How to efficiently integrate the daily practice of molecular biologists, geneticists, and clinicians with the emerging computational strategies from systems biology is still much of an open question.We built on the recent advances in Wiki-based technologies to develop a collaborative knowledge base and gene prioritization portal aimed at mapping genes and genomic regions, and untangling their relations with corresponding human phenotypes, congenital heart defects (CHDs). This portal is not only an evolving community repository of current knowledge on the genetic basis of CHDs, but also a collaborative environment for the study of candidate genes potentially implicated in CHDs - in particular by integrating recent strategies for the statistical prioritization of candidate genes. It thus serves and connects the broad community that is facing CHDs, ranging from the pediatric cardiologist and clinical geneticist to the basic investigator of cardiogenesis.This study describes the first specialized portal to collaboratively annotate and analyze gene-phenotype networks. Of broad interest to the biological community, we argue that such portals will play a significant role in systems biology studies of numerous complex biological processes.http://www.esat.kuleuven.be/~bioiuser/chdwikiCHDWiki is accessible at  Recently, Wiki technology - inspired by the well-known Wikipedia encyclopedia - has been proposed as a potential strategy for the collaborative development of biological knowledge bases -6. AlthoGoing beyond knowledge gathering, integrative data analysis strategies have been proposed recently for the prioritization of genes potentially involved in a given biological process, phenotype, or disease -9. NeverWe therefore propose to combine Wiki technology, databases of genomic and phenomic information, and data analysis tools into a Wiki portal that supports the needs of a specialized community. In particular, we describe a Wiki portal for the genetic study of congenital heart defects (CHDs), termed CHDWiki. CHDs are the major cause of mortality in newborns in the developed world, but despite this manifest importance, most CHDs still have unknown etiologies. In some instances, specific genetic and environmental factors have been shown to cause CHDs. A review of the etiology of CHDs is available through CHDWiki .The CHDWiki portal focuses on mapping out the gene network leading to human CHD phenotypes. It supports both genetic and molecular biology research that aims at hunting for CHD genes, as well as clinical research that aims at identifying and interpreting genetic aberrations in patients suffering from well-characterized CHDs.in silico predictions, and the functional studies  described in the study.To build a set of most currently known gene-phenotype links, OMIM (Online Mendelian Inheritance in Man) and MEDLINE were manually searched by an expert in the field for genes that are linked with any of 139 relevant cardiac defect phenotypes listed among the internationally used CHD codes from the Association for European Paediatric Cardiology (AEPC). The use of this specialized ontology maximizes the relevance of the collected information to the CHD community and improves the consistency of this information. Relevant genes and mutations were selected and their corresponding cardiac phenotype were manually gathered and described based on the available literature. The level of support for a gene-phenotype link was defined by its incidence and the number of independent publications reporting it. We only considered such links confirmed if at least two reports from independent groups described the incidence of CHD in patients with a mutation to be greater than 1%. Moreover, the support for the link between every single gene mutation and CHD type was further characterized based on the genetic evidence (inheritance and incidence), et al. [To build a set of most currently known chromosomal regions linked to CHDs, MEDLINE was searched for imbalances detected by molecular karyotyping, breakpoints of balanced chromosome aberrations or regions implicated in CHDs through linkage studies. These data complement at a much higher resolution the CHD regions identified by Van Karnebeek et al. , which wCHDWiki is conceived to allow straightforward inclusion of published and unpublished data from all collaborators. All clinical (cardiac and non-cardiac) and molecular data are rendered anonymous and collected in a standardized manner, either directly in the CHDWiki database  or  using another tool designed for this purpose, CHDBench . ConsentCHDWiki is based on the MediaWiki engine initially developed for the Wikipedia project. We implemented a generic extension that allows registering specific components for the management of structured data and for the on-the-fly execution of analysis tools.The benefits of databases are manifold and become apparent when providing different views on the same data. For instance, it allows providing the detailed list of genes linked to CHDs, as well as the list of CHDs having linked genes. Databases also solve consistency issues; for example, when a link is added or updated between a gene and a specific CHD, both the gene page and the CHD page instantaneously reflect this change. The principles of the generic extensions are the following. Specific components can subscribe to pages so that the Wiki engine executes these components when the page is rendered, or they can explicitly be called from within the page through the use of a specific tag.JAG1, the generic extension first calls the chdsForGenes component with parameter JAG1 to retrieve the data in the form of  pairs. Second, the extension retrieves the chdsForGeneTemplate layout template, which is stored as Wiki text in a standard Wiki page. Third, it replaces the variables by their actual values. Eventually, the resulting Wiki text is rendered by the MediaWiki engine.For example, to include the list of CHDs linked to The simplicity of the generic extension mechanism makes it both flexible and powerful. Specific components already available include: numerous data retrievers from our local databases; a chromosome map summarizing genes and genomic regions linked to CHDs; pie chart generation; gene network visualization and exploration; and candidate gene prioritization. This variety of components illustrates the versatility of the approach. For instance, pie charts are easily included by calling the lightweight pieChart component with the list of slices , while the prioritization component consists in a complete web application specifically tuned towards prioritizing CHD genes. More generally, the generic extension proved successful in the fast development of new components working as wrappers for databases, web services, command line tools, and DAS (Distributed Annotation System) servers . Also, tIn addition, CHDWiki interacts with a patient data repository, CHDBench , for manWhen more Wiki portals such as CHDWiki are available, the problems of interoperability of these systems and integration of stored knowledge can be managed through standard protocols such as DAS for data access, web services for the programmatic use of knowledge, or dedicated application programming interfaces (APIs), which will have to be further specified by the community. The component-oriented architecture of CHDWiki will make such future developments easy to implement.The authors had full access to the data and take responsibility for its integrity.The results of the knowledge acquisition are described in Tables For each gene or phenotype, CHDWiki provides a pie diagram that graphically represents the spectrum of related cardiac phenotypes or mutated genes, followed by a detailed overview of the studies defining the mutational spectrum of these gene-phenotype links. Moreover, to get an intuitive view of the mutation data, a graphical map of all proteins is automatically produced and updated as new genotypes are entered, displaying the position of coding mutations in the context of the protein domains. The relevance of this display is highlighted by the significant clustering of missense mutations in annotated domains . A structured Wiki allows the integration of all these data into a view that is centered on the needs of a community. A large number of bioinformatics analysis methods are available, yet a significant gap remains towards their integration within the daily practice of most biologists. By tightly integrating a knowledge base with dedicated analysis tools, a Wiki portal provides a natural stepping stone for biologists to use advanced data analysis techniques. While the development of data analysis portals has been a long-standing aim of bioinformatics, few convincing applications with a clear impact on biology have been demonstrated since the development of homology-based methods ,25. We bIn the presented CHDWiki, we compiled all information available in the literature on CHD genetics to construct a collaborative portal. We thus introduce the first example of a new type of literature review, which is dynamic and evolving; it readily provides researchers, geneticists, physicists and cardiologists with direct access to the most comprehensive knowledge on the genetics of a particular disease. An immediate impact of this methodology is how it complements and enhances the process of reviewing the literature. Wikis are clearly dynamic in nature, and can accompany a review article so that information can be kept up-to-date after the publication of the original review. A second advantage is that they cross-link interpretation and actual data: browsing such a review provides a straight link into relevant data sources . Querying the genome on the other hand directly links into an integrated and up-to-date review. A third advantage is obviously their collaborative nature, where the expertise of numerous researchers can be pooled together to obtain extensive peer-reviewed knowledge.The impact of disease-oriented Wikis is quite significant for both clinicians and researchers. For clinicians, it allows them to find out instantly if a gene or region has been linked to the phenotype of their patients. For