The customary medicinal plant knowledge possessed by the Australian Aboriginal people is a significant resource. Published information on it is scattered throughout the literature, in heterogeneous data formats, and is scattered among various Aboriginal communities across Australia, due to a multiplicity of languages. This ancient knowledge is at risk due to loss of biodiversity, cultural impact and the demise of many of its custodians. We have developed the Customary Medicinal Knowledgebase (CMKb), an integrated multidisciplinary resource, to document, conserve and disseminate this knowledge.. Known bioactive molecules are characterized within the chemoinformatics module of CMKb, with functions available for molecular editing and visualization.CMKb is an online relational database for collating, disseminating, visualising and analysing initially public domain data on customary medicinal plants. The database stores information related to taxonomy, phytochemistry, biogeography, biological activities of customary medicinal plant species as well as images of individual species. The database can be accessed at CMKb has been developed as a prototype data resource for documenting, integrating, disseminating, analysing multidisciplinary customary medicinal plant data from Australia and to facilitate user-defined complex querying. Each species in CMKb is linked to online resources such as the Integrated Taxonomic Information System (ITIS), NCBI Taxonomy, Australia's SpeciesLinks-Integrated Botanical Information System (IBIS) and Google images. The bioactive compounds are linked to the PubChem database. Overall, CMKb serves as a single knowledgebase for holistic plant-derived therapeutics and can be used as an information resource for biodiversity conservation, to lead discovery and conservation of customary medicinal knowledge. Australia is among the 34 biodiversity hotspot countries in the world  endowed Documented Australian medicinal plant knowledge is in the main, fragmented, restricted to specific locales and of limited applicability, usually to pharmacology or phytochemistry. Several studies have focussed on the Northern Territory, where the use of medicinal plants has been documented, with limited data on chemical components and pharmacological assay work ,10. A daCustomary knowledge of medicinal plants and practices is a significant contributor to scientific research and development in pharmaceuticals, cosmetics, foodstuffs, agricultural products and a wide range of other biologically based products and processes . Access The goal was to design a database which could be flexible and could accommodate heterogeneous data from published literature or bibliographic search. CMKb is developed using MySQL 5 relational database  for systBefore developing the database schema, end user and data resource availability assessment was carried out. The assessment results showed that the potential end users range from members of Aboriginal communities to scientists with interests in ethnobotany, phytochemistry, biology and microbiology. The major data resource is the information collated from an exhaustive literature survey.We have created a novel schema for integrating multidisciplinary information on medicinal plant species, such as taxonomy, habit and habitat, phytochemistry, bioactivity, biogeography, data sources, medicinal preparation methods and usage, community information, and images into CMKb. Since the species name is the fundamental biological descriptor , all theInformation related to medicinal plant species is stored in seven major tables Figure  which arInformation related to customary medicinal plant species such as kingdom, family, scientific name, synonym, common name, native language name, habit and habitat as well as author citation, is stored in this table. This table is the hub to which all other tables are connected. The scientific name from this table is also used for cross-linking to external data portals, such as IBIS, ITIS and NCBI Taxonomy.Each published article in the literature used to collate and populate the database, is assigned a unique DSN identifier. The DSN table contains fields such as the title of the article, reference type , names of authors and citation details.Species-specific customary medicinal information such as the parts of the plant used, preparation method, taste, odour, colour, application and storage method, is collated in this table.This table records the biological activity associated with the medicinal plant. The type of assay used to identify biological activity , the specific assay used, assay targets  are recorded in this table.This table is used to store the chemical information and structure of bioactives derived from the medicinal plants such as IUPAC name, CAS number, PubChem  identifiThe biogeography table collates observational data of the species from the published literature such as locality name, latitude and longitude in decimal units, district/town, state and country.CMKb will also accept data in multimedia formats. This table is used to store multimedia information for each species, in the form of videos, drawings and photographs. Multimedia file formats such as jpeg, mpeg and avi can be uploaded to the database, with detailed text description.Browse, Search and Data Management. Links to these modules are provided as a menu on the LHS of the CMKb website, as "Browse," "Search" and "Login," respectively. A brief description of each module is given below.CMKb provides a user friendly web interface for accessing and managing the customary medicinal plant data. The database consists of three main modules: The database contents can be browsed Figure  using thSince CMKb is a web-based application, we have provided external links to other relevant global databases and data portals. Linking with other databases providing taxonomic, geospatial and molecular information, and search engines for images will help in data mining and facilitate the exploration of questions that, at present, cannot readily be answered  and woulThe database can be searched using its comprehensive search engine. The "Quick Search" option provides users with the facility to query the database by scientific name, species common name, native name, locality or chemical name using different logical parameters such as "contains", "begins with", "ends with" and "is" Figure .For more complex queries, the "Advanced Search" option can be used, where the user can combine different search fields, using AND as the logical parameter Figure Efficient online content management is coordinated by CMKb's data management module, accessible to authorized users via the Login link. The data management module is provided with ADD, EDIT and DELETE functionality for managing data present in different tables.The overall contents of the database can be accessed from the "Content Summary" link.Customary Medicinal Knowledgebase (CMKb) is a prototype for collating, integrating, visualising, disseminating and analysing multidisciplinary public domain data on customary medicinal plants. It is a holistic knowledgebase with data on taxonomy, biogeography, ethnobotany, phytochemistry, and bioactivity of the customary medicinal plants used by the Australian Aboriginals. The goal of CMKb is to collate information from scientific publications which are peer reviewed along with documenting and conserving the dwindling customary medicinal plant knowledge. The data will be constantly scrutinised by the experts and will be updated accordingly. Overall, CMKb is developed as a single knowledgebase for holistic plant-derived therapeutic substances and can be used as an integrated resource by researchers, policy makers, students and Aboriginal communities. As the database grows CMKb can be used for research in areas such as Geographical Information System (GIS) studies, chemoinformatics and biodiversity informatics. Further, the goal is to help address global and national priorities of biodiversity conservation, better human health, and smart use of information using information technology.CMKb is freely available online at CAS: Chemical Abstracts Service; CMKb: Customary Medicinal Knowledgebase; CSIRO: Commonwealth Scientific and Industrial Research Organisation; DSN: Data Source Number; FDSN: Field Data Source Number; GIS: Geographical Information System; IBIS: Integrated Botanical Information System; ITIS: Integrated Taxonomic Information System; IUPAC: International Union of Pure and Applied Chemistry; JRE: Java Runtime Environment; LHS: Left Hand Side; NCBI: National Center for Biotechnology Information; SMILES: Simplified Molecular Input Line Entry SpecificationThe authors declare that they have no competing interests.SR, JK, JJ and SV conceived the database concept. JG developed and constructed the database. VK contributed to the web interface and developed the Chemical information module. JG and SR wrote the paper. All authors approved the manuscript and declare that there is no conflict of interest.


