Biological pathways are a useful abstraction of biological concepts, and software tools to deal with pathway diagrams can help biological research. PathVisio is a new visualization tool for biological pathways that mimics the popular GenMAPP tool with a completely new Java implementation that allows better integration with other open source projects. The GenMAPP MAPP file format is replaced by GPML, a new XML file format that provides seamless exchange of graphical pathway information among multiple programs..PathVisio can be combined with other bioinformatics tools to open up three possible uses: visual compilation of biological knowledge, interpretation of high-throughput expression datasets, and computational augmentation of pathways with interaction information. PathVisio is open source software and available at PathVisio is a graphical editor for biological pathways, with flexibility and ease of use as primary goals. The concerted actions of genes, proteins, and metabolites are often conceptualized as pathway diagrams. Pathways represent a familiar concept in biological research, and software designed to work with pathways can help the researcher to organize information related to research questions. Here we present PathVisio, a visualization tool for managing biological pathways, and show several ways in which this tool can facilitate the process of doing research.It is often said that an image is worth a thousand words, and this is especially true for describing complex interactions among biomolecules. Pathways are commonly used with great effect as teaching aides in textbooks, as notes in lab journals, and as explanatory slides in research presentations. However, a pathway that is drawn in a notebook or presentation software is just a static image. The usefulness of a pathway could be increased dramatically if the software knows something about the biological entities it represents. For example, one could click on entities of a pathway to view the Ensembl page of a relevant gene in a browser window. Ideally, textbook pathways could be combined or compared with other versions of the pathway and stored in an online repository. New pathway information could be compiled from the latest experiments and discoveries. Large experimental datasets would be more understandable through pathways. Clearly, user-friendly software would be helpful for dealing with biological pathways.A new program, PathVisio, is based on many design principles derived from GenMAPP, a populPathVisio improves GenMAPP in four separate areas. First, PathVisio is written in the Java programming language as opposed to Visual Basic. Thus, PathVisio is cross-platform, easier to integrate with other scientific software (often written in Java), and works with web technologies such as Java applets and Java Webstart. PathVisio already integrates well with some other Java-based scientific tools such as Cytoscape for netwThe most important aspect of GenMAPP that has been mimicked in PathVisio is that the software places the biologist at the center. PathVisio works best in the hands of experimental biologists with a high level of domain knowledge. We aimed to avoid the situation where the software is so difficult to use that a specialized bioinformatician is needed. We want to gather pathway knowledge directly from the biologists who conceptualize pathways when designing and performing their experiments. This was our main guideline during the software design process. For example, we chose to use manual instead of automatic layout, to emulate presentation software that may be already familiar to the user. We chose locally installable synonym databases to make cross-referencing gene identifiers quick and automatic.As noted above, the data model of PathVisio is completely separated from the rest of the application. It consists of two parts: the pathway data model and the synonym database model.DataNode objects represent biological entities, Line objects represent various types of interactions, and Shape objects serve as graphical annotation. The term DataNode is similar to the GenMAPP term GeneProduct, but we chose to use the former to show that it can be used to refer to any type of biological entity, not just genes and proteins. DataNodes can be grouped to show that they are biologically related .In the pathway data model Figure , there aTo store this pathway model, we developed GPML or GenMAPP Pathway Markup Language. GPML is backwards compatible with the GenMAPP MAPP format, meaning that all information stored in MAPP format can be stored in GPML as well, and pathways can be readily converted. GPML has a set of extensions on top of the initial requirement of compatibility. It allows storing relations among different elements, so that a graph can be derived from a pathway. A pathway using this facility can be converted into a network, something that is impossible with the MAPP format. The utility of this feature will be discussed below. It also can link to BioPAX ,6. BioPAThe second part of the model used in PathVisio is the synonym database model. A variety of online genome databases are available to the bioinformatics community, leading to multiple identifiers for the same gene. One solution to this problem would have been to standardize on a specific database and let users make use of external services such as DAVID  to transSynonym databases  can be downloaded from the website pathvisio.org. Because this type of database is potentially used intensively, we chose to create locally installable versions rather than relying on a slow web-service. The synonym database schema Figure  consistsSynonym databases that we produce are based on Ensembl  and can For viewing pathways, we chose an implementation based on the Java Graphics2D API, which makes it possible to output to screen as well as various file types. The flexibility of this API makes it trivial to add Shape types in the future. The Batik SVG Toolkit  is used We envision three ways in which PathVisio could aid biologists doing research.Many research questions are related to biological pathways in some way. For example, which receptor is responsible for carrying a stress signal across the nuclear membrane? Which proteins need to be activated to lead to a choice between two possible differentiated cell types? In these cases, a question is asked about a certain unknown component of a biological pathway. Experimental results could lead to a conclusion in terms of adding new elements to a pathway, clarifying the role of an element in a pathway, or proving the existence of an interaction between two elements. In all cases, biological knowledge is increased, and since pathways are a representation of this knowledge, the pathway itself is improved as a direct result of research. Expressing biological knowledge visually as a pathway can be a very powerful tool to organize disparate bits of information.What is the best way to represent biological concepts graphically? There are certain conventions for this, as well as several published formalized symbolic languages -16. The Molecular Interaction Maps (MIMs)  is one aThe current version of PathVisio (version 1.1) has some support for MIMs; many of the needed graphical elements can be drawn. Work is underway to make this support complete, including making complex aspects of MIMs, such as contingency arrows, available in a user-friendly manner Figure . PathVisKitano  proposedBiological knowledge stored in a collection of pathways is most useful if it is available online and frequently updated. An applet version of PathVisio is used for the WikiPathways ,19 resouData from high-throughput experiments such as microarrays can be combined with pathways to achieve new insights. Using pathways one can view data in its biological context rather than in an arbitrarily ordered table. Once a set of pathways have been collected in a repository, the possibility to do pathway statistics becomes available.PathVisio supports this workflow in combination with the GenMAPP software suite. Pathways created with PathVisio can be exported to MAPP format. Subsequently, high-throughput experimental data can be loaded into GenMAPP, and user-defined color criteria established. DataNodes on pathways in GenMAPP can then be colored accordingly, putting relevant parts of the expression dataset together in a single view.MAPPFinder , anotherEu.Gene can provide an alternative to MAPPFinder in circumstances where something other than z-scores is required. PathVisio can export pathways to the Eu.Gene gene list format. Eu.Gene can employ Gene Set Enrichment Analysis (GSEA) or Fisher exact tests for pathway statistics. We also assisted the Eu.Gene development team to allow direct import of GPML and visualization of pathways. These features will be available in the next Eu.Gene releaseCompilation of pathway information is necessarily a manual process, but it would be very useful to augment pathway information with computational tools, including text mining, data mining and interaction information from high-throughput datasets, such as yeast-two-hybrid data.As an improvement on GenMAPP, GPML has been extended to store node-edge relations, making it possible to store true interactions. PathVisio allows the user to define interactions by joining two items with a connector line. Fore ease of use, the connector moves together with the element to which it is connected.The user-interface of PathVisio is optimized for the pathway model, meaning that PathVisio does not treat pathways as networks. The two concepts are closely related however, and network analysis of pathways can be useful. Other software, such as Cytoscape , is bettFor a specialized tool such as PathVisio to be relevant, it must be tightly integrated with other bioinformatics tools and standards. PathVisio can already be used in combination with Cytoscape through the GPML plug-in, and it is compatible with GenMAPP MAPP format and MAPPFinder for statistical analysis.There currently exists a set of partly overlapping pathway format standards , and in To clarify the role of GPML, we can compare it to other existing standards related to pathway definitions. BioPAX is a standard designed for exchanging data between pathway databases. GPML can embed elements from a BioPAX document and add visual annotations to it. This capability could facilitate integration of PathVisio with other pathway sources, such as Reactome, in the future. GPML is not suitable for computational modelling. Systems Biology Markup Language (SBML) is designed specifically for that purpose, and the overlap in scope of GPML and SBML is small. In the context of applications like CellDesigner , SBML dePathVisio is fully open source, and GPML is an open format. We see open source as a necessity for this type of bioinformatics tool. Open source makes it possible for other tools to adapt to PathVisio and vice versa. With closed-source tools , this adaptation can only go one way, which is a strong disincentive to collaborate. In a field where integration is of utmost importance, open-source software provides an optimal solution. To further encourage cooperative development, PathViso supports the addition of plug-ins, something that is facilitated by the Java programming language.With PathVisio and GPML we have developed a framework for visual pathway analysis. This framework is very flexible with future extensions in mind. Development of PathVisio is ongoing. We wish to continue in the direction of increased flexibility and tighter integration with other bioinformatics standards and applications. This should ensure that pathway analysis and visualization can be done efficiently to improve biological research.Project Name: PathVisioProject Home Page: Operating System: cross-platform. PathVisio has been tested on Windows XP, Ubuntu Linux 8.04 and Mac OS X 10.5.Programming Language: JavaOther Requirements: Java 5 or higherLicense: Free and open source under the Apache 2.0 License. There are no restrictions to use by non-academics. The source code is available at All authors have read and agreed upon the content of this article. The PathVisio software was designed and written by MvI, TK, AP and KH. SC performed invaluable beta testing. AP and BC created the GPML concept, CE the PathVisio concept. MvI and SC drafted the paper.Krebs cycle pathway in GPML format. Rendering of the Krebs cycle in GPML format, derived from the GenMAPP archive. This pathway is shown in figure Click here for fileDNA replication pathway in MIM Style in GPML format. Rendering of the DNA replication pathway using the MIM style, derived from Aladjem et al[jem et al Part of Click here for file