instance, the finding that a chromosomal imbalance in a CHD patient affects an annotated CHD gene or region provides conclusive evidence for its causality, thus facilitating diagnosis and therapy. For the researcher, several novel regions recurrently linked to CHDs in patients emerge from the compiled genomic data, such as 1p36.3, 6q21, 15q26, 16p13.3, 17q21.3, and 22q12 . Another example is from Wikiproteins, which aims at being exhaustive about available protein knowledge, where the GATA4 entry currently links to several species and provides general information  but where its link to CHDs  is briefly and incompletely mentioned.Wiki portals also perfectly fit with the philosophy of DASs. Not only can the data of any gene - or genomic feature - be distributed among portals and collaboratively annotated, but the genome browser can also serve as an entry point to the Wikis. CHD researchers who enable the CHDWiki tracks in a genome browser directly visualize any information compiled in CHDWiki.An important asset of specialized Wiki portals is that they could be a solution to the long-term maintenance of biological knowledge bases. Such long-term maintenance is a major recurring issue for the biological and bioinformatics community because of the persistent lack of long-term funding. We envisage a model for community-driven knowledge bases where the original portal development is funded through a classical grant, but where the curation of the knowledge base is gradually shifted from the core development team to the community at large over the period of the grant (3 to 5 years). Data curation should be included in the original development cycle until a critical mass of information is reached that guarantees adoption by the community, as has been done for CHDWiki (described in the knowledge acquisition section). After curation has been shifted to the community, technical portal maintenance can be minimal.Although such collaborative tools can easily exchange information and queries over the web, there would remain a significant risk of fragmentation if no protocols and strategies were available to effectively exchange information among the myriad of tools that are currently emerging . To satisfy the need of standardized and unified web accessible databases allowing simple data exchange, several bioinformatics initiatives have recently emerged promoting the Semantic Web . IndeedIn biology, the advantages of the Semantic Web are obvious: unique names for biological entities and consistent standards for knowledge representation, retrieval, and processing. Such standards simplify integration of web resources as they can be queried the same way. Bioinformatics query systems in diverse fields have started to use this technology: examples include WikiPathways, a wiki dedicated to the precise collaborative annotation of metabolic pathways , integraAlthough CHDWiki is not directly compatible with the Semantic Web approaches, it was implemented using a standard nomenclature for all terms , thus allowing straightforward transfer of its content to the new semantic web standards . Moreover, the CHDWiki DAS server already allows users to integrate the CHDWiki data in the EnsEMBL genome server. An important further development of our platform in this respect will be the implementation of web services following current semantic web standards. Indeed, some recent initiatives  now allow developers to combine Wiki and semantic web technologies.Wikis also provide an effective solution to the enduring problem of unpublishable or negative results . These can be highly valuable for other research teams pursuing similar paths of investigation, or contain relevant information below statistical significance. Wiki portals like CHDWiki are a natural repository for such findings. Also classic collaborative features, such as a mailing list  and a directory of researchers, are basic but valuable tools such a portal can offer.A number of technical and social issues may require significant further technology developments. The first is access control. Given the functionalities provided by a Wiki portal, it comes close to a collaborative laboratory notebook. However, research teams will understandably be reluctant to share their experimental data prior to publication. Access control restrictions  will require some further development of the Wiki platforms, although this is basically a technical problem that is routinely solved in database systems.A second issue is quality control. Wikipedia is based on a model of peer-editing, which is generally effective but can sometimes lead to conflicts between contributors (edit wars). Such conflicts are likely to arise when conflicting scientific hypotheses and interpretations collide. Additionally, conflicts are also likely regarding scientific attribution and priority. For smaller Wikis, a 'benevolent dictator' who oversees any conflict can solve those problems. For larger Wikis, a hierarchy of editorial roles  might become necessary.A third issue is credit assignment. Scientific credit is an essential driver and one cannot expect researchers to contribute a significant proportion of their time if no credit is attributed to them. For smaller Wikis, citation of a key publication describing the Wiki will be sufficient  because key contributors will be among the authors. For larger Wikis, a direct crediting system might be difficult to establish. Significant contributions to Wikis (as contributor or editor) should be recognized as relevant scientific work and therefore appear in the track record of a researcher. Related to the issue of credit is the issue of citation. As some Wiki entries may become key scholarly references, proper citation to accurate dates or versions might be needed in scholarly work because entries change over time.Regarding software development, each knowledge portal is likely to have highly specific aspects  that make the development of a single off-the-shelf solution (similar to MediaWiki and other Wiki software) highly unlikely. A more realistic solution will lie in the development of generic tools that enable the flexible construction of such Wikis by embedding generic modules for ontology management, XML data representation, visualization, database query, and data analysis. The goal of such software will be to maximally speed up the development of Wikis while minimizing functional constraints. The current version of our Wiki framework has been made available as an open source project .The future we envisage is one where a specialized community 'swarms' around a Wiki portal that provides most of the knowledge, data, and analysis tools needed to support its experimental work. Via this portal, the community can collaboratively and incrementally chart complex networks involved in biological processes, phenotypes, and diseases. Collaboration and efficient access to knowledge, data, and tools will significantly speed up experimental research.CHDWiki is accessible at . Data caAEPC: Association for European Paediatric Cardiology; CHD: congenital heart defect; DAS: Distributed Annotation System.The authors declare that they have no competing interests.RB, JB, BT and SB participated in the design of the study and drafted the manuscript. KD and YM conceived of the study, participated in its design and coordination, and drafted the manuscript. All co-authors contributed to various aspects of the platform development: Wiki development , Gene networks , genotype-phenotype relations , Bench , and gene prioritization tools . Knowledge acquisition was performed by JB, BT, KD and MG. All the co-authors listed above fulfill the three requirements for authorship defined by the ICMJE guidelines.P-values: NKX2-5, 1 × 10-3; TBX5, 0.05; across all nonsydromic genes, 1 × 10-4). This finding is independent of the ascertainment bias associated with preferential classification of mutations affecting protein domains as pathogenic: missense mutations identified through linkage analysis in multiple individuals similarly affect preferentially protein domains . This graphical representation moreover enables straightforward genotype-phenotype correlations: missense mutations causing atrial septal defects are preferentially affecting the homeobox domain .A figure of nonsense mutations encoded in the CHDWiki for the proteins NKX2-5 and TBX5. Displayed here are mutations in NKX2-5 and TBX5 present in CHDWiki. Missense mutations (asterisks) are significantly enriched in functional domains (Click here for fileA figure showing a network with genes sharing syndromic cardiac phenotypes when mutated. Network with genes (nodes) sharing syndromic cardiac phenotypes when mutated (edges). As members of the RAS-MAP kinase pathway  clearly form a phenotypic cluster, they seem to be involved in the same developmental cardiac cell lineages.Click here for file


The strength of the rat as a model organism lies in its utility in pharmacology,biochemistry and physiology research. Data resulting from such studies is difficultto represent in databases and the creation of user-friendly data mining tools hasproved difficult. The Rat Genome Database has developed a comprehensive ontology-baseddata structure and annotation system to integrate physiological data along withenvironmental and experimental factors, as well as genetic and genomic information.RGD uses multiple ontologies to integrate complex biological information from themolecular level to the whole organism, and to develop data mining and presentationtools. This approach allows RGD to indicate not only the phenotypes seen in a strainbut also the specific values under each diet and atmospheric condition, as well asgender differences. Harnessing the power of ontologies in this way allows the userto gather and filter data in a customized fashion, so that a researcher can retrieveall phenotype readings for which a high hypoxia is a factor. Utilizing the same datastructure for expression data, pathways and biological processes, RGD will providea comprehensive research platform which allows users to investigate the conditionsunder which biological processes are altered and to elucidate the mechanisms ofdisease.


