and physicalEntityParticipant. A similar dichotomy exists within the telomere ontology. When converting the information retrieved in Use Case 1 back to SBML, these considerations need to be taken into account. Ultimately, the use of speciesType is not ideal, as future versions of SBML will have a different way of modelling such concepts [Finally, an understanding of the semantics of the data formats is required when developing the export mapping rules from the core ontology to a syntactic ontology. A simple case would be how different formats define similar concepts. For instance, a protein in BioPAX is strictly defined as having only one polypeptide chain, while a protein in UniProtKB can consist of multiple chains. A more complex case is when two concepts, e.g. two interaction instances, have virtually indistinguishable properties, making it difficult for automated procedures to know which interaction to use. Another challenge is addressing cases where concepts in the core ontology could map to more than one concept in the syntactic ontology. For example, in most systems biology formalisms there is a clear delineation between a protein as an abstract concept and a protein as a specific interactor in a reaction. Within SBML, this is defined using the concepts . TherefoManual SBML model creation and annotation can be a slow process, and is made more difficult by the large number of data sources relevant to modelling. We have shown that rule-based mediation is capable of resolving both syntactic and semantic heterogeneity. Specifically, addressing syntax and semantics separately allows the straightforward addition of new information; the modelling of the biological domain of interest without any knowledge of underlying formats; and simple manipulation of the mapping rules using off-the-shelf tools. et al. model. Future work will use this approach as the core of an automated semantically-aware model annotation system.We showed a proof-of-principle application of rule-based mediation to systems biology model annotation, producing a new SBML model containing new MIRIAM annotations, new species, and new reactions. Syntactic ontologies for the UniProtKB and the PSI-MIF formats were created, while MFO was updated and BioPAX was used without modification. Additionally, a telomere ontology was developed to model the biology associated with the use cases. In the course of this work, not only have new biological annotations been added, but we have discovered new biology by putatively identifying a previously-unknown protein in the ProctorALL developed rule-based mediation and the described ontologies, and drafted the manuscript. PL, MP and AW participated in the development of the integration technique and ontologies. PL and AW helped to draft the manuscript. All authors read and approved the final manuscript.The authors declare that they have no competing interests.


Pathway models serve as the basis for much of systems biology. They are often built using programs designed for the purpose. Constructing new models generally requires simultaneous access to experimental data of diverse types, to databases of well-characterized biological compounds and molecular intermediates, and to reference model pathways. However, few if any software applications provide all such capabilities within a single user interface.. Data arises from metabolomic (lipidomic), microarray, and protein array experiments performed by the LIPID MAPS consortium of laboratories and is arranged by experiment. Facility is provided to create, connect, and annotate nodes and processes on a drawing panel with reference to database objects and time course data. Node and interaction layout as well as data display may be configured in pathway diagrams as desired. Users may extend diagrams, and may also read and write data and non-lipidomic KEGG pathways to and from files. Pathway diagrams in XML format, containing database identifiers referencing specific compounds and experiments, can be saved to a local file for subsequent use. The program is built upon a library of classes, referred to as the Biopathways Workbench, that convert between different file formats and database objects. An example of this feature is provided in the form of read/construct/write access to models in SBML (Systems Biology Markup Language) contained in the local file system.The Pathway Editor is a program written in the Java programming language that allows de-novo pathway creation and downloading of LIPID MAPS (Lipid Metabolites and Pathways Strategy) and KEGG lipid metabolic pathways, and of measured time-dependent changes to lipid components of metabolism. Accessed through Java Web Start, the program downloads pathways from the LIPID MAPS Pathway database (Pathway) as well as from the LIPID MAPS web server Inclusion of access to multiple experimental data types and of pathway diagrams within a single interface, automatic updating through connectivity to an online database, and a focus on annotation, including reference to standardized lipid nomenclature as well as common lipid names, supports the view that the Pathway Editor represents a significant, practicable contribution to current pathway modeling tools. Pathways may be broadly described as models that characterize movement of material through a network of molecular species and processing steps. They serve as the basis upon which much of the new field of systems biology must build. Many tools have become available over the last 10 years for enabling biological pathway construction . Their cIn general, the field of metabolomics involves inducing perturbations to the ongoing state of living systems and subsequently monitoring changes to compounds at specific time points. The interactions among components of a pathway are then inferred by a variety of techniques, including metabolite fingerprinting and profiling, and by comparison between organisms that have been genetically perturbed or subjected to altered nutritional states ,9. To seThe Pathway Editor is the initial application of the Biopathways Workbench project. The Biopathways Workbench is an on-going effort of LIPID MAPS to build a programming toolkit, applications, and a database for creating, managing, visualizing, and editing signaling and metabolic pathways. The Biopathways Workbench makes extensive use of the connectivity that is possible between data sources provided by the internet and the local file system of a user. The toolkit includes capability for read-only access to Pathway and other databases, and for reading and writing different file formats.Brief descriptions of the Biopathways Workbench and the Pathway Editor during early development have been presented previously .2-lipid A, a purified component of bacterial lipopolysaccharide (LPS) which may be responsible for much, if not most, of the pro-inflammatory response to LPS. LPS mimics processes of a similar nature occurring in many disease states and conditions, including inflammation, atherosclerosis, and altered growth [Pathway is implemented as a relational database, using Oracle9i Enterprise Edition Release 9.2.0.2.0, running on a Sun Fire 880 server and maintained at the San Diego Supercomputer Center, University of California - San Diego by the LIPID MAPS consortium's Bioinformatics core. At the time of writing, the database contains LIPID MAPS experimental data acquired from treatments of RAW 264.7 cells, a cell line derived from macrophage tumor cells . The celd growth . Lipids d growth . Descripd growth . All of The Pathway Editor is written in the Java programming language and is downloaded as a Java Web Start application from the LIPID MAPS website . Web StaThe Pathway Editor appears as native look and feel applications on Microsoft Windows and Apple Macintosh operating systems. Modern graphics accelerator hardware  is required. A link to a tutorial on use of the Pathway Editor may be found on the Pathway Editor download page .The design of Pathway Editor follows the Model/View/Controller (MVC) design pattern . The ModThe Pathway Editor constructs pathway objects using a standardized object