Scientific studies to investigate Chinese medicine with Western medicine have been generating a large amount of data to be shared preferably under a global data standard. This article provides an overview of Semantic Web and identifies some representative Semantic Web applications in Chinese medicine. Semantic Web is proposed as a standard for representing Chinese medicine data and facilitating their integration with Western medicine data. As the scientific evidence for the preventive and therapeutic efficacy of Chinese medicine (CM) is growing, it is strongly demanded to bridge CM with Western medicine (WM), particularly through the data obtained from biomedical and clinical research. For example, there were acupuncture studies on certain diseases/disorders such as chronic pain ,2 and cei) TCMGeneDIT  is a datii) Phytochemical databases of Chinese herbal constituents were constructed ;http://clinicaltrials.gov/ contains information of a large collection of clinical trials including those that involve CM;iii) ClinicalTrials http://www.nlm.nih.gov/medlineplus/[iv) MedlinePlus lineplus/ developehttp://cowork.cintcm.com/engine/windex1.jsp consists of over 40 categories of CM Databases such as the Traditional Chinese Medical Literature Analysis and Retrieval Database, Clinical Medicine Database, Traditional Chinese Drug Database, Database of Chinese Medical Formula, Traditional Chinese Medicine Enterprises and Productions Database, and State Standards Database.v) TCM Online Data mining and integration of CM and WM databases are of great value but problematic ,11. Datahttp://www.w3.org/Addressing/ and Resource Description Framework (RDF) http://www.w3.org/RDF/, which are the Semantic Web standards for data identification and data representation respectively. The RDF provides a "triple" format for representing a statement that consists of a subject, property and object. Each component of the triple is identified by a URI that serves as a global unique identifier for the Web. For example, the following triple (statement) asserts that an herb-derived drug "Huperzine A" (subject) "inhibit" (property) "NMDA receptor" (object).Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the Web are defined, making it possible for the Web to "understand" and answer queries in accordance with the Web content. SW's enabling technologies include the Uniform Resource Identifier (URI) Subject -- http://en.wikipedia.org/wiki/Huperzine_AProperty -- http://en.wikipedia.org/wiki/inhibitObject -- http://en.wikipedia.org/wiki/NMDA_Receptorhttp://www.w3.org/TR/rdf-sparql-query/, which is the RDF query language standard. SPARQL is analogous to SQL http://en.wikipedia.org/wiki/SQL for querying relational databases.The above example demonstrates that the Wikipedia URIs are used to identify and define the subject, property and object (this is only for demonstration purposes). The statement indicates an "inhibitory" effect of the drug "Huperzine A" on "NMDA Receptor" (drug target). A collection of linked RDF statements forms a directed acyclic graph (DAG). Such collections of statements represent the knowledge of a domain. To query and manipulate RDF statements, we may use "SPARQL" http://www.w3.org/TR/rdf-schema/ and the Web Ontology Language (OWL) http://www.w3.org/TR/owl-features/ have been used to encode ontologies in the biomedical domains [rdfs:Class construct to declare a resource as a class, e.g. Herb. A hierarchy of classes can be defined using the rdfs:subClassOf construct. For example, "Huperzia serrata" is a subclass of "Herb". Most of the RDFS components are included OWL, which is more expressive than RDFS. OWL has the built-in property owl:sameAs that allows a synonymous relationship between two classes (e.g. "Huperzine A" and "Huperzia serrata"). Cardinality constraints can be applied to properties . While OWL is semantically richer than RDF or RDFS, it can be expressed using the RDF syntax. OWL reasoners such as Pellet [To capture richer data semantics to support computational inference and reasoning, the RDF Schema (RDFS)  domains ,14. RDFSs Pellet  and Races Pellet  can be uhttp://www.w3.org/2001/sw/hcls/. The group has been chartered to develop, adopt, and support the use of Semantic Web technologies and practices to improve collaboration, research and development in health care and the life sciences.Adoption of the Semantic Web has been significantly important to health care and life sciences. In part, the adoption has been driven by the World Wide Web Consortium (W3C), which launched the Semantic Web for Health Care and Life Sciences Interest Group (HCLS IG) http://virtuoso.openlinksw.com/, Oracle http://www.oracle.com/technology/tech/semantic_technologies, AllegroGraph http://agraph.franz.com/allegrograph/, and Sesame http://www.openrdf.org/. While some of these technologies (e.g. Oracle and Virtuoso) are proprietary, others (e.g. Sesame) are open source. Some of them  support SPARQL, but some others (e.g. Oracle) have their own RDF query languages. To provide a uniform query access, many triplestores provide a so-called "SPARQL endpoint" so that queries can be issued by client programs against the triplestores via the SPARQL language. For example, even though Oracle does not support SPARQL internally, it can be configured to provide an external SPARQL endpoint through the Jena adaptor http://www.oracle.com/technology/tech/semantic_technologies/htdocs/documentation.html. Triplestores such as Oracle provide their own native OWL reasoners, while some others  can be integrated with external reasoners.As RDF/OWL-formatted datasets are growing in terms of the number and size , efficient data storage and manipulation become big issues. To this end, a variety of triplestore technologies have emerged, including Virtuoso http://wiki.dbpedia.org/OnlineAccess, are listed on Linking Open Data (LOD) http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData. A similar effort has been launched by the Linking Open Drug Data task force of the HCLS IG to use the linked data approach to link drug-related data.Linked Data  is a newAs the relational database technology is prevalent in the health care and life science domains, many of the CM databases are currently in the relational format. While these relational databases serve the specific needs of individual labs or institutions, their accessibility by other labs or institutions is limited. An object or data record is identified by a unique identifier (primary key) that is local to the database. In other words, the same identifier does not identify the same object (data records) in different relational databases. Another issue with the relational databases is that relationships are defined based on links between primary and foreign keys. These links are not to convey some meaning semantically. Semantic Web can be used to address this problem by allowing a semantic layer to be created on top of existing relational databases. Semantically rich queries (based on meaningful relationship names) can be formulated at the semantic layer (built using the Semantic Web technology) and then be mapped to the local queries against the underlying relational databases. DartGrid  is a sysAs data are increasingly available in RDF/OWL format, new warehouses and federated query systems have been built from scratch using Semantic Web technologies to allow direct access by programs. As part of the HCLS IG effort, a subset of TCMGeneDIT was converted into RDF format and loaded into an RDF triplestore . In addiOntologies encoded by Semantic Web enable expressive knowledge representation, integration, and discovery. Ontology research is active in the biomedical informatics community. Examples include the OBO Foundry  and BioPin silico research.Some information technologies such as text mining, Grid computing, and Web services have been using the Semantic Web. These technologies combined with the Semantic Web can further empower CM researchers to carry out Re  in CM may be referred to as high fever and irritability in WM. The theories behind WM and various CM can be fundamentally different, leading to the difficulty to make alignments among their domain ontologies. For example, CM practitioners interpret human body and organs based on Chinese philosophical ideas of "yin-yang" and "five-elements". They are aware of the efficacy of the herb, Huperzia serrata (HS), in aging disorders, and interpret the action mechanism of this herb as strengthening the Shen (kidney). Biomedical scientists analyze some experimental evidence, and deduce that a compound of the herb HS acting on the brain can serve as a potential therapy for the Alzheimer's disease. In this case, HS targets the brain (WM) instead of the Shen (kidney).Given the long history of CM, most of the CM documents were written in Chinese. While the Web is multilingual, a simple literal translation, however, is not sufficient in terms of making the CM knowledge accessible by Western researchers. An example is the translation of signs and symptoms between CM and WM. For example, the term These language gaps limit the communication and interaction between WM and CM in both directions. On the one hand, scientific communities have not reached the full potential of utilizing CM knowledge. On the other hand, best practices of WM are not widely adopted in the regions where CM is predominant form of healthcare service. To bridge these gaps, we need to establish an infrastructure that can support communication and collaboration in integrative medicine studies. The infrastructure should also be able to capture and publish the results of these integrative medicine studies to extend the actionable knowledge shared among communities.http://www.tcmedicine.org/ was formed to promote data sharing as well as collaboration among academia, industry and regulatory agencies in various countries.Data sharing is a key to advancing science in the digital age . For exaWhile the Semantic Web is a candidate for standardizing the format of CM data sharing, it needs to be used in conjunction with other standardization efforts that are underway in the CM community, e.g. the regulatory standards for quality control of Chinese medicinal materials . This alThere is a broad spectrum of international Semantic Web research related to the health care and life sciences. Semantic Web research effects in CM are mainly in Asia. It would be beneficial to integrate CM into these international activities. More use cases are needed to demonstrate how the Semantic Web can be used to harmonize CM and WM through data linking and integration as well as community collaboration.As the interest of using Semantic Web in the health care and life sciences is growing, it has the potential to facilitate cross-disciplinary data integration between Chinese Medicine and Western Medicine. The Semantic Web could potentially play an important role in Chinese medicine informatics involving a new breed of informaticians who are able to bridge multiple scientific and cultural disciplines.The authors declare that they have no competing interests.Both authors took part in the discussion and writing of this article. They also read and approved the final version of the manuscript.


Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server.MetNetAPI is a versatile Application Programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive . Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only.http://www.metnetonline.org/api.An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, Microsoft.NET and R programming environments and offers flexible query and broad data- retrieval methods. Data retrieval can be customized to client needs and the API offers a framework to construct and manipulate user-defined networks. The design principles can be used as a template to build programmable interfaces for other biological databases. The API software and tutorials are available at  Analysis of the topology of biological networks provides understanding of structure, function and interaction among cellular entities . As knowThe MetNet systems biology platform is a suite of software programs that model metabolic and regulatory pathways in plants . At its http://www.metnetonline.org, users control various network resources including 1) the ability to bookmark pathways, 2) tracking user-defined components of interest, and 3) a localization datalayer. Users can create new pathways by combining and modifying existing pathways and then save the new pathways. Using the website, pathways can be exported to SBML  or XGMMDesigner ). All thAn API allows data to be approached and viewed in several modi. Unlike statically-exported files such as data dumps and standardized schema which offer only a single view of the data, whereas an API enables much more user customization, such that a researcher can view or computationally manipulate the data in multiple ways. Consider the static SBML BioModels dataset, wherein each file represents a single pathway . Assume Several options exist to share information contained in a biological database. One option for transfer of database content is a data dump. This exposes all the information contained in the database. However, it may require significant effort to understand (and possibly reconstruct) the original database schema.A second option is to support a standard data format. Chado and BioSQL are two examples of standardized data schema specific to sequence databases ; BioPax,http://jax-ws.dev.java.net/, Axis http://ws.apache.org/axis/ or XFire http://xfire.codehaus.org/), it is far from efficient. REST-based webservices are somewhat less cumbersome in this regard , but they have their own peculiarities: Every resource needs to be accessible through a unique URI. This means that information is represented in a hierarchy, which can become complicated very quickly and cumbersome to browse. It is possible, however, to circumvent this problem by allowing querying of the dataset at a different location on the website. The URL to a REST-resource is then a query-string in its own right. While the messaging protocol involves less overhead than its SOAP-counterpart, the lack of required message meta-data makes these environments at the same time less intuitive and harder to query for complex data. Reactome is one such pathway database [A third option is providing an Application Programming Interface (API). A major advantage is that content and functionality are combined . Tying adatabase  that supdatabase . Doodle database , while Gdatabase , WikiPatdatabase  and CPDBdatabase  choose tIf functionality is added to the webservice (either REST or SOAP), supplemental resources - CPU, memory, hard disk - for the server hosting the service must be considered. Webservices therefore seem to be destined to either offer limited functionality (and thus be less useful), or offer extensive functionality but artificially limit access to them because no institution can gather unlimited bandwidth and resources to serve the world. MetNetAPI offers close proximity (strong datatyping) to the MetNetDB database and underlying model, while still able to provide flexibility and abstraction in regard to biological information content. Processing of information mostly occurs on the client running the API, which results in a more distributed load. This presents opportunities to better plan (and distribute) resources across various projects.MetNetAPI is designed as an object model that abstracts and encapsulates the data in the underlying MetNetDB repository. We chose Java, R and Microsoft.NET as target programming languages because they are platform independent and are widely in use today. Users do not need to understand the internal intricacies of the back-end database model. The goal is to hide complex data modelling techniques and allow the bioinformatics software developer (and by extension the biologist) to get started using novel integrated datasets quickly. Overhead is kept to a minimum, as there is no WSDL-file to be parsed, as with SOAP. The structure of the information in MetNetAPI is exposed intuitively through Java reflection mechanisms that are provided in most development environments.MetNetAPI is a Java jar-file (or .NET Assembly) which contains several logically-ordered namespaces (abstract containers that express semantic categories of code). The main namespace is edu.iastate.metnet (Edu.Iastate.Metnet in .NET). Underlying namespaces and classes allow reasoning by type and allow a programmer to bring biological semantics into the program code. This is in contrast with many other APIs, which result in generic Dictionary-objects, which still require further interpretation and parsing after retrieval. The same argument applies to webservices , where the returned output is text-based that requires further processing.http://www.hibernate.org, we adapted Just In Time (JIT) compilation for data retrieval. When retrieving a pathway, only the main data is obtained from the database. Information represented in linked tables (one-to-many or many-to-many) is retrieved when the respective methods are invoked. This occurs transparently, so a client application should function optimally and use a minimal footprint whether retrieving a list of "all pathways", or constructing an integrated network from "all amino-acid biosynthesis-related reactions". The JIT data retrieval mechanism not only encapsulates a complex data model, it also makes retrieval and reconstruction of network data efficient. This behaviour is impossible to implement through webservices, as the server cannot "guess" what clients want to do with a returned piece of information in the future. One option would be to provide a verbose-like parameter when calling the webservice, which introduces additional overhead for the programmer consuming the service. Another option would be that the server assumes a worst-case scenario and streams all available  information back to the client, leading to increased (and possibly unnecessary) server-load and network traffic.Querying of MetNetDB through MetNetAPI is optimized for efficient memory use. Similar to the Lazy Load concept in the Java persistence library Hibernate http://www.metnetonline.org[MetNetAPI is a flexible API that interacts with and retrieves data from MetNet, an established information resource and suite of software applications for model organisms, currently including Arabidopsis, soybean and grapevine nline.org. By acceThe API allows a software developer to navigate the database from multiple points of view, without having to understand the underlying database schema. The database can be navigated either as a list of pathways, a list of entities or a collection of organism-centric networks. In contrast, static data files allow only one such point of view and require customized parsing to determine the answers to specialized questions. Examples of user queries would include "which elements in a list of entities participate in at least two pathways" or "for a given collection of pathways, single out and reconstruct a regulatory network". MetNetAPI can answer such queries without extensive programming for any respective list of entities . The API approach allows a database platform to abstract and expose its repository data, along with its functionalities.The MetNetAPI is designed to capture MetNet architecture, which centers around four central classes:Entity represents any type of molecular entity that can be found in a biological environment. Entities have a general categorical descriptor that describes the type of an entity, such as "gene", "RNA" or "Protein Complex". They can be organism-specific (in the case of a gene) or not .An LocalEntity represents a particular entity found within a sub cellular location. An example is the molecule (Entity) ATP, which is found in several compartments (locations) in the cell, including mitochondrion, nucleus, plastid, and cytosol. Therefore, the Entity ATP has four associated LocalEntities.A Interaction represents the impacts or transformations among entities. Due to the diversity and generalization of the Entity class, Interactions are kept equally generic. Like entities, they are classified. Interactions include enzymatic reactions, transport, transcription, translation, and various classes of regulatory inhibition and activation such as allosteric effector or indirect positive regulation.An Pathway represents a group of multiple interactions and the associated biomolecules organized into a convenient functional unit. The pathway concept in MetNetAPI is defined as an unordered collection of Interaction objects. In order to allow developers to determine the start- and end-points of a pathway, getSources and getSinks methods are provided.A Organism class represents information about organisms currently in MetNetDB. EntityType and InteractionType represent the different types of respective entities and interactions. PathwayClass provides a Pathway Ontology to navigate through the collection of all pathways, which is based on AraCyc pathway classes. CellLocation provides a similar hierarchy that can be used as an alternate pathway ordering tree.Peripheral classes are provided to further define pathways and represent MetNet-specific data. The Pathways are arbitrary groupings of interactions. Even for well-defined pathways such as glycolysis and TCA cycle, different views can be created, which may or may not include the genes and the transcriptional and regulatory framework of the various enzymes involved. As more knowledge is acquired through scientific experimentation, pathways may become so complex that it is beneficial to break them into smaller units for some applications. Conversely, smaller pathways may be joined into a larger unit or a super-pathway for meta-analysis.Network class is provided. It serves the purpose of providing custom granularity. A Network object consists of a custom collection of interactions. A Network incorporates the concept of a pathway, yet it is not confined to the boundaries of a predefined pathway. Networks can be constructed either by combining existing pathways or by adding individual interactions.To model these evolving datasets, a Several APIs offer top-down approaches to network data. An example is libSBML, in which a pathway consists of reactions, which