Biological signaling pathways that govern cellular physiology form an intricate web of tightly regulated interlocking processes. Data on these regulatory networks are accumulating at an unprecedented pace. The assimilation, visualization and interpretation of these data have become a major challenge in biological research, and once met, will greatly boost our ability to understand cell functioning on a systems level.To cope with this challenge, we are developing the SPIKE knowledge-base of signaling pathways. SPIKE contains three main software components: 1) A database (DB) of biological signaling pathways. Carefully curated information from the literature and data from large public sources constitute distinct tiers of the DB. 2) A visualization package that allows interactive graphic representations of regulatory interactions stored in the DB and superposition of functional genomic and proteomic data on the maps. 3) An algorithmic inference engine that analyzes the networks for novel functional interplays between network components.SPIKE is designed and implemented as a community tool and therefore provides a user-friendly interface that allows registered users to upload data to SPIKE DB. Our vision is that the DB will be populated by a distributed and highly collaborative effort undertaken by multiple groups in the research community, where each group contributes data in its field of expertise.omics data. The integrated capabilities of SPIKE make it a powerful platform for the analysis of signaling networks and the integration of knowledge on such networks with  Our realization of the complexity of signaling networks that regulate cellular physiology is growing commensurate with the rapid growth in biological knowledge. It is now clear that biological pathways that govern cellular development and responses to environmental challenges are not linear, parallel and independent, but rather form an intricate web of interlocking processes tightly controlled by various logics of positive and negative feedback loops ,2. GivenBiological KBs are also becoming essential to the analysis of data obtained by high-throughput functional genomic and proteomic technologies. For example, when the effect of a certain perturbation on the cellular transcriptome is examined, hundreds of genes typically respond. One way to understand the biological meaning of the observed response is to systematically integrate these results with current biological knowledge and then search for pathways that are significantly enriched for responding genes. GenMapp , KEGG 55, and Cyomic' datasets. SPIKE's DB already contains extensive and highly curated data on signaling pathways in human cells related to DNA damage and other stress responses, cell cycle checkpoints, apoptosis, and innate immunity signaling, in addition to data from massive screenings for human protein-protein interaction. The main feature that distinguishes SPIKE from other extant signaling KBs is its design and implementation as a community tool. Our vision is that the manually curated tier of the DB will be populated by a distributed and highly collaborative effort undertaken by multiple groups in the research community, where each group contributes data in its field of expertise. To meet this goal, both the modeling scheme used in SPIKE for the representation of signaling pathways and the process of data upload by end users are kept very simple, making SPIKE a highly flexible and agile platform for storing, sharing and analyzing data on cellular networks.We are developing SPIKE  as a tool to help researchers integrate, visualize, interpret and share existing and novel information on cellular signaling networks, and to boost the biological interpretation of wide-scale 'omic datasets  A database (DB) of biological signaling pathways with an interface that supports data uploading and querying by multiple end-users. Carefully curated information from the literature as well as data from large public sources constitute distinct tiers of the DB. SPIKE DB is implemented using MySQL as the DB management system (DBMS). 2) A Java-based visualization package that allows interactive graphic representations of regulatory interactions stored in the DB, dynamic layout and navigation through the networks, and superposition of high-throughput genomic and proteomic data on the displayed maps. 3) An algorithmic inference engine that analyzes the networks for novel functional interplays between network components, and enhances the analysis of SPIKE uses a formal modeling scheme in which information on signaling pathways is summarized in a structured language amenable to computerized manipulation and analysis. The scheme is based upon three fundamental requirements: First, it should be of sufficient expressive power to capture information on most aspects of regulatory pathways. Second, it should focus on signaling rather than metabolic pathways, and on the regulatory interplays maintained between network components . Third, the scheme should be kept simple in order to ease data input by the SPIKE user community. Such input is crucial as our long-term goal is to have the manually curated tier of the DB populated primarily by the users' community.'entity-relationship' scheme over a 'state transition' one. The 'state transition' scheme regards different post-translational modified versions of a protein as separate entities (or as different 'states' of a protein), visually represents them as distinct nodes in the map, and seeks to trace the transition between these states. The 'entity-relationship', on the other hand, does not distinguish between different modified states of a protein and does not look for such state transitions; it focuses, rather, on the regulatory effects maintained between different proteins within a signaling network . An entity-relationship scheme  an 'entk Figure . In a st by KEGG ,15) is mbiological entities" and "relations".The entity-relationship scheme we designed for SPIKE is based on two basic types of objects: "SPIKE's modeling scheme comprises four types of biological entities:Genes (and their respective RNA and protein products) are the primary atomic elements of the scheme. To avoid ambiguities, only characterized human genes that are assigned a formal designation by the Human Genome Nomenclature Committee (HGNC) are included in SPIKE's gene space. These objects are uniquely identified by their Entrez Gene IDs . For simA family is a group of isoform genes (encoded by distinct genomic loci) with high sequence homology and whose encoded proteins share most of their biological activities. Well known examples are the JNK family, which includes the JNK1, JNK2 and JNK3 proteins , and the p38 family, which is comprised of four p38 isoforms .A complex is a group of proteins (or protein families) that carry out a specific function only when associated with their complex mates. An example is the DNA-damage sensor MRN complex, which is composed of the proteins MRE11, RAD50 and NBS1 . The com++, etc. Signaling molecules in SPIKE are identified using their ID in the ChEBI DB [This entity enables the modeling scheme to include descriptions of regulations involving small signaling molecules such as GTP, cAMP, CaChEBI DB .In contrast to genes/proteins and small molecules, no controlled nomenclature is available for designating families and complexes. For these entities we use the most common name in the literature.relations. The scheme defines three types of relations:The second type of object in our modeling scheme is Every regulation in SPIKE is defined by a triplet of the following simple form: Source, Regulatory effect, and Target. For example, "ATM activates TP53", and "CDKN1A inhibits the CDK2-CCNE complex". A regulatory interaction can be defined between any two biological entities, or between a biological entity and another regulation. A regulation can have one of three regulatory effects: "promote/activate", "inhibit/repress", or "unknown". Each regulation  is associated with several attributes: the biochemical mechanism by which it is driven , one or more supporting references, quality level and status flag for quality control (see below), and the submitter/data source.physical target, which specifies the physical entity on which the biochemical interaction is exerted , supporting reference(s), quality and status flags, and the submitter/data source.Much data exist on pairs of proteins that are known to physically interact, but whose regulatory effect is not yet understood. This type of relation is used to define the relationship between families/complexes and the members they contain.SPIKE's modeling scheme is intentionally simple, facilitating easy input to the DB by many end-users. Despite its simplicity, the scheme can describe most key aspects of signal transduction pathways. However, at this stage it deliberately does not account for metabolic reactions, splice variants, cell types and cellular compartments, or for any quantitative aspects of regulations. It also deliberately avoids distinction between a gene and its RNA and protein. SPIKE focuses on regulatory effects maintained between components within signaling networks. We found that for our goals it is not essential to explicitly model the layer in which the regulatory effect is exercised . However, information on the specific biochemical mechanism is stored in the DB, being one of the attributes attached to each regulation. While SPIKE's modeling scheme can accommodate data on any signaling pathway from any organism, at present the implemented DB supports data obtained only in human cells .Despite these limitations, SPIKE's design is flexible enough to allow its adjustment to support many of the above features in the future, as biological knowledge expands and standard ontologies and nomenclatures become widely accepted.SPIKE DB contains data from three sources: 1) relations uploaded manually by SPIKE curators and users; 2) relations imported en masse from external signaling pathway DBs . Additionally, the SPIKE XML serves as the primary import/export format in which all the information stored in the SPIKE DB is available. Consequently, this information can be electronically manipulated by any interested party and may be readily incorporated into other external KBs. To facilitate data exchange, we implemented a program that converts SPIKE data to the standard BioPAX format (BioPAX level 2 ). The coThe pathway visualization module allows dynamic network representation of selected biological data stored in the DB and gradual navigation through it. Researchers can manipulate the network map interactively, control its layout, expand or collapse selected segments, and retrieve further information on both biological entities and regulations. SPIKE's four types of biological entities are displayed in the maps using nodes of different colors Figure . AlthougSalient features implemented in SPIKE's visualization module include:• Dynamic navigation and expansion of maps. Double clicking on a node leads to the addition to the map of all its direct neighbors meeting the quality-level threshold set by the user.