vocabulary that includes small molecules, proteins, nucleic acids, experiments, and time course datasets. Code representations of nodes and interactions within a pathway are called participants, and contain information that is used for drawing on the VisPathwayPanel . Node participants may also contain experiment data.Complete pathway diagrams may be saved as .path files in XML format for future viewing in the Editor, or as image files of the drawing surface in various formats.Additional implementation details are described in the Results section.The focus of data management in Pathway is the experiment, identified by an experiment ID (identifier) and its associated metadata . An experiment contains all of the measurements and compounds or gene symbols for treatments performed by a particular laboratory on a particular date in the form of sets of time course data, or datasets. A dataset consists of a string representing a treatment type and a series of time points . The dataset contains one or more measurements at each time point. Time units are considered as part of the enveloping experiment, while measurement units are contained within the dataset.The Pathway database also contains references to small molecules as identified by LIPID MAPS structure database identifiers ,21 and tWhen experiments are loaded into the Pathway database, each dataset becomes associated with a corresponding small molecule ID, protein ID, or combined protein identifier plus gene symbol identifier. The dataset of a compound thus becomes linked to the characteristics of the compound, such as small molecule category, molecular weight, synonyms, comments, and the external database identifiers described in the preceding paragraph. Further details on experiments and datasets may be found in the on-line tutorial .The Pathway database contains KEGG reference metabolic pathways for lipid metabolism. The components of the pathways are parsed from KEGG XML files originally written in the KEGG Markup Language (KGML), and include pathway name and compound or protein name assigned to pathway nodes, node connectivity and stoichiometry, their respective map coordinates, and KEGG database IDs for KEGG LIGAND COMPOUND and KEGG COMPOUND ENZYME . If the A view of the Pathway Editor showing a sample LIPID MAPS pathway (the Arachidonic acid pathway) is shown in Figure View preferences, such as node/interaction color, shape, font size, and others may be configured by a preference assignment dialog accessed from the popup menu, or by global preference assignment dialogs accessed on the View menu.The program also uses the JFreeChart code package  to generThe user may initiate a session with the Pathway Editor in a number of ways. Perhaps the most powerful mode may be entered by downloading a pathway. A pathway file in the Biopathways Workbench format (ending with the extension .path) may be downloaded from the LIPID MAPS website server  by meansKEGG pathway files may be downloaded directly from Pathway, again using a dialog available through the File menu Figure . If the Nodes on the drawing surface of the Pathway Editor are of four types, following KEGG terminology: small molecule, protein, nucleic acid, or unknown (the default type). Each node  may contain a referenced compound ID. Protein nodes may also contain a list of one or more gene symbols. Processes, or interactions that connect nodes, are of type metabolic, signaling, or unknown (the default type). These compound IDs, symbols, and types may be assigned using dialogs accessible via mouse buttons and top menu items.The user assigns compounds  to nodes by accessing a database search dialog and searching the Pathway database. The search may utilize compound and synonym names or fragments of names. Once assigned, the Pathway database compound ID and compound metadata becomes associated with the node. Compound metadata is updated automatically from the database whenever a .path file is downloaded or opened.Node create button in the toolbar is pressed and the drawing area is clicked. To connect nodes, the Node connect button is pressed, a node is selected, and, keeping the mouse button pressed, the mouse is moved to a second node, and the mouse is released. The resulting interaction, or process, may be assigned information using the mouse and pop-up dialogs, in a manner similar to nodes.Nodes are created by combined use of toolbar buttons and mouse actions. To create a node in any part of the drawing area, the Within the Pathway Editor, experiments are managed independently of pathway components. When an experiment is presented to the Pathway Editor, the Pathway Editor automatically associates datasets with nodes containing the compound ID or gene symbol for the dataset. Experiments may be downloaded from the Pathway database separately from pathways Figure , or may During the process of constructing a pathway, a user constructs a node of the desired type, and assigns a compound to a node by searching Pathway for the name of a compound or gene symbol, using a compound information dialog for the node type. Once found and selected, the Pathway database ID for the compound and/or gene symbol then becomes incorporated into the participant object that is contained in the local pathway. If experiment data is loaded in the program, the program then automatically traverses the experiment data contained within the internal pathway object and assigns datasets containing the same database compound ID or gene symbol to the participant contained within the node for display.2-lipid A vs. control treatments) on the Select tab of the Node information dialog , technical replicates (multiple within-experiment replicates), or treatment significance  of changes to compounds in the system under study.The user is further aided in the process of compound assignment by having the ability to change the visibility of entire experiments, of datasets for individual compounds, and of datasets for individual gene symbols in the display, as desired. The user may choose to reject assignment of a compound to a node because of poor reproducibility of measurement, or may reject an individual experiment because of inconsistency within the experimental data, thus accepting the compound.Further information on the compound may be obtained by double-clicking a table row containing an identifier in the databases tab of the compound's information dialog, as in Figure The Pathway Editor reads and writes Systems Biology Markup Language (SBML) models through level 2, version 3 ,18,25. SThe SBML plugin in Pathway Editor is designed to be fully compatible with the SBML specification . MathemaTools for constructing and editing pathways require drawing on readily available information of various types. Features of the Pathway Editor that assist in providing this information are: First, it provides simultaneous access to time course (metabolomic) and microarray measurements, as well as protein array data. Intermediates in pathways are identified with reference to compound names as defined by an ongoing effort to standardize lipid nomenclature (LIPID MAPS) ,21 as we).Of the several tools available, VANTED  may perhBiologicalNetworks  is a croSmall molecules in the Pathway database are annotated with reference to LIPID MAPS lipid identifiers and KEGG small molecule IDs. In some cases, the precise stereochemistry of a measured lipid may not be known because of ambiguity in structure assignment . The timFor constructing pathways de novo, the Pathway Editor currently depends upon user interaction. The process of pathway construction relies heavily on domain knowledge. However, the focus of the Pathway Editor is on access to data, its graphical representation, and annotation. It is hoped that its appealing visual design, richness of features, simplicity of default preferences, and responsiveness, as well as similarity of the descriptive terminology used by the program  with laboratory methodology, will support facile use of the Pathway Editor by experimenters.2-lipid A, as well as models for minimally modified LDL (low density lipoprotein) [Pathway is enhanced