consist of molecular species . It is cMost all network database websites have a search-function. Upon downloading files for offline use, the online functionality is no longer available. This means that a data dump does not always offer the correct amount of information one is interested in. Much effort needs to be invested in study of the original data format and writing parser code to extract the information of interest.Through MetNetAPI, online search-capabilities are extended and can be integrated in desktop and other applications . This makes it convenient to execute a large number of queries against MetNet. The investigator can automatically determine which pathways a given list of metabolites participates in, restrict a pathway to its regulatory interactions, or request a list of affected pathways for a set of up-regulated genes. Most Java-classes in the MetNetAPI library have a static search method, which allows developers to launch queries against MetNetDB in real time, without having to go to a website, fill out a form and submit it.Filtering using MetNetAPI is similar to searching, but zeros in on results within results. For example, a user could extract all gene regulatory interactions from a previously defined set of pathways (combined as a Network object). Alternatively, a user could look at a complex pathway with 100+ interactions, and decide to remove temporary clutter caused by transcriptional and translational events. The resulting "core" pathway makes it easier to understand the metabolic functions performed by the pathway.The availability of a dynamic code-driven class hierarchy instead of a collection of static, rigid files allows developers to rapidly provide MetNet data and bring its functionality to their own applications. MetNetAPI is object-oriented, which allows for code to be mixed with data (methods and properties). When a collection of pathways is represented by a PathwayVector object, functions to manipulate the member objects are provided. This is preferable to the use of rigid files, or the passing back and forth of Dictionary-like structures.http://www.graphviz.org .dot-file, details of which are shown in Figure Source code is provided [Additional file MetNetAPI exports data to standard data formats such as SBML or XGMML (used in Cytoscape). This functionality is available for developers that wish to exploit the richness of MetNetDB. It also allows integration of MetNet-originated data into a more expansive research pipeline. The Network class contains a set of methods that allow export to a variety of standards. To ensure compatibility with a wide spectrum of software, the depth of information has been restricted to a minimum. So, while the Network class is recommended to prepare data for external software such as Jarnac (SBML) or Cytoscape (XGMML), specialized needs would require a developer to generate customized export-routines.MetNetAPI facilitates the creation of static files based on dynamic actions. An example would be to gather the 5 pathways in the database that describe the metabolism and signalling associated with the plant hormones brassinosteroids and auxins into a single Network object, and to export this network to a single XGMML file. This file can be directly imported into Cytoscape to enable visualization and further analysis of a user-specified unit of biology .http://www.metnetonline.org/api/cytoscape/ and source code is available upon request so its functionality may be extended.Several proof-of-concept applications using MetNetAPI have already been developed: We have developed the MetNetScape plugin to allow a user to select an organism and pathway to be imported into Cytoscape. An example of an imported pathway is shown in Figure A more complex plugin has been developed for CellDesigner  to allowMetaOmGraph (MOG) is an application to display large expression datasets ,29. Subshttp://www.caida.org/tools/visualization/walrus/ is a desktop-application to visualize 3D-data. A proof-of-concept application has been developed that enables a user visualize MetNet pathways in 3D on a standard computer [Large biological networks often benefit from visualization in 3D . Walrus computer . The appMetNetGE  is an enWe have adopted the API as a method to standardize development of applications that exploit the MetNetDB dataset. In addition to facilitating prototyping and rapid application development, this approach ensures consistency across end-user interfaces, command line interfaces, and graphical user interfaces. MetNetAPI is flexible and can be modified, based on needs of internal and external software developers.http://www.r-project.org through the rJava bridging software http://www.rforge.net/rJava/.We are exploring the possibilities of using the API in environments other than Java. This has already lead to integration of MetNetAPI into Microsoft .NET and R Advanced programming knowledge (such as SQL or JDBC) is not required for using MetNetAPI. The complexity of the underlying data model is encapsulated within the API. The interface is only slightly less universal than the socket-based protocol provided by BioCyc , and theOnline databases often provide data export by means of static downloadable files or dynamic webservices. MetNetAPI provides an additional approach to data export. The API provides a method to standardize development of applications that exploit MetNetDB, but may also serve as a framework and template for other pathway databases. A standardization of terminology among different databases would certainly benefit developers that work on integrative applications. Many databases expose similar types of data, and the definition of a minimal set of interfaces that pathway database APIs may be expected to implement would be helpful. MetNetAPI can be a first step in this direction.Apart from facilitating prototyping and rapid application development, our approach ensures consistency and data integrity across command line interfaces and graphical user interfaces alike. The choice of Java and Microsoft.NET allows the API to be used by a broad audience of software developers and bioinformaticists. The complexity of the underlying data model is encapsulated within the API. Because it is a Java-API rather than a webservice, more functionality can be provided without requiring extensive computational resources on the server-side.Network objects, combinations of existing or putative novel pathways can easily be constructed, manipulated and refined. MetNet is an information resource, as well as an active toolkit to develop new hypotheses. Many complicated operations, which would be difficult to implement via xml or text-based files, can be accomplished through MetNetAPI. These feature flexible capabilities to agglomerate data over multiple pathways, to examine connectivity among different datatypes, and prepare custom datasets for use in other downstream applications. MetNetAPI is fully documented, free of charge and can be downloaded from http://www.metnetonline.org/api/cytoscape/.For a densely populated and information-rich database (such as MetNetDB), our API model offers many advantages. It has the ability to incorporate online search capabilities into custom-built applications. It also offers the option to customize the granularity of pathways of interest. MetNetAPI captures user-defined network structures into self-contained semantic objects. Through Project name: MetNetAPIhttp://www.metnetonline.org/apiProject home page: Operating system(s): Platform independentProgramming language: Java, Microsoft.NET, ROther requirements:Java-specific: Java 1.3.1 or higher, MySQL JDBC driverMicrosoft.NET-specific: Microsoft.NET 1.1 or higher, MySQL .NET driverLicense: NoneAny restrictions to use by non-academics: NoAPI: Application Programming Interface; CSV file: comma-separated values file; KEGG: Kyoto Encyclopaedia of Genes and Genomes; MOG: MetaOmGraph; REST: REpresentational State Transfer; SBML: Systems Biology Markup Language; SQL: Structured Query Language; SOAP: Simple Object Access Protocol; URI: Uniform Resource Identifier; WSDL: WebService Description Language; XGMML: eXtensible Graph Markup and Modeling Language.The authors declare that they have no competing interests.YS carried out API design and development. ESW conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.Sample program code to generate an inter-pathway map. Sample program that creates a distance matrix between a set of pathways. The distance is based on the number of genes that are shared between two pathways. Output is sent to a .csv-file as well as a GraphViz-compatible .dot-file. A rendering of the .dot-file is provided as a separate Supplemental file.Click here for fileInter-pathway map based on shared genes between pathways. A complete rendering of the distance matrix that was computed with the sample program provided in distance.java.Click here for file


Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information.Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach.This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information  from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database. Although there has been a trend whereby many researchers widely use microarray technologies, less is done computationally to interpret and validate biological hypotheses formulated from inherent investigation results. Continued microarray data deposit and revision of genome annotations are important to supplement previously submitted microarray metadata. While the advent of microarray technologies and an increasing number of analysis methods present an opportunity to better understand life mechanisms, exploitation of microarray data and the choice of analysis methods remain challenges. The idea behind the development of PathEx originates from a benchmarking study we conducted comparing microarray statistical analysis methods . During At this level, the challenges include (a) how to effectively and more easily obtain a dataset with the number of replicates necessary for the analysis method chosen and (b) how to select a dataset for a specific purpose  to increase the statistical power of the analysis method. One way to effectively meet these needs would be to consider re-using previously deposited microarray data from the same or different studies  without necessarily conducting new experiments.We propose here a novel web tool that combines information from microarray data, the literature and omics technologies. Its main objective is to allow for instantaneous selection and generation of datasets of interest by drawing relevant samples files from major publicly available microarray repositories and using simple but biologically meaningful keywords to query the underlying database. PathEx provides biologists (with no or limited pre-knowledge of the structure and organization of the microarray data) with an intuitive web interface to generate datasets for validation of existing studies, discovery of new phenomena or complementation of hypotheses regarding phenomena only partially understood.Many researchers must often manually retrieve or use