• Retrieving further information on genes and regulations. Clicking on a gene's pop-up menu opens its Entrez-Gene web page. Clicking on an edge's pop-up menu allows opening abstracts of the supporting citations from PubMed. In addition, a properties description panel displays more details about each object, in addition to the information supplied in the map itself .• Data filtering. A filtering mechanism allows users to display only data filtered according to different parameters, such as status flag, quality level, data source, and data owner.• Graph layout. Automatic and manual layout mechanisms can be used to facilitate organization of the maps. Edges can also be moved and manipulated by the user to improve the layout of the map.• A 'recycling bin' enables users to temporarily hide specific nodes and edges in order to improve clarity of the displayed maps.omics data on the maps. This utility facilitates the biological interpretation of microarray and other high-throughput measurements. For example, coloring maps according to gene expression levels might point to pathways that are active in the analyzed dataset . In addition, updates to the DB are automatically reflected in the map (the existence of new regulations that involve any node in the map and that are not displayed in it will be indicated by a red dot within the node). This ensures that the maps posted on the site are continuously synchronized with the DB. SPIKE users can join the collaborative effort, building signaling maps for pathways that are in the focus of their research, and contributing these maps to the community by posting them on the SPIKE web site.omics data to identify functional modules in the network, and suggest novel regulatory interplays between components. At present, we have implemented three basic utilities:SPIKE's algorithmic engine is designed to include various graph-theory algorithms that analyze the network, characterize its topological properties, integrate This utility finds and displays all direct paths, up to a pre-defined maximal length, that connect source and target nodes specified by the user Figure . This utGiven target sets of genes/proteins provided by the user , this utility displays all the direct connections among nodes within each set  Figure .Given target sets of genes/proteins uploaded by the user, the utility searches for signaling maps enriched for nodes included in one of the target sets. Enrichment scores are calculated using the hypergeometric distribution and are computed with respect to a given background set . Maps that are significantly enriched  are listed in the results table. Clicking on a map's link in this table opens the enriched map, in which target set nodes are highlighted Figure .SPIKE can be downloaded from . It has A research group that wishes to join the SPIKE community should fill-in an online form available at SPIKE website and have the tool locally installed in its "standard installation" mode. The standard mode of installation grants the user full functionality of the tool. Mainly, it supports synchronization between the site and the central DB and can serve several users on different machines within one internal network site . It requires installation of the MySQL DB server on one machine on the site. Detailed installation instructions are provided on the website. This installation mode allows data submitted at the site to be shared with the entire SPIKE community (while retaining the option to hide some private data), and enables that site to benefit from the data submitted by other sites. The standard installation supports Windows, UNIX and Mac OS.omics' labs that strive for comprehensive, systems-level delineation of cell functioning.SPIKE provides the research community with a powerful integrated KB of signaling pathways in human cells. The tool helps researchers integrate, visualize and interpret existing and novel information on cellular networks. It can be of great utility both for labs whose research is focused on specific processes and networks , and for 'Gene/protein-centric labs can utilize SPIKE as a central repository for regulations and interactions involving genes, proteins and processes that are in the focus of their research, and greatly benefit from data uploaded by other labs in the same research field. As a pilot case, at present we are focusing our curation effort on signaling pathways that are induced by DNA damage , and are building a community of SPIKE users in the DNA damage research field. Any biologist in this community can find SPIKE very useful in addressing simple queries such as 'which proteins are directly regulated by p53?', 'is there a pathway that connects two genes whose knockdown results in a similar hyper-sensitivity to DNA damage?'. In parallel, we encourage and support the establishment of additional sub-communities that cover other aspects of cellular physiology.Labs that carry out wide-scale 'omics' experiments can utilize SPIKE's analytic and visualization features to mine meaningful biological hypotheses out of the data , and present them in lucid, aesthetic network graphs. The flexible coloring schemes implemented in SPIKE make it very powerful for this task. For example, using SPIKE we built network maps in which gene nodes were colored according to the kinetics in which they were induced in the dataset that we analyzed and according to regulatory-elements that we identified in their promoter regions ,28.SPIKE is under continuous development and we plan to enhance several functionalities in future versions. It will be upgraded to a web-based system that will enable its use over the web without local installation. It will be developed into a multiple-species KB. Visualization capabilities will be enhanced by adding hierarchical and other graph layout mechanisms. We are currently implementing an interface between SPIKE and our EXPANDER package for analysis of microarray data  aimed atProject name: SPIKEProject home page: • Operating system(s): Windows, UNIX or Mac.• Programming language: Java• Other requirements: Java 1.4 or higher, MySQL DB server• License: free for non-commercial users• Any restrictions to use by non-academics: license needed• RE, YS and RS designed the functionality of tool and coordinated its development. RV, NA, IU, IZ and JA developed the system architecture, wrote the code and tested it. NO, GS and RB implemented an earlier version of the system. RE also populated the database and performed beta testing. GM and MW are the principal curators of SPIKE. All the authors have read and approved the final version of the manuscript.SPIKE's DB synchronization module makes it possible for remote sites to receive and send updates from/to the central DB. Below we describe the logic of DB synchronization as implemented for remote sites and for the central site.genes/proteins members; a regulation is defined by its source, target and effect.A snapshot of the central DB saved in XML format on the SPIKE web site, and is updated periodically. Upon initiation of the synchronization process at the remote site, SPIKE downloads the latest version of the central DB from the web site and compares it with the local DB. All entities in the SPIKE DB are tagged with an internal accession ID. This ID is unique, yet it is possible that the same object will temporarily have different accession IDs in different DBs . Therefore, objects in the central snapshot that match those present in the remote DB are first sought using SPIKE accession ID, and then by searching for objects with identical defining components. For example: families and complexes are defined by the set of their biological entities and proceeding to matching relations. The tool identifies differences between matching objects  and handles these differences by the following logic: Each DB change that is applied by a remote site is attached with a change-id, and for each remote site there is a record (last-review-change-id) for the last change that was already reviewed by a curator at the central site. Then, if an object that is present in both DBs is attached with a change-id that is smaller than the last-review-change-id , then the object's version of the central DB prevails and overwrites the local version. Otherwise (change-id is higher than the last-review-change-id), the local version of the object prevails and remains as is.The synchronization process first handles entities and relations that are present in both the local and central DBs, starting with matching Following the handling of objects that are present in both DBs, the procedure handles objects that exist in only one of them: objects that exist only in the local DB remain as they are and objects that exist only in the central snapshot are added to the local DB. At the end of the synchronization process, the application provides the user with a summery of the updates made to the local database and creates an XML snapshot of it, which is sent to SPIKE central site. Importantly, data uploaded at the remote site and flagged as private are excluded from the snapshot, ensuring the confidentiality of such data. A curator at the central site will review the snapshot sent by a remote site and integrate it into the central DB.change-id is higher than the last-review-change-id of that remote site, or objects found only in the remote DB. The curator examines these differences and decides which to approve (possibly after some editing) and which to reject. A decision to reject may involve a discussion with the author of the change in order to understand the cause of disagreement. The status flag of changes approved by the curator is set from draft to approved. The central database is updated by the application according to the curator's decisions. When the update is completed, the last-review-change-id parameter of the corresponding remote site is updated, and a new snapshot of the central database is posted on the web site in XML format.The logic of DB synchronization carried out at the central site is similar to the one of the remote sites with the major difference that while at remote sites local DB changes due to synchronization are exerted automatically, at the central site all DB changes are presented to and manually reviewed by a curator . A typical DB synchronization takes approximately 5 minutes on a PC with at least 0.5 GB RAM.We have implemented an automatic benchmarking suit that examines proper functionality of the DB synchronization module both at remote and the central sites under different DB change scenarios as listed below.The tested scenarios for the remote site are:1. Addition of a group by the remote-site – should not be affected by synchronization2. Deletion of a group by Central – should be removed from the remote site after synchronization3. Addition of a group by central – should be added to the remote site after synchronization4. Editing of an existing group locally – should not be affected by synchronization5. Editing of an existing group by Central – should be updated at the remote site after synchronization6. Editing of an existing group both locally and by Central – should not be affected by synchronization7. Same tests for regulations and interactions.The tested scenarios for the central site are (the tests assume that the curator approved the change exerted by the remote site):1. A group deleted at the central site still exists in the remote site – the group should not be restored by synchronization2. Addition of a group by Central – should not be affected by synchronization3. Addition of a group by the remote site – should be added to the central site after synchronization4. Editing of an existing group by the remote site – should be updated at the central site after synchronization5. Editing of an existing group by Central – should not be affected by synchronization6. Editing of an existing group both by a remote site and by Central – should accept remote changes7. same tests for regulations and interactionsAs the information stored in the SPIKE database is fundamentally different from that stored in reaction-based database, the widely accepted BioPax and SBML formats were not found to be suitable for conveying SPIKE pathways. We thus developed a specialized XML schema for describing a SPIKE pathway. Importantly, the same XML format is used for all communications between SPIKE sites and for storing pathway maps. Also, the format is extremely simple and can be easily used for converting information to and from other formats. Examples of XML files can be found on the SPIKE website .SpikeDatabase> conveys general information on the version of the database from which the pathways were extracted. On the next level the following information is encapsulated into three blocks:On the top level of the XML, <SiteBlock> contains information about SPIKE sites, intended for internal use of SPIKE. Each site is represented by a single <Site> tag• <BuildingBlock> contains information about the basic entities in SPIKE.