Wiki technology has become a ubiquitous mechanism for dissemination of information, and places strong emphasis on collaboration. We aimed to leverage wiki technology to allow small groups of researchers to collaborate around a specific domain, for example a biological pathway. Automatically gathered seed data could be modified by the group and enriched with domain specific information.We describe a software system, BioKb, implemented as a plugin for the TWiki engine, and designed to facilitate construction of a field-specific wiki containing collaborative and automatically generated content. Features of this system include: query of publicly available resources such as KEGG, iHOP and MeSH, to generate 'seed' content for topics; simple definition of structure for topics of different types via an administration page; and interactive incorporation of relevant PubMed references. An exemplar is shown for the use of this system, in the creation of the RAASWiki knowledgebase on the renin-angiotensin-aldosterone system (RAAS). RAASWiki has been seeded with data by use of BioKb, and will be the subject of ongoing development into an extensive knowledgebase on the RAAS.http://www.bioinf.mvm.ed.ac.uk/twiki/bin/view/TWiki/BioKbPlugin as a plugin for the TWiki engine.The BioKb system is available from  The storage, collation, and presentation of biological knowledge is a ubiquitous theme in bioinformatics. A typical approach has been the use of a relational database system alongside a web 'front end', allowing deposition and retrieval of content. Where the target data are very structured, comprising lists of homogeneous data, this is an effective strategy with a number of advantages, including rapid retrieval of content. However this approach does not lend itself to collaborative editing of, and addition to the database content in a user-friendly manner, a role for which wikis are uniquely suited.http://www.wikipedia.org/, wikis are starting to have an impact in a number of scientific disciplines, fostering communication and collaboration between the members of a research community [Biologists are often interested in a specific field and would like to gather and annotate information on biological objects with reference to that field. Collaboration in this limited scope, with deep user derived annotation, can be implemented with wiki technology and is as appropriate as large scale, wide focus wikis. Wikis allow the collborative editing of content by anyone visiting a web page, via simple text markup. Entering the popular conciousness with the advent of Wikipedia ommunity . Some reommunity , miRDB [ommunity  and RepPommunity  employ wommunity  integratommunity  providesommunity  is a gooommunity  employs ommunity , and to ommunity . While tommunity .Biological wikis with very broad focus, for example the WikiProfessional concept web  can be iWe present both a generic system to establish base content for a specialised biological wiki, and an example of its use to create a knowledgebase for the renin-angiotensin-aldosterone system, RAASWiki.The initial goal of this work was to create a comprehensive store of information pertaining to the specific biological system of interest. We decided on a combined approach, starting with seed content easily derived from online resources, to be edited and augmented subsequently by more manual means to maintain a comprehensive, up to date curated resource of current knowledge of the system. BioKb is the set of generically applicable functions developed for this purpose, implemented as a plugin for the TWiki engine.http://twiki.org/, written in Perl and supplemented with pre-existing JavaScript where appropriate. This approach enables simple integration of BioKb into an existing TWiki installation, and makes the code freely available for critique, and possible development by the community.Software to derive initial seed content, which we call BioKB, was developed within the plugin framework of the TWiki engine Since many other developers have written plugins for TWiki, a number of additional functions are available very simply with minimal extra effort. Features native to TWiki or available in plugins include fine-grained access control, revision control, dynamic embedded search functions and simple incorporation of comments. All of these features could have been constructed independently, but their presence in the TWiki setup significantly eases their incorporation into any wiki-based resource.http://twiki.org/. In order to increase speed of search and navigation within the TWiki web where large numbers of topics are present, a pre-existing plugin, variable cache plugin, was applied that allowed caching of topics. This second functionality is especially useful in topics where large amounts of content are generated dynamically: for example subsequent to one or more complex searches. Code to download and compile content, as well as facilitate user input was then written in Perl as a TWiki Plugin, following instructions on the TWiki website. CSS code defining layout and appearance was extensively developed from an example taken from http://www.freecsstemplates.org/preview/grandenally. The use of these modifications to appearance were applied to TWiki by use of the 'pattern' skin of TWiki, enabling simple addition to, or overriding of CSS settings where necessary. All image and CSS files are distributed with the plugin and installed along with the Perl code.TWiki version 4.2 was downloaded from http://www.ihop-net.org/). OMIM provided a source for limited details on potential disease associations for genes [http://www.ncbi.nlm.nih.gov/entrez/query/static/eutils_help.html. Employing topic titles and their synonyms, relevant terms in other topics are linked appropriately.With a KEGG pathway ID as a starting point, the KEGG  API is uor genes   is to enable creation of specialised knowledgebases benefiting from the complementary features of automatic and collaborative manual annotation. A large number of features were engineered in addition to those available by default in TWiki, including:• Consistent and automatic formatting of topics of similar type• Interactive addition of automatically generated topics to a TWiki web based on molecular biology data retrieved by use of a KEGG pathway ID, or paper abstracts produced through a PubMed search• Post-seeding topic addition via relevant identifiers such as PubMed or gene IDs.• Assisted topic editing, whereby the more structured content of automatically generated topics is parsed back into an input form for editing, precluding the need to edit wiki code.• Automatic linking of keywords to topics based on a dictionary relating keywords to topics, also making use of synonyms where appropriate.The structure of a page in a BioKb wiki consists of two parts. A structured portion at the top allows delineation of fundamental data such as sequence IDs and MeSH terms, and provides links to the source data. A column in this section indicates the origin of the data, and shows if information is derived directly from external resources, or has been created or edited by a user. Functions are provided which allow data in this section to be edited via a form, which both maintains syntax and facilitates the editing process. Underneath the data section, space for unstructured annotation is provided, for example discussion of the importance of a gene for the system(s) of interest. The combination of structured and unstructured input allows for flexible data input, and may reinforce one another. For example, a given group of users might consistently add a particular item to a gene topic- for example the availability of clones etc. Observing this, or at the request of users, an administrator could alter the structure of the data types to include a field for this in future.Compared to a wiki-only approach, BioKb offers the possibility of added structure- for example summarising the associated IDs and sequences of a gene, as derived from external databases such as KEGG. Relative to a database-driven approach, the inherent features of the underlying wiki engine allow much greater editability and collaboration outside of the structured page regions. Where common themes develop in the unstructured discussions around specific data types, one can imagine these inspiring the addition of new structured elements in the data type. The ability to mix features in this way was a key driver for the development of BioKb, and the usage scheme envisaged is illustrated in Figure BioKb divides each wiki into three subsections: Disease and Medicine, References, and Molecular Biology, and data types such as gene and disease fall into one of these three categories. Wiki adminstrators can create new data types with a defined set of fields for the structured part of each topic, and assign them to whichever division is most appropriate. For example, data types to represent different high-througput experimental techniques may be incorporated, in addition to 'Gene Expression' present in BioKb by default. This functionality should enable users to extend the usefulness of BioKb webs significantly beyond the default settings.BioKb defines a template wiki (or 'web' in the TWiki terminology), from which all BioKb wikis are derived. Some of the pages (or 'topics') defined in this wiki determine the appearance of a given data type (for example 'Reference'). Others provide summary sections for each data type, and are created with embedded TWiki search functionality that will provide overviews of content once the wiki is populated. 