certain tools available to retrieve microarray data from public repositories. However, such tools are most often limited to pre-knowledge of the structures and formats of the deposited microarray data.Several tools proposed are mainly either retrieval tools (Microarray Retriever (MaRe) ) or fullUnlike existing tools, the power of PathEx is its fast processing capability made possible through local storage of all of the data . PathEx also remains unique in that it acts as a point of integration of fully re-organized information from public sources. Furthermore, PathEx is not bound to any microarray manufacturer or type. This allows for the datasets selected by PathEx to be analyzed by any platform associated analysis method.Genes, Proteins, Metabolic Pathways and the Literature. By further characterizing previously deposited microarray data; we provide researchers with new opportunities to select interesting datasets by simply using meaningful biological criteria to query the underlying PathEx database.As PathEx does not aim to be yet another microarray retrieval tool and the main goal was to develop a novel concept to offer less exploited opportunities for the analysis of deposited microarray data. Deposited microarray data comes with description files (though these files are sometimes incomplete). These metadata files do however contain some key information that can be used to link the microarray data to other biologically related information. We propose here a system that uses this identification metadata to link microarray data to other biological concepts such as http://www.linux.org/; Apache web server http://www.apache.org/, MySQL relational database management system http://www.mysql.com/, PHP http://php.net/ and Perl http://www.perl.org/. A set of new web technologies such as Asynchronous JavaScript And XML , JavaScript Query , MooTools http://mootools.net/ and JavaScript Object Notation  was mainly used to increase the system's interactivity, functionality and versatility.To implement PathEx, we used the popular LAMP bundle, where LAMP stands for Linux operating system The purpose of PathEx is to allow for custom selection of microarray datasets by completing microarray annotation with biological information from different and heterogeneous sources. While microarray data is automatically drawn from the National Center for Biotechnology Gene Expression Omnibus (NCBI GEO)  and EuroHowever, one of the challenges we faced when dealing with publicly available biological data was the lack of appropriate tools to organize that data and overcome critical issues such as different file formats, ontologies, structures and accessibilities, lack of information about the contents provided (e.g. KEGG) and incomplete annotation.Although some sources have opened up their contents to the public by different means such as application programming interfaces (API) and other programmatic tools, the issues mentioned above hamper automated retrieval processes.To overcome these constraints and provide researchers with an automated criteria-driven dataset selector, we developed a set of complex tools to deal with these issues through step-by-step conversion of the contents into open formats and collection of the relevant data to be integrated into the database. The power of these tools relies on how they effectively handle different constraints  by independently importing locally and converting all required data to populate the PathEx database.To ensure the reliability and quality of the data collected, a team of biology experts scrutinized and cross-checked it wherever necessary.The PathEx architecture is divided into three main components Figure : The ProThe PathEx Processing Logic is responsible for:1. Federation of:http://www.ncbi.nlm.nih.gov/geo/info/soft2.html#SOFTformat) files and AE MicroArray and Gene Expression tab (MAGE-TAB) [a. Basic microarray data  and associated sample raw data from GEO Simple Omnibus Format in Text  ) files,b. Additional reviewed microarray metadata, not primarily envisioned by the experiment owners  andc. Biological information ,2. Remote change tracking and updating whenever required,3. PathEx user and query management and4. PathEx database integration.As one of the back end components of PathEx, The Data Mining Utility provides a set of algorithms to extract, parse, organize, correlate and convert relevant information: Microarray data (e.g. .CEL files) and metadata, Genes, Proteins, Pathways and Literature information. The Integration Utility manages a relational database Figure  componenThis component manages the PathEx data storage system: (a) the File Repositories of microarray data files: SOFT files (from GEO), MAGE-TAB files (from AE) and different biological source files used to enrich microarray characterization and (b) the Database containing structured and related microarray metadata and annotational information. GEO SOFT files contain data tables and the accompanying descriptive information for multiple, concatenated Platforms, Samples, and/or Series records.The integrated AE MAGE-TAB files consist of four different types of files: (a) A "raw" zip archive contains the raw data files, i.e. the files produced by the microarray image analysis software, such as CEL files for Affymetrix GeneChip, (b) The Array Design Format (ADF) tab-delimited file describes the design of an array, (c) The Investigation Description Format (IDF) tab-delimited file contains top-level information about the experiment including the title, description, submitter contact details and protocols and (d) The Sample and Data Relationship Format (SDRF) tab-delimited file containing the relationships between the samples and arrays, as well as sample properties and experimental factors, as provided by the data submitter.This component comprises a set of intuitive, interactive and easy-to-use web interfaces. They provide users with features to customize and select a dataset simply by specifying criteria not initially envisioned by those who deposited the expression array data.a large number of arrays. Based on these criteria, we believe that custom selection of a dataset to analyze is crucial.To conduct a routine microarray study analysis, we need (a) a dataset of interest, (b) an appropriate analysis method and (c) a means to evaluate, interpret and validate the results obtained. Currently, benchmarking studies have often emphasized the importance of selection of the analysis methods. This agrees with our recent benchmarking analysis, where we showed that the choice of appropriate analysis methods is crucial for the accuracy of the expected results. Recently, a re-analysis conducted on Golden Spike data by Pearson  outlinedAs the principal objective of a microarray analysis is to reduce variability, we should consider unexploited ways to do this, particularly in light of the outcome of several studies ,29 that However, as there are no existing tools to automatically select such a dataset, PathEx constitutes an important tool in this context.With its enriched content and advanced selection features, PathEx provides simple and easy-to-use interfaces Figure  to help With its multiple level consecutive query interfaces, PathEx maximizes the user searching process and keeps users informed of each query task filter output at each level of dataset selection within an interactive grid. A user is provided with a specific area and interfaces according to settings chosen on the entrance page. PathEx provides three-level selection interfaces, related consecutively on the organizational levels of the microarray data . Besides a search area, coupled with a set of filters  at each level to allow for criteria-driven selection of datasets, there are advanced features to ease selection such as grouping, sorting and multiselecting.Through the navigational settings, the user specifies the kind of keywords to query PathEx, to allow PathEx to display a customized interface. This approach was chosen to ease dataset selection and present clear interfaces. Many keyword types can be used to query PathEx .For each dataset selection request, a user is given a building ticket to trace the job process and download it when finished. The outcome is a compressed file containing all samples files related to the criteria submitted."lung cancer"from "all""GEO"experiments of the type "Affymetrix"GeneChip "HG-U133A". By submitting the five highlighted search keys to PathEx and applying appropriate filters, we ended up, in less than 30 seconds, with a dataset of 108 samples.There are two ways of retrieving the datasets generated. Any user may retrieve its own datasets through the job cart, as it is name-driven. To evaluate the performance of PathEx, we tested it by selecting a customized dataset related to It is important to note that generated datasets should be analyzed carefully given the known variability due to microarray design and phenotypic differences between studies. However grouping the datasets properly according to some specific characteristics can decrease the variability of a meta-analysis.In our recently published work, we trieIn the first phase of our meta-analysis, we used PathEx to select datasets about metastasis and/or hypoxia. Out of the 24 retrieved datasets, 17 were retained for further analysis , we are considering integrating other sources such as Wiki Pathways  for pathAs PathEx is not bound to any analysis method, we are currently developing a fully-automated and integrated Affymetrix web analysis tool to combine PathEx with analysis methods developed by us and proven to be efficient: the Window t-test and PHOENIX , interesPublicly available microarray data are rich mines of information. Fully exploiting them may help to understand life mechanisms. However, effective exploitation of this information requires us to further characterize them by crossing their metadata with other biological information.We present here a database coupled with a web interface that, by intelligently organizing information from different biological sources, will allow researchers to select relevant datasets . We believe this will help to discover, interpret, validate and further develop biological hypotheses without the need to conduct new experiments.http://urbm-cluster.urbm.fundp.ac.be/webapps/pathex/PathEx is freely accessible for non-commercial users from Login parameters for testing:demoUsername: 138.48Password: EB designed and coded the major part of PathEx, drafted the manuscript and wrote the final version of the manuscript. ED, as the principal project initiator, supervised the project development at all levels, reviewed and approved the last version of the manuscript. NH, as co-director of the project, intervened during technical specification, provided advice on technical choices made, reviewed and approved the last version of the project. AG, BdM, and SD verified the database content quality, tested it and gave comments on the manuscript. MP tested the PathEx database and used it to generate the data used in the article published in BMC Cancer. All authors read and approved the final manuscript.