• <RegulationBlock> contains information about the regulations contained in the pathway.• <InteractionBlock> contains information about the interactions contained in the pathway.• <BuildingBlock> contains entities referring to single genes/proteins or gene/protein groups:The <Gene> entity refers to a gene/protein and is linked to an EntrezGene identifier through an <XRef> tag. Information about the graphical properties of an entity, its description and aliases are given in the <Display>, <Description> and <Alias> tags, respectively.• A <Group> entity contains information about the two groupings supported by SPIKE: complexes and families. These two groupings are distinguished by the type attribute of the entity. The members of the group are described in <Member> entities, each referring to an identifier of another entity from the <BuildingBlock>.• A <RegulationBlock> contains <Regulation> entities describing regulations within the pathway. Recall that regulations in SPIKE are directed events  as opposed to mere interactions (which are found in the <InteractionBlock>). The constituents of the regulation are referred to by the <Source>, <Target> and <PhysicalTarget> entities. Note that while the target of a regulation can be any entity from the <BuildingBlock> or from the <RegulationBlock>, the physical target and the source are always entities from the <BuildingBlock>. Literature support for each regulation entity is given in a <Reference> entity.The <InteractionBlock> is generally similar to the <RegulationBlock>. It consists of <Interaction> entities, describing an undirected connection between a pair of entities from the <BuildingBlock>, which are referred to by the <ProteinA> and <ProteinB> entities.The <As an example, below we give SPIKE XML statements for the three cartoon cases shown in Figures Figure    <Regulation id="B→C"><Source ref="B"/><Target ref=" C"/><PhysicalTarget ref="C"/>   </Regulation>   <Regulation id="B→D"><Source ref="B"/><Target ref=" D"/><PhysicalTarget ref="D"/>   </Regulation>   <Regulation id="B→E"><Source ref="B"/><Target ref="E"/><PhysicalTarget ref="E"/>   </Regulation>   <Regulation id="A--| (B→C)"><Source ref="A"/><Target ref="B→C"/><PhysicalTarget ref="B"/>   </Regulation>Figure    <Regulation id="CHK2--| p53"><Source ref="CHK2"/>      <Target ref=" p53"/>      <PhysicalTarget ref=" p53"/>   </Regulation>Figure    <Regulation id="MDM2--| p53"><Source ref=" MDM2"/>      <Target ref=" p53"/>      <PhysicalTarget ref=" p53"/>   </Regulation>   <Regulation id="CHK2--| (MDM2--| p53)"><Source ref="CHK2"/>      <Target ref="MDM2--| p53"/>      <PhysicalTarget ref="p53"/>   </Regulation>Curator's report for DB synchronization. When synchronizing the central DB with a snapshot of a remote site DB, all DB differences are reported to the curator at the central site in such a report table. The curator manually reviews it and decides which changes to accept and which to reject.Click here for file


Recent years have seen the development of various pathway-based methods for the analysis of microarray gene expression data. These approaches have the potential to bring biological insights into microarray studies. A variety of methods have been proposed to construct networks using gene expression data. Because individual pathways do not act in isolation, it is important to understand how different pathways coordinate to perform cellular functions. However, there are no published methods describing how to build pathway clusters that are closely related to traits of interest.We propose to build pathway clusters from pathway-based classification methods. The proposed methods allow researchers to identify clusters of pathways sharing similar functions. These pathways may or may not share genes. As an illustration, our approach is applied to three human breast cancer microarray data sets. We found that our methods yielded consistent and interpretable results for these three data sets. We further investigated one of the pathway clusters found using PubMatrix. We found that informative genes in the pathway clusters do have more publications with keywords, like estrogen receptor, compared with informative genes in other top pathways. In addition, using the shortest path analysis in GeneGo's MetaCore and Human Protein Reference Database, we were able to identify the links which connect the pathways without shared genes within the pathway cluster.Our proposed pathway clustering methods allow bioinformaticians and biologists to investigate how informative genes within pathways are related to each other and understand possible crosstalk between pathways in a cluster. Therefore, building pathway clusters may lead to a better understanding of molecular mechanisms affecting a trait of interest, and help generate further biological hypotheses from gene expression data. They can be applied to 1) identify differentially expressed genes, 2) discover subclasses through clustering, and 3) classify subjects into known classes. Although most of these methods either examine one gene at a time, i.e. single-gene based, or all the genes at the same time, a number of methods investigate a set of genes at a time, where the gene-set information can come from various external databases, such as KEGG [ identify discover classify2 , and Sig2 . Althoug2 . FurtherIt is well known that different pathways do not work in isolation. In fact, each pathway is part of an overall biological network. Therefore, it is natural to ask how different pathways, or gene-sets, coordinate their activities. In the context of using gene expression data to predict a trait of interest, e.g. cancer, some pathways may function in a coherent fashion whereas others may have independent functions or effects on phenotypes. Despite the importance of this topic, there is scant literature on relating different pathways. In this paper, we propose to cluster pathways that have similar effects on the phenotype of interest. Our approach is built on our previous proposal of adopting the Random Forests approach for pathway analysis . The RanThe rest of the paper is organized as follows. The detailed methodology is discussed in the Methods section. In the Results section, we demonstrate the usefulness of this approach through the application of our methods to three different breast cancer microarray data sets to uncover pathway clusters that are involved in estrogen receptor (ER) status classification. We conclude the paper in the Discussion and Conclusions sections.We first briefly review the Random Forests approach for pathway analysis . Random We can build for each pathway a Random Forest to predict an individual's phenotype based on his/her gene expression levels within this pathway. To define whether two pathways have similar effects on an individual's phenotype, we can use the pathway prediction results to define their similarities. For example, if two pathways always give the same phenotype prediction based on gene expression data in these pathways, we infer that these two pathways have similar functions. To realize this idea, we use an output from Random Forests, class votes, to define pathway distances that can be used to build pathway clusters.To define class votes, for each study subject, the proportion of votes for a specific class is recorded based on the prediction results from individual trees in the Random Forest. Therefore, every pathway defines a class vote matrix of length n by k, where n is the number of samples in the study and k is the number of classes. In case of only two classes, we can use the votes of one class to represent the confidence of each individual belonging to that particular class. For example, for subject A, if we have 0.15 for class 1 and 0.85 for class 2, that means subject A has been voted to be class 2 85% of the time. Therefore, two pathways can be thought to have similar effects on the phenotype if the class vote matrices/vectors from these two pathways are similar.Based on class votes, we propose to use Tight Clustering to infer pathway clusters.Tight Clustering is a robust method using re-sampling for clustering and pattern recognition . It findSchematic diagrams of our proposed methods are given in Figures A total of 495 pathways were used for the analysis. These pathways are wired diagrams of a set of predefined genes and molecules from KEGG , BioCart(1) A total of 151 pathways were taken from KEGG, a pathway database with the majority responsible for metabolism, degradation and biosynthesis. There are also a few signal or information processing pathways and others related to human diseases and drug development.(2) We considered 283 BioCarta pathways. Most of these pathways are related to signal transduction for human with a smaller group of metabolic pathways.(3) The 61 GenMapp pathways we used consist of more genes per set on average. There are different types of pathways such as metabolic pathways, signal transduction pathways, gene families and subcellular components.