'Administration' topics, also provided by default, can be used to alter the functionality that BioKb provides over that present in TWiki, and can be customised prior to the population of a wiki. Most important of these is a definition of the data type and form structure, illustrated in Figure BioKb provides four complementary ways to populate a wiki, with varying requirements for user input. On creation of a BioKb web, users are presented with default content including summary pages and lists of topics of various types. Embedded functions, for example those placed by default in the left toolbar, can then be used to incorporate content by manual or automatic means as described below.By supplying a KEGG pathway ID, a list of associated genes (and their orthologs), compounds, drugs, disease states  while allowing free content creation. This might not be appropriate where a structured data type is required, for example listing the properties of a gene, but might be in other situations where a conceptual data type and associated fields are difficult to define.Topics may be created by the use of forms generated dynamically following definitions in an Admin topic. By completing the fields in this form, the result is a topic structure identical to that achieved by automated means, thus maintaining consistency. The fields in this form can also be populated on-screen if an external ID (e.g. KEGG Gene ID) is supplied, allowing the user to edit the information from these resources prior to topic creation. The form also contains fields for input of identifiers pertaining to external databases. In contrast to purely manual topic creation, these IDs are presented to BioKb in a structured way, so that appropriate URLs can be constructed. In order to track provenance of the data, where fields are not altered from the source, source and date alone are recorded. Where a user has edited the field, this is indicated. A function is provided in each such structured data section to read out the information into a form again for editing. This method has the advantage that non-technical users do not encounter wiki syntax.Another available function is a form to enable complex queries to PubMed. Similar to the seeding process, a user is presented with a list of articles and titles, alongside an indication of whether they currently form part of the wiki. Users may choose to add or remove paper abstracts from the list presented to them for incorporation. Since references are not mutable over time, users select which to include, but are not presented with the opportunity to edit them via form functions- though this can be achieved through the native wiki functions.On topic creation by any of the means described above, a reference dictionary of key terms and topics is consulted, and used to add cross-links where appropriate. This dictionary is also amended with details of the topic currently under construction, to allow links to it on future topic creation events, and links added to it from all pre-existing topics based on keyword matching and use of appropriate iHOP-derived synonyms. To reduce the occurrence of incorrect links, linked terms must be longer than three characters, and the dictionary is accessible as an admin function, to allow corrections where topics are linked inappropriately to particular terms. Where a term matches to multiple dictionary terms, the longest match is selected to preserve specificity.BioKb is distributed with scripts which, when added to the task scheduling system of an operating system, can be used to update content regularly. For example abstracts can be regularly retrieved from PubMed corresponding to a specific search string, and its keywords linked appropriately to the rest of the wiki. We do not, however, envisage regular automatic updates of the seed content of existing topics. In a knowledgebase of this kind it is important that consistency is mainted between manual and automatic annotations. The types of annotation derived at seeding are fairly static , but where revisions to seed data are necessary, we would expect this to be undertaken manually, with the help of the form editing functions provided, preserving manual annotations.BioKb is not intended simply as an aggregator of existing online resources, but rather as a method by which a framework for collaboration within a research group may be established. Remote queries establish basic information such as protein sequence, synonyms and possible disease associations, around which further annotation and discussion may be placed in a more manual, but possibly more informative way. As in all wiki approaches, we would hope that user groups will used seed data, and expand upon it, with only administrative or structural pages o3 limits for editing.The renin-angiotensin-aldosterone system (RAAS) is a molecular pathway implicated in a number of physiological systems, most notably in the regulation of blood pressure and fluid balance. A number of diseases are associated with disruption of RAAS function, and manipulation of the RAAS is often the first target of therapeutic drugs against high blood pressure (hypertension). This system is highly relevant to many research groups worldwide, including those in our institute, and the collation (and collaborative curation) of information relevant to this system was therefore highly desirable.A focused scope for a specific field allows a biologist to concentrate on immediately relevant information for that gene. Of course, the relevance will be defined by the user or group. A user of RAASWiki is likely to be interested in the involvement of membrane metallo-endopeptidase in hypertension, but less so in its role in Alzheimer's. Similarly, cathepsin G has many interactions outside the RAAS that might be less relevant to a user of RAASWiki.http://www.raaswiki.org). The KEGG database contains an entry for the renin-angiotensin system , and this served as the starting point in construction of RAASWiki. However the KEGG pathway does not incorporate other genes tightly associated with the pathway, most importantly those associated with aldosterone, and these were incorporated piece-wise by use of BioKb's form interface. In addition, the illustration used on the front page of RAASWiki was custom-designed by use of 'Wordle' [Using the BioKb system, we engineered a knowledgebase for the RAAS  is unambiguously identified in RAASWiki. We have attempted to use a fairly well accepted standard, the official gene symbol to identify a gene. In addition, other database identifiers  are given where available. Hyperlinks using these identifiers allow the user to navigate to many alternative resources for each gene. Literature references are identified by PubMed ID; Gene expression dataset by GEO or Array Express identifier and so on. These automatically generated links replicate how a biologist would navigate the available information.Figure http://www.bioinf.mvm.ed.ac.uk/twiki/bin/view/LTEMWiki. This was generated by reference to the pathway definition for leukocyte transendothelial migration (pathway ID hsa04670) found in KEGG [Another pathway of significance to our institute is that of leukocyte transendothelial migration. As an example of the versatility of the BioKB system, we also created LTEMWiki  in KEGG , and hasThe core philosophy of RAASWiki, and more generally of BioKb, is the use of automatically derived data as core around which to assemble information not easily obtained by automatic means, in particular data and annotation specific to a system of interest. User participation will be of central importance and development is therefore ongoing for both BioKb and RAASWiki with input from laboratory scientists.A more diverse range of sources for automatically derived seed data Predominant among planned developments for BioKb is the incorporation of as many sources as possible for seed content and automatic population of input forms.• Provenance Users stressed the importance of knowing the origins of data displayed. A simple provenance scheme has been implemented (see above), but other aspects will be developed- for example to indicate the reasoning associated with including a given publication in the wiki .• More integral provision for discussion The ability to undertake discussions around the data present in a wiki is highly useful, and we will be examining ways of improving upon this. The current implementation allows discussion via a series of comments in a given topic in a discussion-board style. It may be that discussion common to a set of topics would be useful, for example in a threaded 'forum' style, and the possibilities for this will be investigated.• User feedback on RAASWiki from laboratory researchers has indicated a number of areas for development, many of which are ongoing, and may feed back into BioKb development. RAASWiki will be developed in the next phase of work, in collaboration with laboratory scientists, illustrating the intended use of automatic content as a foundation for high-quality manual additions.Documentation and examples It was suggested that a more explicit delineation of the goals of RAASWiki would be useful to users and contributors. We have taken a first step here by placing some introductory content on RAASWiki's front page, and in a new 'about' page. However, it will be necessary to 'flesh out' a small subset of topics in each type, on selected genes and publications for example, in order to suggest the type of content envisaged. This will involve some collaborative effort with laboratory scientists, but with more exposure it is our hope that community annotation will build on the foundation present, moving RAASWiki beyond a set of aggregated of data. This process, from a foundation of automatically derived data to a collaboratively annotated resource, is also the scenario in which we would expect other users to employ BioKb.• Further data types One comment made was that the highly focused nature of this type of wiki makes it an ideal place to store laboratory-related data difficult to find elsewhere. An example is animal models, which are already present, but could perhaps be more extensive, and play a more central role in the resource. Other topic types are under consideration- for example for system-specific protocols and reagents. Addition of such data, together with links to the relevant publications, and associated comments on usefulness, will form an important component of RAASWiki development. The expanding set of data types may have implications for the organisation of topics, as represented on the 'Browse' page of RAASWiki, and possibilities for reorganisation will be explored.• For the creation of a set of base data for a comprehensive knowledgebase on the renin-angiotensin-aldosterone system, we developed a software utility that may be used to automatically seed a wiki with easily available information on molecular biology, references, and disease associated with a given biological pathway. We present here both this software utility and one outcome from it: the RAASWiki knowledgebase, and it is our hope that other biological researchers can make similar use of BioKb. Through the assistive functions of BioKb we aim to expand the content of RAASWiki to provide a comprehensive resource on this clinically very significant system.The type of wiki we have discussed is very focused: BioKb is designed to allow semi-manual annotation centred around a very specific biological system, tuned to the needs of individual groups of researchers. This approach is complementary to large-scale resources covering, for example, the entirety of known proteins, pathways, or interactions, and we ancipate the two variants would be most useful in concert.• Project name: BioKbhttp://www.bioinf.mvm.ed.ac.uk/twiki/bin/view/TWiki/BioKbPlugin• Project Home page: • Operating systems: Platform independent • Programming language: Perl• Other requirements: TWiki version 4.2, TWiki plugins VotePlugin, PatternSkin, TwistyPlugin and AccessStatsPlugin (plus associated dependencies), perl modules XML::Simple, XML::LibXML, LWP::Simple, SOAP::Lite 0.60 and Clone • License: GPLDRD and JJM conceived the original ideas for this work; implementation and development was carried out by JRM, AH carried out work to supply some of the content for RAASWiki. The manuscript was prepared by JRM and DRD with input from AH. All authors read and approved the final manuscript.