Biochemical pathways provide an essential context for understanding comprehensive experimental data and the systematic workings of a cell. Therefore, the availability of online pathway browsers will facilitate post-genomic research, just as genome browsers have contributed to genomics. Many pathway maps have been provided online as part of public pathway databases. Most of these maps, however, function as the gateway interface to a specific database, and the comprehensiveness of their represented entities, data mapping capabilities, and user interfaces are not always sufficient for generic usage.We have identified five central requirements for a pathway browser: (1) availability of large integrated maps showing genes, enzymes, and metabolites; (2) comprehensive search features and data access; (3) data mapping for transcriptomic, proteomic, and metabolomic experiments, as well as the ability to edit and annotate pathway maps; (4) easy exchange of pathway data; and (5) intuitive user experience without the requirement for installation and regular maintenance. According to these requirements, we have evaluated existing pathway databases and tools and implemented a web-based pathway browser named Pathway Projector as a solution.http://www.g-language.org/PathwayProjector/.Pathway Projector provides integrated pathway maps that are based upon the KEGG Atlas, with the addition of nodes for genes and enzymes, and is implemented as a scalable, zoomable map utilizing the Google Maps API. Users can search pathway-related data using keywords, molecular weights, nucleotide sequences, and amino acid sequences, or as possible routes between compounds. In addition, experimental data from transcriptomic, proteomic, and metabolomic analyses can be readily mapped. Pathway Projector is freely available for academic users at  With a long tradition of being a descriptive discovery science, the field of scientific visualization has been an integral part of biosciences and has also been an indispensable approach for understanding complex, large-scale data in molecular biology. Numerous approaches for information visualization have been successfully utilized and have contributed to the understanding of genomic information, including those for the protein 3D structure, sequence alignment, and phylogenetic trees in vivo and that highly comprehensive experimental data that encompass a wide variety of pathways is readily available, arbitrary partitioning of pathways is often not useful for the mapping and observation of comprehensive experimental data. For instance, the glycolysis/gluconeogenesis pathway (map00010) in KEGG http://maps.google.com/), as well as for the visualization of gene networks and the implementation of genome browsers Most existing pathway maps have been provided as part of major public pathway databases at their websites. These maps are subdivided into individual pathways, in part due to technical limitations in manipulating large images on the World Wide Web. Given that pathways are essentially connected Despite the recent availability of several integrated pathway maps, the abstraction level of represented entities in these maps is often not sufficient to map experimental data, which is primarily due to the objectives of each pathway database. For example, the KEGG Atlas and Omics Viewer do not show genes and enzymes as nodes, but instead represent them as reaction edges. Reactome only shows enzymes as nodes, which limits its applicability for the mapping of microarray data, since many enzymes exist as heteromers that are comprised of several proteins. Similarly, when only reactions are represented in the map, data from metabolomic experiments cannot be mapped. To the best of our knowledge, there is currently no pathway browser that can map experimental data for genes, enzymes, and metabolites simultaneously using a comprehensive integrated pathway map.In this article, we present Pathway Projector, a pathway browser that allows the mapping of multi-omics information on an integrated pathway map through an intuitive user interface and ZUI. The integrated pathway map of Pathway Projector is based on the widely used layout of the KEGG Atlas with the addition of nodes for genes and enzymes for the mapping of experimental data from transcriptomic, proteomic, and metabolomic experiments. We also identify and discuss the requirements for an ideal pathway browser.As a result of a close collaboration between our bioinformatics group and experimental biologists, we have first identified the requirements for a pathway browser. We have analyzed the requirement especially in consideration of the current situations facing the biologists working with large-scale omics data with systems biology approaches, where they require a comprehensive understanding of cellular workings. Therefore, the pathway browser should be continuous and global, covering the omics layers of genome, transcriptome, proteome, and metabolome, while being intuitive and not requiring too many user interactions to allow rapid navigation for the day-to-day heuristic usage. The requirements are categorized in five groups: (R1) pathway representation, (R2) data access, (R3) mapping and editing, (R4) data export and exchange, and (R5) availability.Several pathway maps are already commonly used by biologists. We therefore chose to utilize the layout of an existing familiar pathway map as the basis of our pathway browser rather than creating a new one. The KEGG Atlas was selected for several reasons: (1) a global integrated map is provided; (2) it is a part of one of the most popular pathway databases http://extjs.com/) library was utilized to implement the overall user interface framework. ZUI for the large pathway map is implemented by using the Google Maps API for intuitive and familiar user interface and to take advantage of many online tools that are associated with the API. Since large images need to be split up into image tiles that are 256×256 pixels for use with the Google Maps API, the original large pathway map of 8192×8192 pixels was split using the generateGMap function available in the G-language Genome Analysis Environment v.1.8.8 e.g. EcoGene In order to provide the pathway browser as cross platform software without the need for user maintenance and installation, Pathway Projector is implemented as a web application using HTML and JavaScript with the AJAX (Asynchronous JavaScript + XML) web development paradigm. The Ext JS 2.0  and KEGG BLAST Search (http://blast.genome.jp/) to take advantage of existing KEGG services. Since PathComp calculates every possible route between two given metabolites from all combinations in the KEGG database, only the paths that are available within the KEGG Atlas layout are displayed in the results. For the BLAST search, sequence type (nucleotide or protein) and the type of BLAST program , are automatically interpreted by the system, in which sequences comprising 80% A, T, G, C, or N are considered to be nucleotide sequences. The Pathway prediction tool reconstructs the pathway from given multi-FASTA sequence files using BLAT and SwissProt Pathway Projector has four types of search functionalities: (1) by keyword and identifiers; (2) by molecular mass; (3) by computation of possible routes between two metabolites; and (4) by sequence similarity. Searches by keyword, identifiers, and molecular mass were established by searching through a server-side database reconstructed from KEGG flatfile distributions. The results are listed in a search result panel and are also visually highlighted by red markers placed onto the respective components on the pathway map. Additional gray polylines are drawn on corresponding reaction edges when the search result points to enzymes or genes. The red markers can be clicked to invoke an information window to display more detailed annotations. The route search and similarity search capabilities are implemented as wrappers of PathComp (http://code.google.com/intl/en/apis/chart/) are displayed on the nodes. Quikmaps (http://quikmaps.com/) was utilized to implement manual annotation and editing capabilities.The Pathway mapping tool modifies the SVG map based on user input, by changing the visibility, size, color, and labels of edges and nodes, and subsequently creates an overlay. Users can also place predefined icons or any image available in the World Wide Web by URLs and show the directionality of reactions by adding arrowheads to reaction edges. When values for time-series or multiple conditions are specified for nodes, graphs or charts generated by the Google Chart API (The requirements are categorized in five groups: (R1) pathway representation, (R2) data access, (R3) mapping and editing, (R4) data export and exchange, and (R5) availability  or small nucleolar RNAs (snoRNA), or phosphorylated isoforms of proteins are emerging areas of research that are actively being explored and could benefit from interpretation within a biochemical context. Therefore, the pathway map should be able to be freely edited and annotated by adding nodes, edges or data, as in WikiPathways http://www.biopax.org).For interoperability and data management, pathway data and mapping results should be downloadable in a standard XML image format, such as SBML (Systems Biology Markup Language) In order to be platform-independent and interoperable without maintenance and installation efforts, a pathway browser should be available as a web-based application that requires no registration fees and that is freely available for academic users.http://www.g-language.org/PathwayProjector/. Since it has been implemented as a web application, this software is cross-platform and requires no installation or maintenance. Moreover, use of the AJAX web development paradigm provides an intuitive user experience similar to that of desktop applications. The main pathway map of Pathway Projector was reconstructed from the popular KEGG Atlas layout by adding nodes for enzymes and genes. As an example, the map for E. coli K12 contains 1365 genes, 1813 enzymes, and 1572 metabolites . Search results are directly shown within the pathway map, marked with red pins on the nodes as well as gray lines highlighting the corresponding edges for reactions. A list of search results is also available in