®, but they are of different versions. Consort data set was based on hgu-133 plus 2.0 with 54,613 probesets whereas the other two, LymphNode and p53 data sets [Consort [LymphNode data set consists of frozen tumor samples of 286 lymph-node negative patients who had not received adjuvant systematic treatment [p53 data set is a set of 251 frozen tissues that were sequenced for p53 [Three different breast cancer microarray data sets were used. All of these studies used Affymetrix GeneChipata sets ,11, were[Consort  data setreatment . p53 datWe chose the breast cancer data sets and ER positive/negative status (ER+/ER-) to study because breast cancer has been extensively studied in the literature and tumor samples are normally classified on the basis of ER status . A recenp53 data set is the most unbalanced among the three breast cancer data sets we analyzed, it has 213 in the ER+ and 38 in the ER- groups. For more details on why we chose this approach, see the discussion in the see Additional file To deal with the issue of unbalanced sample size between the ER+ and ER- groups we utilized weighted Random Forests. The Consort, p53 and LymphNode data sets, respectively. See Table The above data sets are available for download from the GEO website under the accessions GSE2109, GSE3494 and GSE2034 for the The library package randomForest v4.5-18 from the R program was used in our analysis  for the We considered using GO terms based enrichment analysis, but Goeman and Bühlmann  pointed p53, LymphNode and Consort data sets respectively. In each of the three data sets, three Tight Clusters were selected. These Tight Clusters are listed in Additional file Consort; Table A2 for LymphNode; and Table A3 for p53 data set. A2ii and A3i from Additional file LymphNode and p53 data sets, respectively, highly resemble each other  with keywords, like breast cancer, estrogen receptor, and progesterone receptor, of interest. We examined the top informative genes from the pathway cluster in Table From the previous section, we have seen that even though there are no overlapping genes between both "Nitrogen metabolism" and "GATA3 participate in activating Th2 cytokine genes expression" pathways with other pathways containing ESR1, they appear to form a tight pathway cluster. In order to further understand the possible crosstalk between them, we looked at HPRD and GeneGo's MetaCore. We found connections between GATA3 pathway and CARM-1 pathway from HPRD. This is illustrated in Figure To investigate the possibility of pathway crosstalk further, we searched for the shortest path between GATA3 and CA12 with other top informative genes in the network of all links in the database of GeneGo's MetaCore. This tool assists in finding regulatory paths between two or more genes of interest. The results are shown in Tables In this article, we have described a Random Forests-based approach to identify clusters of pathways sharing similar functions. Class votes measure similarity at the individual level. Using the three different breast cancer data sets to classify between estrogen receptor positive and negative status, we found that Tight Clustering for class votes yielded consistent and interpretable results. We also considered other means of measuring the similarity of class votes, such as the similarities between class votes solely by Euclidean distances, but their performance was less consistent than the methods described here. Moreover, another output, proximity matrices, for Random Forests was also investigated, but it was found to be highly correlated with the class votes . Bioinformaticians and biologists can make use of the proposed methods to discover pathway clusters, find informative genes shared between pathways and identify genes that bridge between pathways within a pathway cluster. This allows researchers to obtain results that are more closely tied to the biological mechanism of diseases and to examine pathway crosstalk.Consort, p53 and LymphNode data sets, it is reasonable to obtain different pathway clusters from them. This is because the patients were from different clinical settings. The Consort data set consists of patients from a consortium of different breast cancer studies, the p53 data set consists of patients whose tissue were sequenced for p53 and the LymphNode data set only has patients with negative lymph node status.Due to the unbalanced nature of the data sets in this study, the weighted random forests (WRF) algorithm was used. WRF seems to perform better than the alternative balanced random forests procedure. Although we are looking at ER+ vs. ER- status for the In this article, we have also demonstrated the biological relevance of our approach using PubMatrix. The number of citations for informative genes within the pathway cluster together with keywords, like estrogen receptor, is enriched compared to other informative genes of top pathways. We have illustrated the use of GeneGo and HPRD to help us understand possible crosstalk among pathway clusters. The shortest path analyses of GATA3 and CA12 show that the informative genes in pathway clusters are closer in terms of regulatory paths than those informative genes in other top pathways. Furthermore, with the aid of a network visualization tool, biologists can investigate how the informative genes are related to each other within the pathway clusters.The novel methods presented in this article were able to identify pathway clusters related to outcome of interests that are biologically meaningful. Understanding how the informative genes relate and talk with each other within pathway clusters can help generate further biological hypotheses for follow-up studies. These may be tested using other "omics" technologies, such as proteomics and metabolomics. When the outcome variable is continuous, we can employ the Random Forests Regression approach  and easiIn this paper, we have proposed one way to building pathway clusters. It might be possible to utilize output from other pathway-based methods, such as GSEA to determine the similarity in enrichment scores between two pathways and build a graph of pathway network from the calculated similarity measures. Moreover, our approach would encourage other researchers to look into new ways in building pathway clusters and bring fresh insights into microarray analysis.HP and HZ developed this new method for building pathway clusters. HP did the programming and carried out the computational work. Both authors read and approved the manuscript.This file contains supporting information for this paper. These include: a study of weighted random forests vs. balanced random forests, GeneGo MetaCore output and tight clusters and absolute differences results.Click here for fileTight Clustering results for 5, 10, 15 and 20 tight clusters.Click here for fileTight Clustering results for 20, 25, 30 and 35 tight clusters.Click here for file


Molecular interaction networks can be efficiently studied using network visualization software such as Cytoscape. The relevant nodes, edges and their attributes can be imported in Cytoscape in various file formats, or directly from external databases through specialized third party plugins. However, molecular data are often stored in relational databases with their own specific structure, for which dedicated plugins do not exist. Therefore, a more generic solution is presented.A new Cytoscape plugin 'CytoSQL' is developed to connect Cytoscape to any relational database. It allows to launch SQL ('Structured Query Language') queries from within Cytoscape, with the option to inject node or edge features of an existing network as SQL arguments, and to convert the retrieved data to Cytoscape network components. Supported by a set of case studies we demonstrate the flexibility and the power of the CytoSQL plugin in converting specific data subsets into meaningful network representations.http://www.ptools.ua.ac.be/CytoSQL.CytoSQL offers a unified approach to let Cytoscape interact with relational databases. Thanks to the power of the SQL syntax, this tool can rapidly generate and enrich networks according to very complex criteria. The plugin is available at  Molecular interactions are the focus of many functional genomics studies, and they form a cornerstone of Systems Biology research. Software to integrate and analyse these interactions and their attributes plays an increasingly important role. The most widely used open source network visualization workbench is Cytoscape . Data frThe potential of Cytoscape to integrate heterogeneous data relies heavily on its data import capabilities. Import functions for several common file types and connectivity to web services are built in. A number of third party plugins, such as BioNetBuilder , the MiSTo tackle these issues, we propose a unified solution that works with virtually any accessible relational database. The approach was implemented as a Cytoscape plugin called 'CytoSQL', which connects Cytoscape directly with any relational database through a generic method. With this plugin, users can construct and extend networks using "Structured Query Language" (SQL) syntax, a well known database interaction language.http://www.ptools.ua.ac.be/CytoSQL also offers an example relational (MySQL) database structure, along with a tutorial and a number of Python scripts to populate the example database with publicly available test case data.CytoSQL was implemented in Java as a Cytoscape plugin. It interfaces with relational databases through the JDBC driver, which offers an abstraction of most relational database systems. The required drivers for MySQL, SQLite and PostgreSQL are included in the default installation, and the online documentation explains how to configure the plugin for use with alternative drivers or drivers for other database systems. The plugin website All local database instances were running on an Ubuntu 6.06 LTS server running MySQL 5.0.22. CytoSQL and the application cases were tested with Cytoscape version 2.6.3 and earlier, on Microsoft Windows XP, Ubuntu 8.04.1 and Mac OS × 10.5.8.The CytoSQL plugin can be installed from the Cytoscape plugin manager, under the "Network and Attribute I/O" category.After installation, CytoSQL can be launched from the plugin menu and it offers access to the query window and to the database definition window.Prior to any query execution, its connection must be defined. The database definition window Figure  collectsThe main CytoSQL window Figure  lets theAfter execution of the query, the resulting rows and columns are subsequently mapped to Cytoscape network elements in a way determined by the selected mode of operation:The first and most simple mode is the creation of a new Cytoscape network through one 'SELECT' statement. Result sets of SQL statements are returned in single table format, consisting of rows (records) and columns. The plugin interface prompts the user to specify for each column whether it concerns the source node, the target node or an attribute of the source, target or edge (see documentation for more details). The Cytoscape network is immediately generated hereafter. Field types  are automatically converted to equivalent Cytoscape data types.For example:SELECT protn1, protn2, intType, confirmed FROM interactions WHERE confirmed = 'Y'This hypothetical query could retrieve two interacting nodes (protn1 and protn2), and two edge attributes for this interaction (interaction type "intType" and whether the interaction is confirmed or not).A second mode of operation enables loading new attributes for a set of selected nodes or - if no nodes are selected - for all nodes in the network. The user can put one or more 'bind' (replacement) variables (represented by question marks) in the 'WHERE' clause of the SQL query. A plugin dialog box then asks the user which Cytoscape node field(s) should replace the bind variable(s). The query will be subsequently launched for each node