A wide variety of biological data can be modeled as network structures, including experimental results (e.g. protein-protein interactions), computational predictions , or curated structures (e.g. the Gene Ontology). While several tools exist for visualizing large graphs at a global level or small graphs in detail, previous systems have generally not allowed interactive analysis of dense networks containing thousands of vertices at a level of detail useful for biologists. Investigators often wish to explore specific portions of such networks from a detailed, gene-specific perspective, and balancing this requirement with the networks' large size, complex structure, and rich metadata is a substantial computational challenge.S. cerevisiae data from bioPIXIE, E. coli data using MEFIT, and H. sapiens data from HEFalMp.Graphle is an online interface to large collections of arbitrary undirected, weighted graphs, each possibly containing tens of thousands of vertices (e.g. genes) and hundreds of millions of edges (e.g. interactions). These are stored on a centralized server and accessed efficiently through an interactive Java applet. The Graphle applet allows a user to examine specific portions of a graph, retrieving the relevant neighborhood around a set of query vertices (genes). This neighborhood can then be refined and modified interactively, and the results can be saved either as publication-quality images or as raw data for further analysis. The Graphle web site currently includes several hundred biological networks representing predicted functional relationships from three heterogeneous data integration systems: Graphle serves as a search and visualization engine for biological networks, which can be managed locally (simplifying collaborative data sharing) and investigated remotely. The Graphle framework is freely downloadable and easily installed on new servers, allowing any lab to quickly set up a Graphle site from which their own biological network data can be shared online. As the breadth, depth, and quantity of biological data has continued to grow, these data have increasingly been represented as graphs or networks for the purposes of analysis and visualization. Historically, biological networks have been used to represent the organization of metabolic pathways , proteinThe visualization of biological networks has inspired substantial research and tool development, ranging from the detailed organization of small, sparse networks as pathways  to vhttp://function.princeton.edu/graphle, showing functional relationship networks predicted for S. cerevisiae by the bioPIXIE system [E. coli by the MEFIT system [We have created Graphle as a tool to address these issues and to provide biologists with a tool for exploring large biological networks. As shown in Figure E system , for E. T system , and forT system ; a downlGraphle is implemented in Java using a client/server architecture to modularize the two main components of the system: a graph server that manages a  collection of weighted graphs and associated metadata, and a user interface client that provides an interactive visualization of portions of this data. This partitions the system to allow hundreds of gigabytes of biological network data to be managed on the server while still providing a focused, responsive user experience. The responsibilities of the graph server include accessing large amounts of graph data on disk in a query-driven manner, caching this data to improve performance, executing graph query algorithms based on client input, and providing information on genes (vertices) and underlying data (edges) as needed. The graph client must run in a web browser and provide rapid, interactive access to all data managed by the server in an informative visualization. Fundamentally, just as Google acts as a query-driven server to present an informative subset of the web, the Graphle server acts in a query-driven manner to filter and present the content of biological networks.The Graphle server is based on a Java port of portions of the Sleipnir C++ library for computational genomics  that allGraph data is stored using the Sleipnir CDat interface, and can thus be interconverted between human-readable text (referred to as the DAT format) and a compact binary (DAB) format. Graphs stored as DABs are automatically indexed and memory mapped; due to memory mapping restrictions on many platforms, an LRU cache is used to maintain a subset of currently mapped graphs. Retiring a graph from this cache, loading a new ~25,000 gene graph, and performing a complete graph query takes at most ~20 s on a modern server, most of which is spent in disk access.The graph server also maintains metadata describing graphs, vertices, and edges. Each graph is assigned to a particular organism (or other broad category) and to a "context" within that organism, where a context can be a biological process, tissue type, or other specific subcategory. Vertices are described by a unique identifier  sent to the Graphle server, which performs a configurable graph query algorithm to return the most relevant portion of the selected  complete graph. In addition to controlling which genes make up the current query, the client also provides realtime filters for vertex and edge inclusion (based on the weight of the graph's edges and the confidence with which the server indicates that vertices are included in the graph query results). The combination of these three features allows a user to fluidly and tractably navigate through large, dense, weighted graphs.The Graphle client is a Java applet designed to interactively visualize configurable subgraphs of biological networks (or other graph data) in a web browser. The client uses the Prefuse library Graphle provides a web-based system for interactively browsing large biological networks. These graphs can represent experimental results , computational predictions , or any other undirected, weighted graphs. Each underlying graph can be very large , and the Graphle server can manage hundreds of such graphs along with associated metadata . The Graphle client executes in a user's web browser and retrieves subgraphs focused on a specific set of query genes. This query and the displayed subgraph can be interactively modified in realtime, allowing a user to conveniently explore targeted subgraphs of interest extracted from the large body of underlying data.A Graphle query consists of two components: a particular underlying graph specified by an organism and biological context Figure , and oneEdge weights in biological networks often represent the strength of our confidence in an experimental outcome: greater sequence similarity, higher correlation between gene expression values, or larger probabilities of functional interactions, for example. Similarly, using the concept of guilt by association, most graph query algorithms assume that vertices more strongly connected to the query set in the aggregate are in turn more biologically related to those query genes. Correspondingly, the Graphle client allows a user to fine-tune the visualization of a queried subgraph by filtering edges by weight and vertices by score Figure ; filter A typical use of Graphle is for a biologist to investigate specific genes in a pre-existing biological network. For example, suppose a yeast biologist is interested in the roles of SAC1  and http://function.princeton.edu/graphle shares approximately 350 GB of biological networks. The process for creating a new Graphle server installation is also detailed on this web site.Conversely, a biologist who generates a large interaction dataset or a bioinformatician with predicted interaction networks can share their data online using Graphle. Particularly for higher eukaryotes with large genomes, the data for a single interaction network can be gigabytes in size; when tens or hundreds of such networks are predicted, transmitting them en masse becomes impractical. A Graphle server can be paired with any web server to share new data for collaborators to query and explore, with few limitations on graph size; the Graphle installation at http://function.princeton.edu/graphle offers graphs generated by bioPIXIE [E. coli, or HEFalMp for human data [The Graphle server organizes its collection of graphs using two biologically motivated levels of abstraction: each graph is assigned to exactly one organism and one biological context Figure . A graphbioPIXIE  in yeastbioPIXIE  in E. coman data , with coGraphle maintains arbitrary metadata optionally describing each vertex (gene) and edge in its graphs Figure . For genGraphle provides the opportunity for users to export the current subgraph as an image or as raw textual data  and visualizing them in an informative manner . Graphle allows collections of dense, weighted graphs to be stored on a server and accessed through focused queries by a web-based client. The data comprised by such graphs can range from experimental results to computationally predicted interaction networks, and Graphle allows each vertex (i.e. gene) and edge to be annotated with arbitrary descriptive metadata. A web-based client sends sets of query genes from a user to the server and interactively displays the resulting focused subgraphs, which can be manipulated in realtime and exported as data for analysis or as images for publication. The Graphle source code, documentation, and a demonstration client can be found at Project name: GraphleProject home page: http://function.princeton.edu/graphleOperating system(s): Platform independentProgramming language: JavaOther requirements: Java 1.5 or higherLicense: Creative Commons Attribution 3.0Any restrictions to use by non-academics: NoSOM and CH implemented the software. CH designed the software and drafted the manuscript. OGT coordinated the study. All authors read and approved the final manuscript.


Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a massive amount of life science data. The data is stored in numerous databases that have been established over the last decades and are essential resources for scientists nowadays. However, the diversity of the databases and the underlying data models make it difficult to combine this information for solving complex problems in systems biology. Currently, researchers typically have to browse several, often highly focused, databases to obtain the required information. Hence, there is a pressing need for more efficient systems for integrating, analyzing, and interpreting these data. The standardization and virtual consolidation of the databases is a major challenge resulting in a unified access to a variety of data sources.We present the Biochemical Network Database (BNDB), a powerful relational database platform, allowing a complete semantic integration of an extensive collection of external databases. BNDB is built upon a comprehensive and extensible object model called BioCore, which is powerful enough to model most known biochemical processes and at the same time easily extensible to be adapted to new biological concepts. Besides a web interface for the search and curation of the data, a Java-based viewer (BiNA) provides a powerful platform-independent visualization and navigation of the data. BiNA uses sophisticated graph layout algorithms for an interactive visualization and navigation of BNDB..BNDB allows a simple, unified access to a variety of external data sources. Its tight integration with the biochemical network library BN++ offers the possibility for import, integration, analysis, and visualization of the data. BNDB is freely accessible at  The development of high-throughput technologies has generated an extensive quantity of -omics data over the last decades. Despite the technological progress, improvements in the application area, e.g. in drug discovery, have failed to keep pace with increased research and development spending, as demonstrated by Nightingale et al. . One of navigators, mediators, and warehouses. The first category, navigators, is based on the idea of a navigational or link-based integration of several data sources. Such a portal normally does not integrate the data itself, but provides the user with pages navigating to external data sources. Well-established examples of portal systems are SRS [Hence, a large number of systems addressing this problem with diffierent approaches have been developed. These approaches can be classified by their architecture into three main categories : navigat are SRS , BioNavi are SRS , and Ent are SRS . A media are SRS , TAMBIS  are SRS , and Bio are SRS . Systems are SRS , ONDEX [ are SRS , cPath [ are SRS , and Bio are SRS .Based on an object-oriented data model, called BioCore, we developed and implemented BNDB, an SQL data warehouse system that integrates data sets from external and internal data sources via importers. The BioCore model allows not only for modelling of nearly all currently known biochemical processes, but also for including new biological concepts with little effort ,16. The de-facto standard. This guarantees a high portability of the biochemical network database allowing a user to create a local version of the BNDB on a wide range of platforms. Therefore, we created an object-relational mapping of the BioCore model onto a relational database management system, using only SQL2 [The BNDB is implemented as a relational database using MySQL . We decinly SQL2  compatibnly SQL2 . AdditioIn the current state, BNDB represents a comprehensive collection of biological data integrated from the following data sources:• Sequence databases: SwissProt , RefSeq • Pathway databases: KEGG , BioCyc • Protein interaction databases: DIP , MINT 2, IntAct • Transcription factor databases: TransFac For the horizontal data integration ,30 of th(1) object matching based on unambiguous external identifiers and (2) structural matching based on identical object relations.The first approach relies on the existence and correctness of selected standardized IDs in the imported databases see Fig. . Each obThe merging process itself consists of several steps: In an initial step, we merge most of the database objects by their identifiers and remove redundancy in their attributes through the first approach. Then, in the second step we collect and merge all equivalent events in BNDB through the second structural approach.A simplified example for merging genes using the first approach, is presented in Fig. For accessing BNDB we offer three different ways: a web interface, a network visualizer, and a programming interface.An intuitive web client browser enables querying and browsing BNDB. The user can search by name, description, or publication for participants, events and pathways. The user query is converted internally into an SQL query. For the standard search the user does not need to know any information about the internal structure of BNDB or its underlying data model BioCore. In addition, for more advanced users the web interface gives the possibility to perform direct SQL queries. The retrieved results are presented text- and link-based in a user-friendly way. Hyperlinks to external data sources are provided for additional information whenever external database identifiers are connected with the object , or a database with a web interface (e.g. Entrez). For the graphical representation of the networks, many of these systems use standard visualizer (e.g. Cytoscape). However, we think that the full integration of an own visualization tool facilitates the visualization and presentation of the stored data.GeneTrail [GeneTrail has been successfully applied to detect a molecular target of the antimicrobial metabolite kendomycin [We have developed several applications based on BNDB that show the usefulness of the approach, e.g. an efficient gene set analysis tool, eneTrail , which endomycin .In summary, BNDB is a comprehensive database system, which makes it not only possible to retrieve the combined information of integrated data sources in an easy way, but can also be customized and extended to meet the needs of different users.Project name: BNDB;;Project home page: Operating system(s): Platform independent;Programming language: Java; Other requirements: Java 1.6.0 or higher;Licence: GNU GPL;. The current versions of BN++ and BiNA are distributed under the GNU GPL license and available from the website .BNDB is freely accessible at BN++ Biochemical Network LibraryBiNA Biological Network AnalysisDBMS Database Management SystemNCBI National Center for Biotechnology InformationBGL Boost Graph LibrarySQL Standard Query LanguageSBML Systems Biology Markup LanguageBioPAX Biological Pathways ExchangeGEO Gene Expression Omnibus databaseAG programmed the network visualization tool. MK provided specialist knowledge on network visualization. JK, CB, and TB were involved in implementing one or more importers. JK, OK, and HPL contributed to the system design of BN++ and to the design of its data model. MK, OK and HPL supervised the project. All authors read and approved the final manuscript.DDL Diagram of BNDB. The general structure of the database schema.Click here for file


Single nucleotide polymorphisms (SNPs) are the most frequent type of sequence variation between individuals, and represent a promising tool for finding genetic determinants of complex diseases and understanding the differences in drug response. In this regard, it is of particular interest to study the effect of non-synonymous SNPs in the context of biological networks such as cell signalling pathways. UniProt provides curated information about the functional and phenotypic effects of sequence variation, including SNPs, as well as on mutations of protein sequences. However, no strategy has been developed to integrate this information with biological networks, with the ultimate goal of studying the impact of the functional effect of SNPs in the structure and dynamics of biological networks.First, we identified the different challenges posed by the integration of the phenotypic effect of sequence variants and mutations with biological networks. Second, we developed a strategy for the combination of data extracted from public resources, such as UniProt, NCBI dbSNP, Reactome and BioModels. We generated attribute files containing phenotypic and genotypic annotations to the nodes of biological networks, which can be imported into network visualization tools such as Cytoscape. These resources allow the mapping and visualization of mutations and natural variations of human proteins and their