the right-most panel , EC number , and gene names, while the basal pathway map is specified by the KEGG Organism identifier . The graph of time-series or multi-condition data can also be visualized on top of the corresponding node by specifying the values as comma-separated vectors, and the graphs can be viewed at higher resolution by invoking the information window. Users can also alternatively upload a tab-delimited data file, where the first rows are component identifiers, and the columns are experimental data. By placing these graphs on the metabolic pathway, researchers can easily interpret complex multiple experiments in the context of biochemical pathways and subsequently identify the systematic response to perturbations. Map generation for Google Maps generally requires several minutes of computation time since thousands of tiled images must be prepared on the server; however, through multi-thread and multi-core optimization, Pathway Projector is able to generate the mapped image as Google Maps ZUI in less than 20 seconds on a dual quad-core CPU node. The mapped image is available as a downloadable image or as a transparent overlay, which can be toggled to display by pressing on the “Customized” button located in the upper right corner of the map. Because the mapping feature can specify the basal organism map and because it is implemented as an overlay of existing pathway maps, users can take advantage of this mapping feature for cross-species comparative study. For example, to compare the pathway maps of human and mouse, creating an overlay of mouse maps, which can be simply accomplished by creating a mapping layer with “Organism:mmu”, on top of the human map allows rapid switching between the two pathways. Simple Object Access Protocol (SOAP) web service is also available for data mapping for interoperability with other tools. Users can access this service through programming languages such as Perl, Python and Ruby, or through intuitive SOAP clients with graphical user interfaces such as Taverna workbench http://www.g-language.org/PathwayProjector/PathwayProjector.wsdl and detailed documentation as well as sample scripts for several programming languages are available in the online documentation (http://www.g-language.org/PathwayProjector/annotation.html#soap).The mapping feature of Pathway Projector is available from the “Tools” window, which allows full customization of pathway diagrams by changing the color, size or width, labels, and node image (specified by preset icons or URLs of images). In addition, directionality can be indicated by arrow heads, and graphs of multiple conditions or time series can be displayed . Users cPathway Projector is also equipped with editing capabilities, such as drawing lines, placing icons and text labels, and adding annotations, to add novel findings that are not included in the reference pathway . The edihttp://www.plantcyc.org/) for plants, EcoCyc for E. coli, and MGD ftp://ftp.genome.jp:/pub/db/community/biopax. The map editing features provided in this software is intended for quick note taking for information exchange along researchers. While this feature allows free editing and annotations on the pathway maps, these editings are overlays of additional information, and are not true modifications to the existing pathway map. For the generation of static pathway map images, users should use the mapping tool to generate a custom pathway map, and download this image to make permanent modifications.Pathway Projector is currently limited to the metabolic pathway, due to the availability of the global map in KEGG Atlas. Therefore, pathways such as signaling networks, gene regulation, and cell cycle are not supported by our software. Since our software tool is semi-automated to import information from KEGG, large-scale maps for these particular pathways, if they become available, will be relatively easy to integrate into Pathway Projector. Second, the KEGG pathway maps are generic implementations, for which the reference pathway serves as the net sum of all known reactions from a multitude of organisms. There are cases for which a specific pathway map dedicated to just a single species of interest is instead more desirable, such as the PMN Click here for additional data file.


Minimal set of Pathways), for biological pathway reconstructions using protein family predictions, which yields a more conservative, yet more faithful, estimation of the biological pathways for a query dataset. MinPath identified far fewer pathways for the genomes collected in the KEGG database—as compared to the naïve mapping approach—eliminating some obviously spurious pathway annotations. Results from applying MinPath to several metagenomes indicate that the common methods used for metagenome annotation may significantly overestimate the biological pathways encoded by microbial communities.A common biological pathway reconstruction approach—as implemented by many automatic biological pathway services (such as the KAAS and RAST servers) and the functional annotation of metagenomic sequences—starts with the identification of protein functions or families  in the query sequences, followed by a direct mapping of the identified protein families onto pathways. Given a predicted patchwork of individual biochemical steps, some metric must be applied in deciding what pathways actually exist in the genome or metagenome represented by the sequences. Commonly, and straightforwardly, a complete biological pathway can be identified in a dataset if at least one of the steps associated with the pathway is found. We report, however, that this naïve mapping approach leads to an inflated estimate of biological pathways, and thus overestimates the functional diversity of the sample from which the DNA sequences are derived. We developed a parsimony approach, called MinPath : given a set of reference pathways and a set of functions that can be mapped to one or more pathways, MinPath aims at finding a minimum number of pathways that can explain all functions. MinPath achieves a more conservative, yet more faithful, estimation of the biological pathways encoded by genomes and metagenomes.Even though there is only a single large biological network within any cell and all pathways are to some extent connected, the partition of the entire cellular network into smaller units  is extremely important for understanding biological processes. Biological pathway reconstruction, therefore, is essential for understanding the biological functions that a newly sequenced genome encodes and recently for studying the functionality of a natural environment via metagenomics. The common practice of pathway reconstruction in metagenomics first identifies functions encoded by the metagenomic sequences and then reconstructs pathways from the annotated functions by mapping the functions to reference pathways. To address the issues of both incomplete data  and pathway redundancy , we formulate a parsimony version of the pathway reconstruction/inference problem, called MinPath ( For instance, in a recently published report, the coupling of N2 fixation to cellulolysis was revealed within protist cells in the termite gut, based solely on the in silico pathway reconstruction of the complete genome sequence of a bacterial endosymbiont We note that proteomics experiment, the proteins represent a particular biological sample collected under a specific physiological condition or from a specific tissue (e.g. from yeast cells after the heat shock), which are in high enough abundance to be identified by tandem mass spectrometry metagenomic data, to characterize the aggregate metabolic processes of microbial communities in a given environment Moreover, pathway reconstruction based on some new high throughput techniques must provide conclusions from explicitly incomplete information, which poses fresh challenges. For example, in a typical e.g. through different protein domains, active sites, or substrate specificities. Third, neither organisms nor communities are closed boxes, and the products or intermediates of pathways may be exogenously supplied. Finally, homology-based protein searching may map one protein to multiple homologous proteins with different biological functions . In summary, it cannot be safely concluded that a pathway is present, even if one or more proteins are mapped to it.In addition to the problems that arise from incomplete data, existing methods of pathway reconstruction or inference may over-estimate the number of pathways because of redundancy in the protein-pathway, at four levels. First, different pathways may share the same biological functions. The partition of pathways  is extremely important for understanding of biological processes, even though there is only a single large biological network within any cell and all pathways are to some extent connected http://www.genome.jp/kegg-bin/show_organism?menu_type=pathway_maps&org=hsa) . The Calvin cycle is the most common method of carbon fixation, while the reductive carboxylate cycle is an alternative carbon fixation pathway, currently found only in certain autotrophic microorganisms. In fact, the reductive carboxylate cycle is essentially the reverse of the Krebs cycle (citric acid or tricarboxylic acid cycle), the final common pathway in aerobic metabolism for the oxidation of carbohydrates, fatty acids and amino acids, so they share reactions and functional roles. For this reason, the proteins responsible for the normal function of the Krebs cycle can be mistakenly taken as evidence for the existence of a reductive carboxylate cycle in the human genome.Even for single complete genomes, pathway reconstruction does not always give a clear picture of the biological functions in an organism, and human curation and experimental verification is often needed pathway inference might be a more suitable terminology than pathway reconstruction. However, considering that pathway inference has been used in a different context to infer networks or pathways from gene express data Minimal set of Pathways), which can be roughly described as the following: given a set of reference pathways and a set of proteins (and their predicted functions) that can be mapped to one or more pathways, we attempt to find the minimum number of pathways that