individually with the appropriate variable(s) replaced by the value(s) of the selected node fields.For example:SELECT protein, mass, pI FROM features WHERE protein = ?In this example, the Cytoscape node identifier may be mapped to the bind variable "?", and the query is launched once for each node, retrieving "mass" and pI" as new node attributes.In a fashion similar to Mode 2, the plugin can load new attributes for existing edges, based on one or more replacement variables in the 'WHERE' clause. Both node or edge attribute fields can be used to replace this variable, and the query will be launched once for each edge.For example:SELECT protn1, protn2, intType, score FROM predictedInteractions WHERE protn1 = ? AND protn2 = ?The query contains two bind variables, each of which is populated with a different Cytoscape attribute, in this hypothetical example the interacting node identifiers of each selected edge. The query loads predicted information  for interactions that are already present in the network.The last mode of operation is used to extend an existing network, by loading new nodes that interact with the selected nodes, together with their edges. This mode also requires the use of at least one bind variable, that inserts a Cytoscape node attribute in the 'WHERE' clause of the SQL query.For example:SELECT protn2, intType, score FROM predictedInteractions WHERE protn1 = ?This query loads new interactions that originate from selected nodes.A toy example database against which aforementioned example queries can be executed, is provided on the plugin website.To demonstrate the functionalities of the CytoSQL plugin with real data and to facilitate its comparison with alternative import options, we describe its use in a series of application cases.To demonstrate how complex networks can be rapidly reconstructed from relational databases, we installed a local instance of the UCSC  proteome browser MySQL database . Using aIn the next case study, we demonstrate the enrichment functions of the CytoSQL plugin, on a reaction network from a local instance of the Reactome database -9. The fCytoSQL is also useful for the local integration of data from remote databases. As a demonstration, we employed 'BioWarehouse', a Bioinformatics toolkit to collect biological data from multiple databases into a so-called "data warehouse" . A publiNetwork reconstruction has become a major aspect in computational analysis of biological data. This task is often dependent on data distributed in distinct databases. Correct combinatorial analysis of data residing in different sources usually requires downloading and combining each dataset, a task that may become challenging if some of these datasets are large, whereas the intersect subset that is actually required may still be small. Relational databases facilitate the task of combining data and generating subsets of data based on complex criteria. The tool presented here unifies all types of interactions with databases into a single Cytoscape plugin. Due to the query language and the generic nature of the plugin, it facilitates otherwise complex data import and export procedures and it offers a valuable complement to existing Cytoscape plugins, which are often focussed on certain data sources or on very specific applications.This ability to import data from any user accessible database simplifies otherwise extensive processing tasks for network generation, network analysis, and network attribute manipulation, to one or more SQL statements. Since SQL syntax supports data conversion through string and numeric functions, even small data manipulations can be accomplished during import. The bind variable option allows the user to incrementally extend or enrich an existing network, through any combination of node and edge attributes.  Together with the easy connection switching option a user can sequentially interconnect independent relational databases using selected attributes, allowing the user to gradually integrate various data sources while generating visual network representations.The plugin may be utilized to connect to local databases, but can also be used to connect to any relational database that the user has sufficient access permissions for. Potential disadvantages of this approach are the requirement of unwrapped SQL connections and the related security risks. For internal applications this does not pose problems, but externally only a limited number of databases are directly accessible by SQL statements. The replication or "mirroring" of data repositories may mitigate this problem and bring the full query potential within any lab. With its generic approach, this plugin complements existing plugins, but is does not replace the available plugins that are specifically designed to offer biologist access to common databases. A relevant drawback of this plugin is the fact that a user is assumed to have some knowledge of SQL. It is also essential that the user understands the structure of the database to be queried. These factors may be a disadvantage for biologists and make the tool primarily relevant for bioinformaticians. Nevertheless, with a basic understanding of the 'SELECT' statement syntax however, the user can already accomplish most network generation tasks. Right now the plugin supports storing queries. Future developments may include a graphical query builder and tools to exchange stored queries among users, in order to bring its power within reach of a larger user base.Relationships between biological data become increasingly relevant and are more and more studied with high throughput techniques. It can be expected that the amount of available relational information will grow rapidly the coming years. This plugin leverages the use of omnipresent relational data through offering generic access to this information.- Project name: CytoSQLhttp://www.ptools.ua.ac.be/CytoSQL- Project home page: - Operating system(s): Platform independent- Programming language: Java- Other requirements: Cytoscape 2.6.1 or higher- License: GNU LGPL3- Any restrictions to use by non-academics: noneKL, DTH, KVL and JH developed and tested the application, KL wrote the manuscript, GDJ, MK and EW were involved in drafting the initial concepts and provided real application cases, and AV coordinated the project. All authors read and approved the final manuscript.AdditionalFile1.pdf - Application case 1. A pdf document that lists the queries and shows the generated network of application case 1: Rapid reconstruction of complex networks.Click here for fileAdditionalFile2.pdf - Application case 2. A pdf document that lists the queries and shows the generated network of application case 2: Enrichment of existing networks.Click here for fileAdditionalFile3.pdf - Application case 3. A pdf document that lists the queries and shows the generated network of application case 3: Use of public, remote relational databases.Click here for file


This Essay discusses the role of pathways for exploratory data analysis in present-day biology. At a time when biological data are increasingly digital and thus amenable to computationally driven statistical analysis, it is easy to lose sight of the important role of data exploration. Succinctly defined over 30 years ago by John Tukey A rich source of visual material relevant to the study of biology is pathway diagrams. Pathways map our understanding about connections and processes underlying biological function. They are powerful models for exploring, interpreting, and analyzing biological datasets and provide a medium to apply Tukey's exploratory data analysis principles to the present-day study of biology . PathwayCurrently, several bioinformatics tools provide pathway visualization to support the exploration of datasets An important goal of “-omics” experiments is to generate directed hypotheses based on relatively noisy but large-scale datasets, which can then be tested in targeted experiments. In this respect, exploratory and confirmatory approaches are complementary, where applying exploratory techniques is a logical first step in the analysis Looking at hallmarks of exploratory data analysis may suggest ways that pathways can be more effectively used in data exploration. We will discuss three properties that typify both the exploratory technique and analyst: flexibility, interactivity, and effectiveness. By relating properties of exploratory data analysis to the current state of pathway analysis techniques, we hope to guide researchers in how to best utilize pathway information in exploratory data analysis and help focus future tool development towards better exploratory pathway analysis techniques.Exploratory analysis is not a linear start to end process with fixed analysis steps but requires flexibility from both researchers and tools. The decision on what will be the next step in an exploratory analysis is guided by the data and observations rather than by a predefined plan, as is the choice for the technique that is most suitable for highlighting the features under investigation. In exploratory data analysis, we look at the data from many different points of view, few of which actually lead to new or relevant observations. But knowing that a certain description of the data does not lead to a new or relevant observation is itself a step forward in the analysis. The following analogy from Tukey illustrates this:“As detective stories remind us, many of the circumstances surrounding a crime are accidental or misleading. Equally, many indications to be discerned in bodies of data are accidental or misleading. To accept all appearances as conclusive would be destructively foolish, either in crime detection or in data analysis. To fail to collect all appearances because some—or even most—are only accidents would, however, be gross misfeasance…” Thus, open-mindedness is important when using pathways for exploratory data analysis and provides software developers with both a challenge and an opportunity. It is hard to create versatile software that does not restrict researchers to a single workflow. A more generic, flexible framework to support various pathway analysis procedures would be very powerful and would provide a basis for developing new and better pathway analysis techniques. Therefore, instead of aiming for a single, isolated software package, developers should implement flexible solutions that can be integrated in a larger toolbox for pathway analysis, in which each tool provides a different perspective on the dataset. In turn, rather than depending on a single program or algorithm to produce a publishable statistic, biologists should seek tools that help comprehend the data, view it from different angles, and thereby lead to greater understanding of what's going on.Consider canonical pathways. These pathways summarize complex biological processes in a comprehensible way, however, these summaries may omit important details by grouping entities, leaving out alternative routes, and imposing artificial boundaries. By limiting analysis to canonical pathways, a researcher is less flexible, fixated on well-described knowledge, and blind to less certain, but potentially more interesting clues. Reality is much more complex than what is depicted in the typical canonical pathway, as