phenotypic effect on biological networks . Finally, an example on the use of the sequence variation data in the dynamics of a network model is presented.In this paper we present a general strategy for the integration of pathway and sequence variation data for visualization, analysis and modelling purposes, including the study of the functional impact of protein sequence variations on the dynamics of signalling pathways. This is of particular interest when the SNP or mutation is known to be associated to disease. We expect that this approach will help in the study of the functional impact of disease-associated SNPs on the behaviour of cell signalling pathways, which ultimately will lead to a better understanding of the mechanisms underlying complex diseases. Single nucleotide polymorphisms (SNPs), among other types of short range sequence variants . This information is documented in the biomedical literature, and it has already been recognized that text mining techniques are required to harvest it from free text. Nevertheless, much of this information is already collected in curated databases. One example is the UniProt database , to retrieve the reactions and pathways in which SOS1 participates. SOS1 is directly involved in the activation of RAS, and this in turn leads to the activation of ERK . In order to be able to use the BioPAX ontology, first the textual description of UniProt has to be mapped to the BioPAX activation reactions and then the entities have to be used to find the specific reaction. For instance the entity RAS could be mapped to the "controlled" entity in the BioPAX representation . The main difficulties here are the different levels of granularities and different perspectives used in text and pathways to describe the same process.Solving these challenges will require ontologies and the use of sophisticated text mining tools able to map information extracted from text to information represented in networks. Once the information is represented in a OWL-DL  based foIn order to integrate data about SNPs and protein sequence mutations in biological networks, we developed node attribute files for Cytoscape that allow the visualization of the data in the context of networks. The use of the node attribute files containing protein annotations allows the identification of the nodes in the network that have mutations and/or natural variations. Figure As an example, Figure Q9Y243] at position 305 is associated with a 2-fold increase in phosphorylation.The detailed information about the mutation or natural variant is stored in the node attribute "mutagenesis" or "polymorphism", and can be visualized as a bullet list by moving the mouse over their textual description . The effect of the MEK1 mutation on the dynamics of the network can be appreciated in Figure in silico, suggesting that the lack of ERK negative feedback on the receptors leads to a sustained signal. Moreover, a similar response is observed when the ErbB2 receptor is overexpressed  signalling network in MCF-7 cells published recently  was seled Figure . In thisd Figure . This efThe previous example was only chosen for illustrative purposes, to exemplify the usefulness of incorporating sequence variation data in a modelling exercise.This approach opens the possibility of evaluating the functional effect of SNPs and mutations on the structure and dynamics of network models.In this paper we have presented a general strategy for the integration of pathway and sequence variation data, towards their use in network visualization and analysis, as well as in the modelling of signalling pathways. In principle, all the data derived from UniProt could be used for this purpose, provided that the relevant models are available. Several difficulties were found when we tried to combine the data from two structured databases: UniProt and Reactome. Even though the data from these resources is already organized or structured (the entities participating in the interactions are specified) there are a lot of difficulties in the identification of the reactions and nodes in the networks that are affected by the mutation or the SNP. These difficulties go beyond tasks that any current text mining system would be able to handle, since at least NER and relationship extraction tools are required. The difficulties are mainly related with the different perspectives that can be used to refer to the same biological process and how to deal with them to map the different representations to a single concept, and also in the complexity of the processes inherent to the knowledge domain. Similar issues were also discussed in relation to the manual annotation of a corpus describing events in the field of molecular biology ,38. In tThe intended integration allowing the mapping of the phenotypic effect of SNPs on biological networks  has evident practical usefulness. The clinical phenotypic effect (e.g. sequence variation associated with colon cancer) and the functional phenotypic effect (e.g. sequence variation produces a decrease of enzymatic activity) can be evaluated in the context of the reactions and processes that are affected by the SNP. This is a very important issue as it provides information about the functional effect of mutations at the cellular level that are relevant in the clinical practice. Disease-associated variants or specific mutations of interest could be evaluated in the context of network models. Moreover, it would be possible to assess the effect of different sequence variations in the same model, an approach particularly relevant to consider the polygenic character of complex diseases. This can have significant consequences for understanding mechanisms of disease and the design of new therapeutical approaches.In this paper we have presented a general strategy for the integration of pathway and sequence variation data, towards the use of the integrated information for network visualization and analysis, and for the modelling of signalling pathways. This will aid the modellers in studying the functional impact of protein sequence variations on the model dynamics and proposing relevant experiments. This is of particular interest when the SNP or mutation is known to be associated to disease. We expect that this approach will help in the study of the functional impact of disease-associated SNPs in the behaviour of cell signalling pathways, which ultimately will lead to a better understanding of the mechanisms underlying complex diseases. was downloaded in BioPAX format level 2 from Reactome (release 27) . The pathway "Signaling by EGFR" ytoscape  and the ytoscape  does not of the UniProtKB/Swiss-Prot database using Swissknife [, release 57.2 of May 2, 2009), and added to the attribute file as an additional annotation. Annotations to NCBI dbSNP [We extracted the information of mutagenesis experiments and natural variants for all human entries of the manually curated part of UniProt resulting in 11806 entries Figure . For thiissknife , an objeissknife  databaseissknife . These mBI dbSNP  are provBI dbSNP ). Our meFurthermore, text mining techniques were applied to the text in the Description field in order to identify concepts from controlled vocabularies in the phenotype description of the natural variants and mutations. The EBIMed system  was acceAll these data were combined for the development of attribute files that can be loaded into Cytoscape allowing the mapping, visualization, filtering and searching of the SNP information in the context of biological pathways.For pathway visualization, we used Cytoscape version 2.6.0 . CytoscaWe used COPASI (version 4.4)  to modelSNP: single nucleotide polymorphism; Non-synonymous SNPs: nsSNPs; NER: Named Entity Recognition; SBML Level 2 Version 1: SBML L2 V1; GO: Gene OntologyThe authors declare that they have no competing interests.ABM designed and implemented the database, prepared all data files, developed the files for the visualization and performed the modelling. MR participated in the implementation of the database and in preparation of data files. LIF led the design and coordination of the study, participated in the implementation, evaluation and analysis of the results, and prepared the first draft of the manuscript. FS participated in the design and coordination of the work. All authors contributed to the manuscript, and read and approved its final version.Glossary and identifiers and names of proteins used in the study.Click here for fileBioPAX file of Signaling by EGFR pathway from Reactome.Click here for fileSBML file containing the original model of ErbB signaling by , slightlClick here for fileCytoscape node attribute file containing the mapping of the nodes in ErbB_signaling (SBML) to UniProt identifiers.Click here for fileCytoscape node attribute file only containing information about mutations.Click here for fileCytoscape node attribute file containing only information about polymorphism.Click here for fileCytoscape node attribute file containing information about mutagenesis and polymorphisms.Click here for fileVisual style file for SBML format.Click here for fileVisual style file for BioPAX format.Click here for fileGuide to map SNP data onto biological networks .Click here for fileExample Cytoscape session where the EGFR signalling pathway from Reactome in BioPAX format, as well as the network model in SBML format are imported and visualized with the mutagenesis and polymorphism dataClick here for fileSBML model of ErbB signalling, in which MEKstar is inactivated.Click here for file