with additional experimental data as it is acquired and validated. Currently, LIPID MAPS laboratories are using several types of primary macrophages from mice as target systems. RAW 264.7 subcellular fractions are also being studied. Lastly, treatments with different agents, including an inhibitor of cholesterol metabolism, compactin, in conjunction with Kdoprotein)  are undeThe Biopathways Workbench component of the Pathway Editor interfaces with the Pathway database and with the local file system. It converts textual and numeric information into data structures that can be transferred to the rest of the application. These data structures represent concrete biological entities and comprise the core of a vocabulary which can be extended as necessary as other databases are accessed and new analysis features are incorporated in the Pathway Editor and other applications.A number of extensions to the Pathway Editor, making use of Biopathways Workbench toolkit classes, are planned. Support for additional pathway model file formats, such as BioPAX  will be Investigators may contact the authors to discuss requests to add on-line access to their data and compound databases in the Pathway Editor.The Pathway Editor provides a user interface that features multiple functions for rapid pathway construction, including access to multiple pathway diagrams and experimental data sets; a focus on annotation, including references to standardized lipid nomenclature as well as common lipid names; and automatic updating of experimental data and annotations through connectivity to an online database. This rich feature set is not available in any existing pathway drawing tool.Project name: Biopathways WorkbenchProject home page: Operating systems: Windows, Apple MacProgramming language: JavaOther requirements: Java 1.5 or higher; installation of libsbmlLicense: Freely availableRestrictions to use by non-academics: NoneRWB wrote Pathway Editor code, proposed features of the Pathway Editor, designed the Pathway database, and drafted the manuscript; DC designed the Pathway database and critiqued the manuscript; AM proposed features of the Pathway Editor and designed the Pathway database; JL designed the Pathway database; DN wrote Pathway Editor code, proposed features of the Pathway Editor, and designed the Pathway database; SS proposed features of the Pathway Editor and critiqued the manuscript. All authors read and approved the final manuscript.


Life depends on the capacity of individual cells to respond effectively to cues about their changing internal and external environments. Cellular decision making and responses are orchestrated by complex molecular networks consisting of entities such as proteins or RNAs connected by interactions such as activation or synthesis. Information contained in primary databases and in the experimental literature relevant to these networks is so extensive and rapidly growing that it is increasingly difficult to integrate. As an aid to theoretical and experimental research, it is convenient to distill the inferences contained in the experimental literature and databases into knowledgebases that consist of annotated representations of biological pathways.Pathway building has been performed by individual groups studying a network of interest  as wellThe curation process of a biological pathway entails identifying and structuring content, mining information manually and/or computationally, and assembling a knowledgebase using appropriate software tools. A schematic illustrating the major steps involved in the data-driven and knowledge-driven construction processes is shown in Parts of Animals to the molecular realm has defined large numbers of entities and interactions in various cells and organisms. Recent attempts to improve knowledge integration have led to refined classifications of cellular entities, such as Gene Ontology (GO), and to the assembly of structured knowledge repositories. Data repositories, which contain information regarding sequence data, metabolism, signaling, reactions, and interactions are a major source of information for pathway building. A few useful databases are described in http://www.pathguide.org.The extension of reductive biology begun with Aristotle's Various standard, computer readable, object-oriented formats have been developed to facilitate the organization, storage, exchange, and parsing of pathway knowledgebases and the relevant experimental evidence information. Important pathway and pathway-related formats, which are all XML-based, include Systems Biology Markup Language (SBML), Proteomics Standards Initiative–Molecular Interactions (PSI-MI), and Biological Pathways eXchange (BioPAX) . SBML, wPathway building tools are required to populate, visualize, and store a pathway. Currently there are various pathway building tools  that proPathway curation can be either manual or automated. Manual curation provides the most reliable information extraction from the literature. However, the pace of new discovery can make manually populated databases difficult to maintain. In the mining process, use of appropriate keywords increases the chances of identifying the relevant information. Automated text mining through Natural Language Processing reduces the personnel required for recovery of information, but has severe limitations in accuracy. Information in the scientific literature is highly specialized, semantically unpredictable, and often not textual. Agreeing on “facts” is difficult even for expert curators. The present generation of text mining tools is probably most useful as an aid to manual curation.The efficient mining of information from the plethora of resource databases hinges on the identification of the most useful primary literature and databases for the biological area of interest. This often poses a challenge, as the choice of databases and mining strategies are biological area–specific. We find Reactome, UniHI, and Ingenuity Systems useful and appropriate for many biological areas.We provide here an example of assembly of a human dendritic cell signaling pathway involved in responding to microbes, assembled in CellDesigner, built using a KDO-based information mining approach. A snapshot of the pathway is in DDO pathway building, which can follow a similar process, differs in that the starting point is typically a collection of genes or proteins identified in a global experiment whose relationships are not well understood. In this case, the pathway building process is used to elucidate the pathways and functional relationships shared by regulated entities.Pathway analysis refers to the computational approaches used to investigate network behavior as a system. Pathway analysis can be broadly classified into two types: topological/structural network analysis and dynamical analysis.Topological analysis of a pathway identifies the global qualitative properties of the system . One appDynamical analysis, a higher resolution mathematical modeling, elucidates the detailed local and certain global quantitative behaviors of the system. Dynamical analysis requires more information on the reaction parameters and initial conditions than topological approaches . DetermiTable S1http://www.pathguide.org). A, automated curation; B, both manual and automated curation; BIND, Biomolecular Interaction Network Database; BioPP, Biological Pathway Publisher; DIP, Database of Interacting Proteins; EcoCyc, Encyclopaedia of E. coli Genes and Metabolism; GNPV, Genome Network Platform Viewer; HPRD, Human Protein Reference Database; KEGG, Kyoto Encyclopedia of Genes and Genomes; M, manual curation; MetaCyc, a Metabolic Pathway database; MINT, Molecular Interation Database; MIPS, Munich Information Center for Protein Sequences; N, No; OPHID, Online Predicted Human Interaction Database; PANTHER, Protein Analysis through Evolutionary Relationship Database; PID, The Pathway Interaction Database; STKE, Signal Transduction Knowledge Environment, UNIHI, Unified Human Interactome; Y, yes.A comprehensive list of databases can be found in Pathguide ((61 KB DOC)Click here for additional data file.