can explain all proteins (functions) , but to determine the minimal set of biological pathways that must exist in the biological system to explain the input protein sequences sampled from it. In this context, we note ons) see . AlthougWe focus on analyzing complete genomes in this study because there is a relatively good understanding of the pathways that actually exist in organisms with completely sequenced genomes (as compared to the emerging metagenomes), making this analysis a good test of our method. Besides, the pathway annotations of these genomes are still far from perfection, as in the example of a carbon fixation pathway in the human genome . We also applied MinPath to the analyses of several metagenomic datasets, to demonstrate the potential applications of MinPath in metagenome annotation.We first revisited the pathway reconstruction of individual genomes using MinPath. The results show that MinPath gave a conservative, but reliable estimation of the pathways of a genome, and therefore the functional ability/diversity encoded by a genome. In addition, MinPath found suspicious pathways in the KEGG database. We then applied MinPath to a set of metagenomic datasets, and the results indicate that the current estimation of functional diversity/ability of studied microbial communities might be overestimated.The function annotations of the genes from individual genomes were extracted from the KEGG database, and used as input for MinPath to reconstruct the pathways encoded by each genome. A total of 854 genomes were studied, and the overall performance of MinPath is shown in For the human genome, there are 205 predicted KEGG pathways (as of December 2008), while the naïve mapping approach identifies 227 pathways. MinPath identified only 191 pathways—these pathways are necessary and sufficient to explain all the annotated human proteins in the KEGG database.Many of the pathways that are identified by the naïve mapping approach are spurious and are not curated in the KEGG database , indicating that MinPath can be applied to remove pathways that are otherwise mistakenly annotated using the naïve mapping approach. More examples are listed in the supplementary website.Some of the pathways that are curated in the KEGG database are marked by MinPath as spurious see . For exaE.coli genome. It is a conservative estimation, as compared to the 125 pathways for Escherichia coli K-12 MG1655 collected in the KEGG database, and 158 pathways that have at least one or more associated functions identified in the genome. Refer to the supplementary webpage for the details of the pathway reconstructions and their comparison for E.coli. It is obvious that the naïve mapping approach leads to an inflated estimate of biological pathways in E.coli—the list even includes several biological pathways involved with human cancer, including renal cell carcinoma (pathway ID 05211), prostate cancer (pathway ID 05215), and bladder cancer (pathway ID 05219). These pathways were wrongly annotated because one or more predicted functions in these pathways are also involved in other pathways. For example, fumarate hydratase (ko:K01679) is involved in the renal cell carcinoma pathway, as well as in the citrate cycle, a fundamental pathway present in most bacteria. Based on the identification of this enzyme alone, the naïve mapping approach predicted the presence of the renal cell carcinoma pathway in E. coli, which obviously cannot be true. MinPath removed these spurious pathways from the list of constructed pathways, without human curation.MinPath identified 98 biological pathways that are sufficient to explain all the identified functions encoded by the E. coli genome, e.g. the mitochondrial fatty acid elongation pathway and the bile acid biosynthesis pathway .We used MinPath to re-analyze the biological pathways of several metagenomes For all the datasets we tested, MinPath reduced the total number of annotated pathways (or subsystems) significantly . The naïve mapping approach identified 224 KEGG pathways, whereas MinPath identified only 143 KEGG pathways. The pathways eliminated by MinPath include the inositol metabolism pathway, the androgen and estrogen metabolism pathway, the caffeine metabolism pathway, etc (see more examples at the supplementary website). Obviously, comparisons of microbial communities or other biomes will be more telling if spurious pathways are eliminated, and our results suggest that as many as 40% of the 224 pathways could be wrong.We have developed the MinPath approach to provide more conservative—but more reliable—estimations of biological pathways from a sequence dataset, and applied this approach to revisit the biological pathway reconstruction problem for genomes as well as metagenomes. Our results show that without further post-processing of the reconstructed pathways, the naïve mapping strategy may overestimate the biological pathways that are encoded by a genome or metagenome, which could jeopardize any conclusions drawn from the constructed biological pathways  Note that MinPath is not designed to directly improve the still imperfect definition of pathways and/or functions in databases such as KEGG or SEED. For example, as a result of how some pathways are grouped in the KEGG database, peptidoglycan biosynthesis is listed for the human genome by KEGG annotation and MinPath does not eliminate this pathway from the list of annotated pathways from human genome. In this sense, efforts are still needed to improve the elucidation and annotation of extent biochemical pathways. But given a database of reference pathways, we feel that MinPath provides a sensible method for inferring the pathways represented in biological sequence samples.First we will briefly describe the naïve mapping approach that is commonly used in current automatic biological pathway reconstruction services , as well as for pathway reconstruction for metagenomic sequences. Then we present a novel minimal pathway reconstruction approach based on a simple yet efficient algorithm for solving this problem.Pathway reconstruction has become routine in functional annotation of genomes and metagenomes, in which KEGG pathways  are reconstructed based on homology. KEGG and SEED databases collect pathways (or subsystems) curated by experts, each pathway/subsystem consisting of a series of functional roles . Pathway reconstruction consists of two key steps: (1) predicting the functions (represented by protein families) of proteins encoded by the DNA sequences, which is often achieved by similarity searching of the predicted proteins against reference proteins from previously characterized genomes; and (2) predicting the presence or absence of pathways in the query dataset, based on the identified functions associated to the pathways. Conventional pathway reconstruction usually adopts simple criterion in this second step (herein referred to as the naïve mapping approach), i.e., a pathway is considered to be present if one or more functions in the pathway are identified in the first step. We have shown in this paper that this approach may lead to the identification of spurious pathways and an overestimation of functional ability, which motivated us to develop a novel approach to pathway reconstruction based on the parsimony principle presented below.minimal pathway reconstruction problem as the following: given a list of functions annotated for a set of genes , find the minimal set of pathways that include all given functions  is an algorithm for finding the maximum or minimum of a linear function of variables (objective function) that are subject to linear constraints n. Let the total number of putative pathways which have at least one component function annotated be p. Denote the mapping of protein functions to the pathways as M, where ijM = 1 if function i is involved in pathway j, otherwise 0 (note one function may map to multiple pathways or subsystems). Denote if a pathway j is selected in the final list or not as jP, with jP = 1 if selected, jP = 0 otherwise. The set of pathways with iP = 1 composes the minimal set of pathways that can explain all the functions that are annotated for a dataset.Here we transform the minimal pathway reconstruction problem to an integer programming problem: Denote the number of functions (protein families) that are annotated in a dataset as The objective function for integer programming is,We use the KO and FIG protein families defined in the KEGG database and the SEED subsystems, respectively, for this study. Many of the mappings of KO families to KEGG pathways were done manually in the KEGG database. These families are the basic units for pathway reconstruction (or subsystem reconstruction in SEED), in which a pathway (or a subsystem) is composed of a list of functional roles.http://www.gnu.org/software/glpk/glpk.html) for solving the integer-programming problem; all the other functions are implemented in Python.We use the GLPK package  annotated in a given dataset of genes , and the output is the list of pathways reconstructed/inferred for the dataset.The input for MinPath is a list of protein families . MinPath will keep one of these pathways, because that is sufficient to explain the functional roles identified. We added a post-processing step here to add those pathways that have more than 50% of their functional roles identified back to the pathway pool, even when these functional roles appear in another pathway that is already predicted by MinPath.ftp://ftp.genome.jp/pub/kegg/release/current/).We revisited the pathway reconstruction for the 854 genomes in the KEGG database  that have at least 20 KEGG pathways annotated for each of these genomes. For these genomes, the function (or protein families) annotations were downloaded from the KEGG database (http://metagenomics.theseed.org/). We conducted the KO family annotations of the sequences based on the best blast hits with E-value cutoff of 1e-5, a typical E-value cutoff used for KEGG pathway reconstruction in metagenomes We also applied MinPath to reanalyze the pathways for nine biome metagenomic datasets http://omics.informatics.indiana.edu/MinPath/. Supplementary material is also available at the MinPath website.MinPath is available as a server and the source codes are available for downloading at MinPath webpage,