We obtained a series of pituitary adenoma proteomic expression data, including protein-mapping data (111 proteins), comparative proteomic data , and nitroproteomic data (17 nitroproteins). There is a pressing need to clarify the significant signaling pathway networks that derive from those proteins in order to clarify and to better understand the molecular basis of pituitary adenoma pathogenesis and to discover biomarkers. Here, we describe the significant signaling pathway networks that were mined from human pituitary adenoma proteomic data with the Ingenuity pathway analysis system.The Ingenuity pathway analysis system was used to analyze signal pathway networks and canonical pathways from protein-mapping data, comparative proteomic data, adenoma nitroproteomic data, and control nitroproteomic data. A Fisher's exact test was used to test the statistical significance with a significance level of 0.05. Statistical significant results were rationalized within the pituitary adenoma biological system with literature-based bioinformatics analyses.For the protein-mapping data, the top pathway networks were related to cancer, cell death, and lipid metabolism; the top canonical toxicity pathways included acute-phase response, oxidative-stress response, oxidative stress, and cell-cycle G2/M transition regulation. For the comparative proteomic data, top pathway networks were related to cancer, endocrine system development and function, and lipid metabolism; the top canonical toxicity pathways included mitochondrial dysfunction, oxidative phosphorylation, oxidative-stress response, and ERK/MAPK signaling. The nitroproteomic data from a pituitary adenoma were related to cancer, cell death, lipid metabolism, and reproductive system disease, and the top canonical toxicity pathways mainly related to p38 MAPK signaling and cell-cycle G2/M transition regulation. Nitroproteins from a pituitary control related to gene expression and cellular development, and no canonical toxicity pathways were identified.This pathway network analysis demonstrated that mitochondrial dysfunction, oxidative stress, cell-cycle dysregulation, and the MAPK-signaling abnormality are significantly associated with a pituitary adenoma. These pathway-network data provide new insights into the molecular mechanisms of human pituitary adenoma pathogenesis, and new clues for an in-depth investigation of pituitary adenoma and biomarker discovery. Our long-term goals for this human pituitary study are to clarify the molecular mechanisms that are involved in pituitary adenoma pathogenesis and to discover tumor biomarkers. Towards those ends, a series of pituitary adenoma proteomic expression data, which include 111 proteins identified from a human pituitary non-functional adenoma tissue ,3, nine Over the past decades, high-throughput "-omic" technologies  have been used in many fields, including biology and human diseases. Relative to the traditional molecular biology methods that had been used to study the role of a single gene, single protein, or single small-molecule model, those "-omic" data have driven the rapid development of systems biology to study a multiple-factor model of disease and to address the network of interaction and regulatory events that contribute to a disease. Pathway biology, as one important component of systems biology, has been extensively developed. Omic data-based pathway biology relies on an accurate and effective pathway analysis system. The Ingenuity Pathway Analysis (IPA) system is an extensively used  pathway analysis system that includes a large-scale knowledge base . IPA can identify statistically significant signaling pathway networks by analyzing the -omic data in those numerous canonical-pathway databases.Proteomic data obtained from pituitary adenoma tissues -6 were aTable S1). A total of 106 identifiers were eligible to proceed into pathway analysis after 41 duplicate identifiers were removed from those 147 mapped identifiers. Each identifier was annotated with a Swiss-Prot accession number, gene name, protein name, subcellular location, biofunction, and potential targets of drugs .Among the 154 identifiers that represented the 111 proteins that were identified from human pituitary adenoma tissue, 147 identifiers were mapped to the corresponding molecules , except for 7 identifiers .Among those pituitary adenoma protein-mapping data, a total of 37 statistically significant canonical pathways were identified that involve the identified proteins Figure . The topFigure Table S2). The 75 identifiers are significant because they derive from a comparison of adenomas and controls. A total of 47 identifiers were eligible to proceed into pathway analysis after 28 duplicate identifiers were removed from those 75 mapped identifiers. Each identifier was annotated with a Swiss-Prot accession number, gene name, fold-change, protein name, subcellular location, biofunction, and potential targets of drugs .Among the 86 identifiers that represent the 56 DEP's that were identified from human pituitary adenoma tissues, 75 identifiers were mapped to the corresponding molecules  except for 11 identifiers .Among those pituitary adenoma comparative proteomic data, a total of nine statistically significant canonical pathways out of 19 pathways were identified that involve those DEP's Figure . The top+-transporting, mitochondrial F1 complex, beta polypeptide; 5-fold), COX6B1 , and NDUFS8 . Moreover, GPX4 (glutathione peroxidase 4) was significantly down-regulated (26-fold) in pituitary adenomas relative to controls. GPX4 plays important roles in the cytochrome c - apoptosis pathway.Figure Table S3) to most probably interfere with PKA functions; that nitration suggests that oxidative/nitrative stress signals are also involved in the regulation of the ERK/MAPK signaling system. The detailed components of the other canonical pathways that are derived from pituitary adenoma comparative proteomic data are shown in the Additional file Figure Table S3). A total of 10 identifiers were eligible to proceed into pathway analysis, except for LILRA4 and ZNF432. Each identifier was annotated with a Swiss-Prot accession number, gene name, nitration status, protein name, subcellular location, biofunction, and potential targets of drugs . Twelve identifiers that represent nine nitroproteins from human pituitary control tissue were also mapped to nine genes. Nine network-eligible identifiers proceeded to pathway analysis . Nitration usually decreases the activity of a protein.A total of 12 identifiers that represent nine nitroproteins, and three non-nitrated proteins, from a human pituitary adenoma tissue were mapped to their corresponding genes/proteins .Among those pituitary adenoma qualitative nitroproteomic data, a total of 12 statistically significant canonical pathways were identified that involve nitroproteins Figure . The topFigure Figure S3.4 shows the cell-cycle G2/M DNA damage checkpoint-regulation pathway. p53 and cdc25 B/C play important roles in this oxidative damage-induced pathway. The 14-3-3 proteins  are the important regulators in this pathway - they couple with Cdc25 B/C to participate in the nuclear export of Cdc25, and couple with Cdc2 and Cyclin B to participate in the cytoplasmic sequestration of cdc2 and cyclin B. The detailed components of the other canonical pathways that are derived from the pituitary adenoma nitroproteomic data are shown in Additional file Additional file Figure S4). The detailed components of the other canonical pathways that are derived from pituitary control nitroproteomic data are shown in Additional file Among those control pituitary adenoma qualitative nitroproteomic data, a total of 12 statistically significant canonical pathways were identified that involve nitroproteins Figure , and incThe present