has been demonstrated by available protein–protein interaction networks One option might be to consider canonical pathways as a starting point in the analysis, based on solid foundations from which we might explore less known but potentially interesting areas. For example, a pathway could be dynamically extended with interactions from other pathways, protein–protein interactions, or relations from literature, based on a set of entities that show interesting behavior in the dataset under investigation. In that way, the researcher can explore instances or interactions that might not be integral to the canonical pathway, but might still be relevant to the observations in the pathway. This process could become data-driven, by highlighting and filtering information that is potentially interesting based on the experimental data and context, instead of showing all available information. An analysis environment that exploits both canonical pathways and detailed interaction networks would encourage researchers to take a flexible, exploratory attitude and facilitate construction of an understandable biological story from complex data.For developers, realizing that exploratory pathway analysis tools might be used not only in isolation but also with other software and different types of data in a flexible analysis setup might guide software design and implementation. For example, providing an application programming interface (API) in addition to the user interface greatly enhances the flexibility to adapt a tool for customized analyses or to reuse components. Reusability of software components that perform common tasks and define general data models leads to more unity among pathway analysis tools. For example, a data format will be more easily adopted by other developers when an API is available to read, modify and write it. In addition, providing an API opens up the possibility for scripting to automate tasks and combine functionalities of different tools. This introduces a nearly unlimited flexibility and allows a developer to focus on the main functions of a tool and keep the user interface simple and focused, while keeping the option open for advanced users to automate and combine standard features of different tools to perform a novel type of analysis.An exploratory analysis is not an automatic process, but relies on decisions by the researcher. Where calculation or visualization tasks may fall to the computer, the researcher controls interpretation and decisions on what data should be viewed, from which angle and in which context. Graphical representations of data are important. As Tukey notes, a good visualization “forces us to notice what we never expected to see,” and “The graph paper  is there, not as a technique, but rather as recognition that the picture-examining eye is the best finder we have of the wholly unanticipated” Pathway analysis techniques that allow the researcher to explore data interactively (rather than delivering a static view) will facilitate exploration and increase the chance of finding interesting observations or patterns. There are several opportunities to improve interactivity of pathway visualizations and highlight features relevant to the question being asked while, just as importantly, filtering out irrelevant features.Geographical maps illustrate the advantages of interactivity provided by effective visualization software. Paper maps divide the world into multiple views of fixed scope and scale. You can look at a map of the complete world with limited detail or a city map without context. But paper maps are cumbersome and lack critical interactivity (folding a map doesn't count). Digital maps, on the other hand, have several advantages, such as the ability to switch scale through interactive zooming, so you can scroll the viewport to trace a possible route or track your real-time location with GPS information. The integration of information, in general, is yet another advantage, as you can add and remove layers of information on the same map. Such integrated information can be interactively queried to find a particular intersection, a high concentration of public parks, or the best route through traffic. The parallels to biological pathways are obvious and should be exploited at every opportunity in the design of pathway analysis tools. The example of traffic overlays even hints at the dynamics of biological processes, e.g., the flow of biochemistry through metabolic pathways.Developers of exploratory pathway analysis tools could borrow concepts from the analogy with geographical maps. For example, enrichment analysis techniques group genes, proteins, and metabolites at the level of pathways ranked by activity. This provides a global “world map” view, showing which pathways may be affected while discarding information about the inner workings of these pathways. This scale may hold information on how each pathway acts as a unit in a specific context and how these units relate to each other. Such relationships could include child–parent relations (glucose metabolism and fatty acid metabolism are both metabolic pathways), the flow of substances (the output of glycolysis is an input for the TCA cycle) or causal relations (the P53 pathway regulates apoptosis). In contrast to the global scale, techniques based on the constituents of pathways provide a more mechanistic “city map” view by relating data to localized interactions and reactions. Continuing to zoom to the molecular level reveals protein domains, the exon structure of splice variants, and polymorphisms. The interactive, user-directed character of exploratory data analysis imposes stricter criteria on the effectiveness of exploratory techniques. The techniques described in Tukey's textbook on exploratory data analysis are surprisingly simple and easy to apply merely with paper and pencil. This allows the researcher to take a quick look at typical questions—“could it be that…?” or “what if it is the case that…?”—without investing days of work on that single question. Effective techniques that are relatively easy to apply and work in a transparent way encourage the researcher to take a true exploratory attitude instead of following well-trod paths while ignoring side roads that may reveal unexpected but interesting aspects of the data.Of course, if the chance of finding an interesting observation in the data does not outweigh the efforts to perform an analysis technique, researchers may decide not to use the technique. This problem may be less relevant in confirmatory approaches, where investing a large effort in a single technique is often justified because the effort versus results can be weighed during planning. However, in exploratory analysis, a single technique is only a small part of the whole analysis , and the yield is often unpredictable (many clues lead to dead ends). Therefore, the acceptable maximum effort is very low, and to make pathway analysis techniques suitable for true exploratory analysis, this should be taken into account.Unfortunately, many obstacles and annoyances exist when applying current pathway analysis techniques. While modern computers allow fast data processing and visualization, there remain numerous hurdles beyond the need to install and train on multiple software packages and the need to format and reformat datasets into specific input formats. Reordering data columns might not be a major hurdle—spreadsheet software that performs this task is widely available. But mapping data to different identifier systems or applying calculations on the data is less trivial and more prone to error, often requiring specific bioinformatics skills. Pathway analysis tools should aim to remove the responsibility of data reformatting from the researcher by making tools more flexible to different types of input data or to adhere to widely adopted standards. Generic libraries and services that might assist the developer in this task are already available, such as BridgeDb The pathways themselves require library-like organization and curation. A handful of projects have undertaken the task of capturing and curating this knowledge as semantic content that is amenable to computation Another opportunity to make exploratory pathway analysis techniques more effective is to work on better integration with public data resources. Biologists create a wealth of data, which is often available in a public repository, such as ArrayExpress or GEO for transcriptomics datasets p-value threshold,” but has trouble with “show me all data related to cancer.” In an ideal situation, the data are annotated with this information, but the computer still needs to deal with synonyms or subtypes of the word “cancer.” It becomes even more complex when integrating data at the pathway level, where the researcher could ask something like “show me all studies in which MYC is activated by MAPK.” Such questions require correctly annotated pathway information and must deal with information at the semantic level (which interactions “activate”) and synonym or identifier mapping problems (which entities map to “MAPK”).Effective data integration is a significant hurdle in working with different datasets and pathways in exploratory analysis. Determining what to integrate and how to present it to the user depends on the context and the question being asked. However, this context is typically defined at the semantic level and, thus, is hard for computers to work with. For example, a computer can easily handle the command “hide everything above a certain Recent developments begin to address these issues. Ontologies help in dealing with information at the semantic level. For example, a disease ontology could tell the computer that melanoma is a subtype of cancer, and a event ontology could tell the computer that activation could include phosphorylation, translation or receptor binding interactions. Standards for ontologies, such as the OBO format, and resources that provide access to different ontologies through unified Web services Biological pathways are a powerful medium in the exploratory analysis of biological datasets, providing a conceptual framework that is familiar to biologists, visually oriented and increasingly available in digital formats that allow interactive display and analysis. By discussing properties of exploratory data analysis in the light of pathways, we highlighted several opportunities for researchers and developers to use pathway analysis in an exploratory setting. Rather than trying to provide a complete overview of pathway analysis approaches, we discussed several ideas and recent developments that lay out a path towards a powerful set of pathway analysis tools developed from an exploratory analysis paradigm. A critical recurring issue is that current pathway analysis tools are rather isolated and hard to combine within an analysis. This may discourage researchers to follow clues that require the use of a different tool to view the data from another perspective, thereby standing in the way of a true exploratory attitude. The field of exploratory pathway analysis is still in its beginning, but with focused and coordinated development, it may eventually play an important role in providing the right questions for confirmatory approaches.