Analysis of data from high-throughput experiments depends on the availability of well-structured data that describe the assayed biomolecules. Procedures for obtaining and organizing such meta-data on genes, transcripts and proteins have been streamlined in many data analysis packages, but are still lacking for metabolites. Chemical identifiers are notoriously incoherent, encompassing a wide range of different referencing schemes with varying scope and coverage. Online chemical databases use multiple types of identifiers in parallel but lack a common primary key for reliable database consolidation. Connecting identifiers of analytes found in experimental data with the identifiers of their parent metabolites in public databases can therefore be very laborious.Here we present a strategy and a software tool for integrating metabolite identifiers from local reference libraries and public databases that do not depend on a single common primary identifier. The program constructs groups of interconnected identifiers of analytes and metabolites to obtain a local metabolite-centric SQLite database. The created database can be used to map in-house identifiers and synonyms to external resources such as the KEGG database. New identifiers can be imported and directly integrated with existing data. Queries can be performed in a flexible way, both from the command line and from the statistical programming environment R, to obtain data set tailored identifier mappings.http://metmask.sourceforge.net, that implements our ideas.Efficient cross-referencing of metabolite identifiers is a key technology for metabolomics data analysis. We provide a practical and flexible solution to this task and an open-source program, the metabolite masking tool (MetMask), available at  Efficient analysis of data from high-throughput experiments requires sufficient access to information about the measured biomolecules. Such data are often referred to as meta-data and provide a biological and chemical context in the form of parameters such as function, localization, and structure. Numerous applications of genomics and transcriptomics data analysis depend on the availability of meta-data, including pathway projections ,2 and ge1H-NMR [Metabolomics data analysis is no exception, and apart from the aspects of biological interpretation, meta-data are also needed for data integration. In particular, wide coverage detection of metabolites can only be achieved by combining multiple metabolomics platforms  such as 1H-NMR . HoweverData on biomolecules can be found in online databases, which must be cross-referenced to allow for collation of meta-data packages for individual data sets. Creating mappings between local identifiers used in experimental data and public identifiers is a laborious process since missing, ambiguous or redundant entries are common. Identifiers are also subject to frequent changes and it is therefore clear that cross-referencing must be an automated process to enable efficient and reproducible research. For sequence-based data, several tools have been developed that can be used to automatically cross-reference public databases. The AnnBuilder R package  assembleSeveral public databases are focused on gathering information about metabolites and integrating this with data on genes and proteins. Notable examples include the Chemical Entities of Biological Interest database (ChEBI) , the KyoThere are several aspects of metabolite identifiers that make them difficult to cross-reference in an automated fashion. A major obstacle is the lack of a widespread standardized identifier. There is a multitude of different schemes for referencing chemical compounds because the best way of doing so largely depends on the purpose of the identifier. Metabolites are in general best referred to by their absolute chemical structure using e.g. InChI . However, in certain circumstances it is necessary that the identifier is human readable warranting the use of chemical synonyms; on other occasions, we need to refer to a specific resource and therefore use database keys. Currently, chemical databases solve this problem by using multiple types of identifiers in parallel.Unfortunately, with different databases relying on different identifiers, consolidation becomes very difficult , especiaAnother serious problem for data integration is that most referencing schemes are redundant in the sense that the same compound has multiple valid identifiers. Therefore, even if everyone used, e.g., PubChem IDs as suggested by Kind et al. , cross-ranalyte does not necessarily correspond to the same chemical structure as the original metabolite and is therefore associated with a different identifier. These identifiers must be mapped back to their plain structure prior to biological interpretation and integration with data from other platforms. This problem becomes especially vexing since different platforms may use different analytes for the same metabolite. The abstraction between analytes and metabolites is typically only defined in platform-specific in-house libraries . Taken One approach to solve the problem of gathering meta-data for metabolomics would be to build a new database of chemical compounds including all known analytes, how they relate to parent metabolites as well as links to all relevant biological resources. Such a project, however, would be extremely resource intensive, and since different metabolomics researchers use different reference libraries and have different ambitions, it would still not solve the problem completely.Instead, we opted for a more pragmatic strategy and designed a program that can import both in-house reference libraries and online resources and organize the identifiers by how they are interconnected. By this method, groups of compounds are formed containing both the analytes and the metabolites they refer to as well as links to the selected biological databases. Because all available identifiers are used in parallel, there is no need for any master identifier, and databases can be consolidated as long as they can be linked using any of the imported resources. The result is a metabolite-centric database, which can be used to obtain tailored metabolite meta-data in a flexible and straightforward manner.Here, we present and discuss our strategy for reconciling metabolite identifiers across in-house libraries and public databases. Examples are given both for how to create and query a custom database as well as the types of data analysis that this technology enables. Using the provided software, MetMask (the metabolite masking tool), tailored mappings between different metabolite identifiers are easy to construct, thereby providing meta-data accessibility similar to that known from gene expression data analysis.The goal of this project was to construct a method to efficiently cross-reference different types of metabolite identifiers in order to facilitate downstream data analysis Figure . SpecifiThese ambitions imply that the constructed groups may contain references to more than one distinct chemical structure. The goal of the constructed database is therefore clearly different from that of public chemical databases such as HMDB and PubChem , which gThe resources that are available for constructing the desired database are chemical reference libraries and external public databases such as KEGG , ChEBI  and the Unfortunately, there are several limitations that make these resources non-trivial to integrate with each other.• The primary identifier that we seek, the identifier group, is not used by any resource.- All resources may list multiple entries for the same metabolite, e.g., separate entries for alanine and L-alanine.• There is no identifier type that is used by all entries in all resources.