study, for the first time, used bioinformatics pathway analysis to reveal the significant signaling pathways and networks that are associated with pituitary adenomas; three types of proteomic data were used - pituitary adenoma protein-mapping , comparaMitochondria are dynamic intracellular organelles that are essential for cellular life, death, and differentiation, and play a central role in oxidative metabolism and apoptosis. Mitochondrial dysfunctions underlie a broad spectrum of human diseases ,8 that iPituitary adenoma protein-mapping data clearly reveal the mitochondria-related signaling pathways that include fatty-acid elongation in mitochondria, glycolysis/gluconeogenesis, and oxidative stress that function in human pituitary adenoma cells Figures  and 3. TMitochondrial dysfunction could be confirmed with a mitochondrial morphological change in a human pituitary tumor. Studies found an increased number of mitochondria, and ultrastructurally abnormal mitochondria were present in pituitary oncocytomas . Large mEvidence demonstrates that dysfunctional mitochondria, not oxygen insufficiency, cause cancer cells to produce inordinate amounts of lactic acid that impact on the treatment of cancer . Studiesin vivo formation and from in vitro environmental carcinogens, and the capability of endogenous antioxidant defense mechanisms to remove those reactive species , then there will be 7 statistically significant canonical pathways Figure  and 4 siBased on those statistical considerations, those statistically significant pathways and networks that were generated from the Fisher's exact test with a significance level of 0.05 were reasonably explained within the pituitary adenoma biological system. Therefore, four important biological systems were discovered for pituitary adenomas, including mitochondrial dysfunction, oxidative stress, cell-cycle dysregulation, and the MAPK signaling abnormality. These four biological systems provide important clues and a clear direction for our next in-depth studies of pituitary adenomas.This present study clarifies pathway networks that function in pituitary adenomas. The results demonstrate that mitochondria dysfunctions, oxidative stress, cell-cycle dysregulation, and the MAPK-signaling system are significantly associated with pituitary adenoma pathogenesis. Further experimental investigation is required to elucidate the biological consequences of those pathway networks and their relevance to the pathogenic mechanisms of pituitary adenoma. Those data could provide biomarkers, and could lead to the development of novel efficacious targets to treat pituitary adenomas.For protein-mapping analysis , the pitTable S5). Pituitary tumors (n = 15) were obtained from patients at the Emory University Hospital during transsphenoidal surgery. All tumors were microdissected and removed with a surgical microscope, rinsed in sterile saline, snap-frozen in liquid nitrogen, and stored (-80°C) until analysis. Each tumor fragment was confirmed independently by a neuropathologist as being homogenous and unadulterated by histology and immunohistochemistry prior to proteomics analysis. Eight control pituitary glands were obtained from the Memphis regional Medical Center (n = 7) and the National Disease Research Interchange (n = 1).For comparative proteomics analysis ,3, 15 piFor nitroproteomics analysis -6, the cTable S1), (ii) a comparative proteomic dataset that includes 56 DEP's that were identified with 2D gel-based comparative proteomics [Table S2), (iii) a nitroproteomic dataset that includes nine nitroproteins, and three non-nitrated proteins that interacted with nitroproteins from a pituitary adenoma [Table S3), and eight nitroproteins from a pituitary control that were identified with 2DGE-based nitrotyrosine Western blots and MS/MS [Table S4).The experimental datasets that were analyzed in this study derived from our published human pituitary adenoma proteomic data: (i) a protein-mapping dataset that includes 111 proteins that were identified with two-dimensional gel electrophoresis 2DGE) and MS [DGE and Moteomics   proteomics server http://www.expasy.org. All Swiss-Prot accession numbers in combination with gene names were input to the IPA data upload workflow to generate the final mapped list for next-step analysis.The SwissProt accession number and gene name were used as the identifiers of each proteomic dataset. Each dataset was saved as an Excel file. Each proteomic dataset with identifier (.xls file) was input into the IPA analysis system Table S1-S4); and the fold-change . The name of each molecule  appears in the pathway network nodes.The dataset, including mapped IDs (protein and gene), was saved, and automatically generated five subdatasets, including the All IDs , Unmapped IDs , Mapped IDs , Network-eligible IDs (= Mapped IDs - Duplicated IDs), and Functions/Pathways/List-eligible IDs. For the duplicate IDs for the same protein/gene, the identifier with the highest fold-change was used in the pathway analysis; or, the first instance of the protein/gene was used in the pathway analysis in the absence of an expression value such as mapping proteomic data and nitroprotein data. Each subdataset contained ID, notes, molecules, description, location, type of biofunction, and drugs  with the Ingenuity Pathways Analysis Knowledge Base (IPAKB). IPAKB is a curated database that contains (i) numerous scientific findings  that are extracted from hundreds of thousands of journal articles, textbooks, and other data sources, and (ii) many canonical pathways  that are constructed from those scientific findings . The sigACTH: adrenocorticotropic hormone; ARE: antioxidant-response element; CDK: cyclin-dependent kinase; CDKI: cyclin-dependent kinase inhibitor; DEP's: differentially expressed proteins; 2DGE: two-dimensional gel electrophoresis; EpRE: electrophile-responsive element; ERK: extracellular signal-regulated kinase; FSH: follicle-stimulating hormone, or follitropin; FSHRH: follicle-stimulating hormone-releasing hormone; GH: growth hormone; IPA: Ingenuity Pathways Analysis; IPAKB: Ingenuity Pathways Analysis Knowledge Base; JNK: c-Jun N-terminal kinase; Keap1: Kelch-like ECH-associated protein 1; LH: luteinizing hormone, or lutropin; LHRH: luteinizing hormone-releasing hormone; Maf: musculoaponeurotic fibrosarcoma; MAPK: mitogen-activated protein kinase; MAPKK: MAPK kinase; MAPKKK: MAPKK kinase; MS: mass spectrometry; MS/MS: tandem mass spectrometry; NF-κB: nuclear factor kappa B; NO: nitric oxide; Nrf2: nuclear factor E2-related factor-2; PKA: cyclic AMP-dependent kinase; PRL: prolactin; PTTG: pituitary tumor-transforming gene; RKIP: raf kinase inhibitory protein; RNS: reactive nitrogen species; ROS: reactive oxygen species; SOD: superoxide dismutase; TSH: thyrotropin-stimulating hormone, or thyrotropin.The authors declare that they have no competing interests.XZ conceived the study design, performed experiments, analyzed the data, and wrote the manuscript. DMD oversaw the experimental results, and reviewed and modified the manuscript. All the authors have read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/3/13/prepubSupplementary Tables. This file contains supplementary tables S1-S5. Supplementary Table S1 shows the protein-mapping data identified from a pituitary adenoma tissue with two-dimensional gel electrophoresis (2DGE) and mass spectrometry. Supplementary Table S2 shows comparative proteomic data identified from human pituitary adenoma tissues with 2DGE-based comparative proteomics. Supplementary Table S3 shows nitroproteins, and proteins that interact with nitroproteins, identified from a human pituitary adenoma tissue with nitrotyrosine affinity enrichment and tandem mass spectrometry. Supplementary Table S4 shows nitroproteins identified from human pituitary post-mortem control tissue with 2DGE-based nitrotyrosine Western Blot and tandem mass spectrometry. Supplementary Table S5 shows clinical and pathological characteristics of pituitary adenomas and controls used in comparative proteomics study.Click here for fileSupplementary Figures. This file contains supplementary figures S1-S4. Supplementary Figure S1 shows significant canonical pathways that are involved in human pituitary adenoma mapping proteomic data. Supplementary Figure S2 shows significant canonical pathways that are involved in human pituitary adenoma comparative proteomic data. Supplementary Figure S3 shows significant canonical pathways that are involved in human pituitary adenoma nitroproteomics data. Supplementary Figure S4 shows significant canonical pathways that are involved in human pituitary control nitroproteomics data.Click here for file