• Comparing different resources, there may be errors in the sense that the same identifier can be used to refer to different metabolites.The input can be thought of as a large network of identifiers where primary identifiers are linked to other identifiers. A straightforward way of obtaining the groups of identifiers that we seek is to combine all identifiers that are interconnected. However, the last observation above implies that this could also erroneously group strictly different metabolites. To solve this problem we designed a heuristic, which is described in the following section.We reason that the primary key, the identifier group, should correspond to a group of strongly interconnected identifiers. We grow these groups incrementally as new data are imported to our database and merge groups if they overlap strongly with each other; see Figure All input can be broken down into groups of identifiers that associate a primary identifier with a set of associated identifiers. Upon data import, the database that is being constructed is searched for any pre-existing identifier group that overlaps with the incoming group. If an overlapping group is found, it is tested for compatibility with the new group. If the two groups are compatible, they are merged to form a new larger group; if they are not compatible, the overlapping identifiers in the new group are annotated as 'weak' and the rest form a separate group. Weak identifiers are defined as identifiers that may be associated with more than one identifier group. The compound name C is a typical example of a weak identifier as it can refer to either cysteine or cytosine.Because no identifiers are deleted, all original associations found in the input are also present in the resulting database and can be used to query for metabolites.The constructed database may group different chemical structures, and therefore there is no direct way of knowing which identifiers should be connected and which should not. Hence, we resort to a rule set and define two identifier groups as compatible if,1. They come from the same source and share a non-weak identifier.n types of non-weak identifiers, where n is user-defined (default 2).2. They come from different sources but share at least 3. Two identifier groups are not compatible if they do not have the same chemical sum formulas (ignoring single proton differences and derivatized compounds).n implies that correctness is prioritized and only groups of identifiers that are very likely to refer to the same metabolite are merged. A low n on the other hand prioritizes non-redundancy, causing more groups to be merged. When consolidating two databases that only can be linked with a single type of identifier, this threshold should be set to one. When working with very unreliable data sources, it may instead be increased.The threshold in step 2 is included to cope with errors and minor ambiguities in the input. A high The resolved groups of metabolite identifiers are stored in a local database that keeps track of the source of every imported identifier, which identifier group it belongs to and if that association is annotated as weak. The identifier groups have a local primary key that allows for instantaneous conversion between different types of identifiers.The notion of defining certain identifiers as weak also makes it possible to set entire identifier types as weak. This way, annotational identifiers such as pathway information can be imported without causing any identifier groups to be merged.http://metmask.sourceforge.net. The project page also gives access to a web-interface, which can be used to perform identifier conversion and visualization using an example database.The program interface is command line-based, making it easy to integrate into automated data analysis pipelines. Sources and binary distributions can be found at The widely used statistical programming environment R  togetherThe main MetMask program is platform-independent, free, open-source software implemented as a Python package. Identifiers are stored in a local SQLite  databaseDifferent parser modules depending on which source is being imported handle imports to the database. The parsers read different file formats but in essence all perform the same thing: collation of groups of identifiers, annotating and inserting them to the database. The currently distributed parsers are listed in Table Imports can be performed both comprehensively and in synchronization mode in which only identifier groups are imported that already have some overlap in the database.The identifier types KEGG compound ID, CAS Number, KNApSAcK, CQ ID (from MMCD), PubChem Substance ID and Compound ID, InChI, Metlib ID and HMDB ID are matched with regular expressions to ensure that imported identifiers are well formatted.Once a database has been created, minor updates that define new metabolites or add data to already existing metabolite groups may be performed by re-importing the updated source. Larger rearrangements and deletion of identifiers is not supported in the current version of MetMask and must therefore be done by rebuilding the whole database.The MetMask database can be queried in a flexible manner, making it easy to extract both information on single entries and to do batch queries. When input is given via standard input, each line is treated as a query string, and the result is provided as standard output. Full export is also supported in which the requested identifier types are extracted for all identifier groups.Default output is given as a comma delimited table where one row corresponds to one identifier group and each column corresponds to the queried identifier type. Multiple identifiers of the same type and group are separated by the pipe character. This type of output can be imported into spreadsheet programs or read by interpreters such as Python, Perl or R.The associations in the constructed database are annotated with both the original source they came from and whether they are considered useful for identifying a specific metabolite or weak (only provide annotation). The identifier groups can be visualized as graphs by tracing how primary identifiers link to other identifiers in the input sources  and ChEBI . There a.0 and ChIn order to demonstrate our approach, we look at an example data set from a CE-MS-, GC-MS-, and LC-MS-based study measuring metabolite levels in tomato fruits in two ripening stages. The data set was composed of three different data matrices, each annotated with either chemical synonyms or identifiers referring to platform-specific reference libraries. The biological aspects of the experiment are not within the scope of this study and we will only consider it as generic data set coming from three platforms were all preliminary data processing such as peak picking, deconvolution and alignment has been successfully performed.In the following sections we use two reference libraries and public resources to create a MetMask database and then show how it can be used to cross-reference the local identifiers with public identifiers. Finally, we provide two short examples of data analysis techniques that the created identifier database enables. The results presented here are specific to how these particular libraries were constructed, but the main concept will be the same regardless of the utilized platform.First, we import two reference libraries consisting of comma separated text files and a NIST MS export file listing local identifiers and synonyms as well as partial links to publicly used identifiers. Using MetMask, an import into a new database called 'mydb' is performed by,> metmask --import library-one.csv --parser simple --db mydb> metmask --import library-two.txt --parser riken  --db mydbThis import limits the search space of the metabolites in order to obtain a stream-lined database for our libraries. When this import has been performed, we can enrich the created identifier groups with data from other sources by importing those sources in synchronization mode. Augmenting our database with data from ChEBI and KEGG is performed by,> metmask --import chebi --synchronize --db mydb> metmask --import kegg --synchronize   --db mydbThe two commands listed above may take up to 20 minutes but only necessary when building or updating the database.Our experimental identifiers may be cross-referenced by querying the created database. ChEBI identifiers, KEGG IDs and synonyms for our example data set can be extracted by calling,> metmask local-ids.txt --goal chebi, kegg, synonym -Q --db mydbwhich yields17497, c00160, glycolate|glycolic acid|hydroxyacetic acid17794|17050, c00197|c00597, 3-phospho-dl-glycerate|3-phosphoglycerate|<cont..>Other more complex queries such as "all CAS numbers and ChEBI identifiers for the entries associated with KEGG pathway 00500 (starch and sucrose metabolism)" are also straightforward, e.g.,> metmask --query 00500 --table pathway --goal cas, chebi --db mydbThe main, still relatively easy alternative to using MetMask for cross-referencing identifiers is to write a custom script to query a public database. In order to compare our performance with this approach, we created MetMask databases using only the example reference libraries library-one.csv, library-two.txt and the input file local-ids.txt . Identifier groups can be visualized as networks where each source connects its own master identifier to a set of externally used identifiers. An excerpt of the connection graph for alanine is shown in Figure Note that multiple entries from the public databases ChEBI and KEGG have been merged to the same entry since the analyte alanine, DL- (2TMS) can be interpreted as any of the entries L-alanine , D-alanine  or alanine . This merging is an important feature because the resolution of the annotations must match that of the experimental platform and be as non-redundant as possible to avoid statistical bias toward multiply represented metabolites. Gathering all equivalent identifiers also helps to avoid inconsistencies, which may arise when the same metabolite is annotated with slightly different compound names.Performing this kind of identifier grouping is very difficult without combining the result of queries to multiple databases.To avoid creating connections between different metabolites we resorted to a heuristic rule-based approach. Our capability to detect erroneous input is currently limited to ensuring good overlap and matching sum formulas between identifier groups before merging them. Therefore, our accuracy depends largely on correct input. After importing new resources to the database it is recommendable to inspect the output manually to confirm the result. If mistakes are discovered, the graph visualization capability of MetMask provides a way to track down the source of the errors. False associations can then be dropped from the database to avoid future errors. The main alternative to MetMask, writing custom scripts, is not less error-prone, particularly since chemical databases tend to be sparsely connected requiring the use of intermediate identifiers.Identifier unification plays an important role if one combines multiple analytical platforms to obtain better coverage of the metabolome. Different platforms may use different reference libraries, which results in data sets with mixed types of identifiers. In order to obtain a consensus, non-redundant data set, it is crucial that metabolite identifiers are used in a consistent manner. Efficient identifier management is therefore a key technology for multi-platform metabolomics. Current middleware solutions exemplified by BioMart  and BioSAfter the identifiers in the example data set are unified using MetMask, all analytes that correspond to the same metabolite can be extracted and summarized by, e.g., replacing them with their first principal component (PC). In Figure P = 0.0002, false discovery rate = 0.006). This observation is supported by Carrari et al.'s (2006) finding that transcripts for nucleotide conversion enzymes are strongly affected during tomato development [MetMask can also link to databases with biological annotation and thereby facilitate biological interpretation. In Figure elopment .Cross-referencing metabolite identifiers and gathering meta-data are essential technologies for metabolomics data analysis. There are several public databases that contain information on metabolites, but linking these data with the local identifiers and compound names often used in experimental data sets has been very difficult. Here we presented a novel strategy for creating a database that gathers and organizes information about metabolites from both in-house reference libraries and external resources. Our approach uses multiple identifier types in parallel when consolidating databases, thereby avoiding the problem of lacking a widely used identifier scheme. Issues with redundant and missing entries are addressed by importing multiple resources to create a unified identifier database.The MetMask tool provides an implementation of our ideas and can be used to create tailored metabolite mappings with minimum user effort. Efficient handling of identifiers enables data summarization and biological interpretation via contextual analysis such as pathway projections.Project name: metmaskProject home page: http://metmask.sourceforge.netOperating systems: Platform independent (tested on Windows XP and Ubuntu Linux)Programming language: PythonOther requirements: NoneLicense: GNU GPLAny restrictions to use by non-academics: NoneHR initiated the project, designed and implemented the program and wrote the manuscript. MK wrote the manuscript, provided GC-MS data and evaluated the program. AF evaluated and tested the program and wrote the manuscript. FM provided LC-MS data, curated our library of reference compounds and contributed to the graph based visualization approach. KS and MA supervised the project.All authors read and approved the final version.User manual for MetMask. Instructions for installation and usage. Also available by the project webpage.Click here for fileExample reference library - CSV. The comma separated text file based reference library used in the demonstration section.Click here for fileExample reference library - NIST. The NIST MS data export based reference library used in the demonstration section.Click here for fileExample input. The file with local identifiers used in the demonstration section.Click here for file


Breast cancer is one of the most common cancer types. Due to the complexity of this disease, it is important to face its study with an integrated and multilevel approach, from genes, transcripts and proteins to molecular networks, cell populations and tissues. According to the systems biology perspective, the biological functions arise from complex networks: in this context, concepts like molecular pathways, protein-protein interactions (PPIs), mathematical models and ontologies play an important role for dissecting such complexity.http://www.itb.cnr.it/breastcancer.In this work we present the Genes-to-Systems Breast Cancer (G2SBC) Database, a resource which integrates data about genes, transcripts and proteins reported in literature as altered in breast cancer cells. Beside the data integration, we provide an ontology based query system and analysis tools related to intracellular pathways, PPIs, protein structure and systems modelling, in order to facilitate the study of breast cancer using a multilevel perspective. The resource is available at the URL The G2SBC Database represents a systems biology oriented data integration approach devoted to breast cancer. By means of the analysis capabilities provided by the web interface, it is possible to overcome the limits of reductionist resources, enabling predictions that can lead to new experiments. Cancer is a complex disease in which both genomic and environmental factors affect the functioning of the molecular circuits leading to the so-called acquired capabilities of cancer . Due to To realise a multilevel and systems oriented approach about a disease, it is crucial to collect and integrate data stored in several dedicated resources. Currently, this process is characterised by some issues. First, data required to realise this perspective are still sparse on the web: despite some existing databases (such as those developed by the NCBI and the EBI) collect data from several projects, data provided by specific resources dedicated to particular pathologies are not yet integrated and therefore are difficult to exploit. Moreover, the accessible information is by far too heterogeneous: for example, some resources make their content available relying on identifiers that do not match directly. Another issue concerns the relevance of data produced by using high-throughput technologies, which represent a useful source of information and, therefore, are essential in a data integration approach: this is the case, for instance, of protein-protein interactions (PPIs) data, that enable the study of cellular networks structure by means of graph theory approaches. Lastly, even if several mathematical models have been developed in the cancer research field, many of them are not coded in standard languages and thus they are not directly available for simulations. In this systems biology perspective, we chose to focus our research on one of the most common cancer types, the breast cancer, which has a high impact on the population and is studied within our institute .Generic as well as scientifically relevant resources exist concerning this pathology. "Oncomine"  was deveTo fill this gap we developed the Genes-to-Systems Breast Cancer (G2SBC) Database. This resource realises the integration of information concerning molecular components related to breast cancer and the overlying molecular and cellular layers, even providing a series of tools for the analysis of the available data.The G2SBC Database relies on a MySQL server. The database structure follows a data warehouse approach, which consists in collecting and formatting heterogeneous data from different sources, in order to make them accessible by the scientific community through a unified query schema using a web interface. This approach is typical of data integration, while normalised databases, designed to support data integrity, are widely used to maintain primary resources. From the data integration point of view, a series of perl scripts have been developed to retrieve different datasets from remote and sparse data sources. The architecture of the G2SBC Database is illustrated in Figure Data has been retrieved from literature, both relying on a manual curation process and exploiting automated methods for data integration from datasets available on the web ,10-22.Breast cancer genes are annotated by gene symbol, description, aliases and sequences. The list of molecular alterations found in breast cancer spans the genome, transcriptome and proteome layers and comes from both clinical data and cell line experiments. Gene characterisation is enriched with information about the related Single Nucleotide Polymorphisms (SNPs), downloaded from dbSNP . SNPs arGene products have been collected as list of mRNAs sequences and related protein isoforms according to the NCBI RefSeq annotations (NCBI Nucleotide ). Gene ein situ breast cancer, lobular breast cancer, malignant neoplasy, hyperplasia). Each image is associated with information concerning the protein detected on the tissue, its spatial localisation (nuclei or cytoplasm/membrane), some patients clinical information, staining intensity  and quantity of stained cells . Similarly to what has been done in the HPA database, values of the cartesian product of intensity and quantity , , , ..., ) have been mapped to natural numbers belonging to the set {1, 2, ..., 16}. These numbers represent a score for the protein expression, where 1 is associated with  and 16 with . Following this operation it has been possible to calculate average protein expression and to infer protein differential expression between breast cancer tissues and normal tissues as difference between two protein expression scores.Data about proteins include all the identifiers suitable to download the related sequences, functional domains according to InterPro , structuConsidering the molecular systems layer, the G2SBC Database lists the biochemical pathways , all-pairs shortest paths (a shortest path in a graph is the path connecting two vertices such that the sum of the weights of its edges is minimised) and their lengths (the number of edges that belong to the path) have been calculated. Other interesting information related to molecular systems is represented by the association of the breast cancer genes with the cell cycle process , incConsidering the cellular systems level, the database includes data about breast cancer types, tissue images and mathematical models  available in literature, which focus on carcinogenesis, tumour growth and tumour response.Gi =  , that undergo the hierarchically structured terms, represent a crucial instrument to shed light on relationships between biological components, thus performing more accurate queries and even promoting the deduction of new relations. Ontologies have been exploited even to better define the scientific context of the developed resource. Human disease ontology [To provide a standard framework for data integration and to enhance a systemic view of breast cancer information, a rich ontology layer underlies the database structure. Where available, genes are annotated using the GO and biochemical pathways by the KEGG Pathway  ontologyontology , BRENDA ontology  and cellontology  are inclontology , an EBI The web interface of the G2SBC Database (implemented employing PHP and JavaScript languages), provides a series of tools to show and analyse data collected in the resource. An extensive help section is available through the left side menu of the web site and shows detailed examples on how to explore the database content and how to exploit the tools integrated in the resource: use cases are described for illustrating what kind of predictions are allowed by the G2SBC database.query system, that is structured in the molecular components, the molecular systems and the cellular systems level. The second section concerns the data analysis tools available through the web interface. This area includes: the "Neighbourhood density and hubs" and the "Shortest paths" tools, both relying on the PPIs network analysis; the gene-annotation enrichment analysis (GEA); the Blast tool [model oriented section, which involves two aspects. The first one concerns the interaction among cell cycle regulation and breast cancer: due to this connection it is possible to retrieve the breast cancer genes involved in cell cycle control and simulate the associated mathematical models. The second regards the mathematical models related to carcinogenesis, tumour growth and response to treatments.The web site pages are grouped into three main sections, listed in the left side menu. The first group concerns the ast tool , which aAt the molecular components level, it is possible to query the G2SBC Database through gene or protein identifiers, synonyms and descriptions, in order to directly retrieve the breast cancer gene report. In this page users can find the information related to each gene that has been integrated into the database. This data includes gene and gene product identifiers, SNPs, molecular alterations involved in breast cancer, microRNAs, tissue expression, drugs, molecular interactions established by gene products, molecular pathways regulated by the listed protein and cell cycle associated kinetic models.Moreover, it is possible to query the system starting from information about gene products function, represented by the protein domains and the GO molecular functions. An interesting application of this query concerns the knowledge about proteins involved in a key biological function, which is often under aberrant control in cancer conditions: transcription regulation. In fact, through this query, it is possible to list the transcription factors associated with molecular alterations in breast cancer.Another possibility is to query the system considering the spatial localisation. This information includes the knowledge related to the gene localisation within the genome by means of spatial coordinates: in this case, the G2SBC Database provides the interactive visualisation of the genome by means of the UCSC Genome browser . SpatialThe G2SBC Database contains several types of molecular alterations associated with breast cancer. These alterations encompass the genome (mutations and SNPs), the transcriptome (RNA expression level and splicing variations) and the proteome , as shown in Table The list of alterations associated with a particular gene is available in the gene report. Each alteration is annotated through some features - that vary according to the type of molecular alteration - such as the contig number, the direction of gene expression variation (up/down), the experimental method used, the cancer type or the cell line. Lastly, each molecular alteration is reported along with the reference to the paper where its experimental identification has been described.top-down approach, which leads to the "building blocks" starting from the systems. In this context, the user interested in a particular biological process can retrieve the list of genes that may affect its normal activity leading to cancer conditions. An example is the response to lowered oxygen tension, an important process for tumour progression; searching through the keyword "hypoxia" it is possible to understand that the GO BP term "response to hypoxia" includes 30 genes which have at least one evidence of association with breast cancer.Considering the molecular systems layer, it is possible to query the G2SBC Database starting from a specific biochemical pathway name, exploiting data from KEGG, Reactome and GO BP. This system level query follows a For what concerns the information that can be retrieved from cellular and tissue levels, the query system lists a series of breast cancer types  and subtypes . A specific breast cancer type can be selected in order to obtain the list of genes products (i) found expressed in the considered breast cancer type according to the HPA data and/or (ii) associated with molecular alterations identified in the selected breast cancer type.Breast cancer types supported by HPA data are provided with information concerning protein differential expression with respect to the normal tissue, Figure In this context, the multilevel data integration realised by the G2SBC Database provides some insights on the characterisation of the different breast cancer types in terms of genes and biochemical pathways.Other than representing an important instrument for generating new hypotheses and producing novel knowledge , the ontThe G2SBC Database provides a series of tools which can be used to analyse the database content. By means of the sequential use of these tools it is possible to create analysis pipelines, which in some cases can lead to new predictions.G2SBC Database web interface provides two tools that rely on the application of the graph theory to biological networks.The first tool retrieves breast cancer genes that have a particular neighbourhood density in the PPIs network and establish a specific number of interactions. On one hand, a protein with a high neighbourhood density, measurable by means of the so called clustering coefficient, is potentially part of a protein complex  and, morThe second tool concerns the "Shortest paths analysis", which is particularly useful in systems biology since it enables the reconstruction of biochemical pathways (an example of the use of shortest paths for the reconstruction of metabolic networks can be found in ). The G2P value  indicating the probability of obtaining the observed distribution of terms among the whole gene set in a hypergeometric experiment. This measure identifies annotation terms that are significantly enriched in a specific gene set. These terms represent distinctive properties of the gene set and constitute the starting point for further functional characterisation. The GEA tool is integrated in the web interface of G2SBC Database and is available whenever a gene list is obtained. Results can be downloaded in a text file. For instance, by performing an appropriate query on the clustering coefficients and the number of interactions, the user discovers that the protein inhibin, encoded by the gene INHBC, is part of a highly interconnected module of proteins which involves the activins (ACV proteins), as shown in Figure P values and, hence, these terms represent the annotation signature that characterises the gene set.The G2SBC Database provides a tool for GEA. The application finds the set of annotations associated with the input gene set. Each annotation term is coupled with a P value 3.66E-8), "skin morphogenesis" , "skeletal system development" , a series of annotations which suggest more detailed biological roles of DCN. Another case is represented by the gene MTA1 (metastasis associated 1), that is currently annotated with two general GO BP terms: "regulation of transcription, DNA-dependent" and "signal transduction". The analysis of MTA1 protein neighbours shows that it interacts with proteins that regulate "histone deacetylation" , "nucleotide-excision repair, DNA damage removal"  and "chromatin modification" , that are all processes related to the DNA transcription.It is worth noting that the GEA tool is useful whenever the selected breast cancer gene is poorly annotated. In this case, the tool provides a way to study the annotations of its interacting partners and gain some insights, according to the concept of network-based function prediction   are highlighted. By selecting the desired SNP, the web interface lists the IDs of the available PDB structures whose chains contain a model of the wild type sequence associated with the selected allele variant. The user can choose one chain and the complete structure is automatically downloaded from the PDB web site. Starting from this PDB structure, the polymorphic model is created using an automatic approach based on a customised python script from Modeller . It replThis tool provides a statistical approach to tissue-related information. Tissue data maintained in the database, which allows vertical integration among proteomic and cellular levels, are analysed by considering the association between genes and breast cancer types. By means of the data collected in the HPA, we calculated the frequency of detection of a specific gene product among the breast cancers types, in order to show its distribution over them. This content is shown in a dedicated web page reachable from the gene report, where the list of the antibodies exploited to detect the specific gene product is provided, together with a pie-chart plot which visualises the calculated percentage distribution.Since the cell cycle process is often affected by misregulations that can lead to cancer onset , the G2SMoreover, some mathematical models related to breast cancer development and response to treatment have been encoded in the Systems Biology Markup Language (SBML)  in orderThe G2SBC Database is a freely available resource developed with the aim of supporting research on breast cancer in a systems biology context. Using data integration a large amount of records have been collected in this database: therefore, enhanced query solutions and web tools are provided to infer non trivial knowledge, e.g. the proteins differentially expressed in each breast cancer type. The contents and tools provided encompass molecular components, molecular systems and cell systems layers. The G2SBC Database provides systems level queries (enabling a top down approach), tools based on PPIs network (e.g. the shortest paths) and a mathematical models section. Due to these features, it is possible to overcome the limits of a resource dedicated only to data exploration, enabling predictions that may induce new experiments.M. musculus and R. norvegicus, making cross-species comparisons possible.The G2SBC Database will be periodically updated according to the publication of new research, biological data and mathematical models about breast cancer. We plan to integrate data from other sources, e.g. the Human Protein Reference Database (HPRD)  , and tohttp://www.itb.cnr.it/breastcancer.The G2SBC Database is freely available at the URL EM implemented the programs for data integration, PPIs network analysis, developed the web interface and the database; RA implemented the database, the web interface; IM implemented the SNP-related contents, developed the sequence and structure retrieving system and provided a complete overview on the work; FV created the ontological layer, the tissue contents and the molecular alterations section; AC contributed to data integration development; LM managed and directed the work. All authors read and approved the final manuscript.