The PathOlogist is a new tool designed to transform large sets of gene expression data into quantitative descriptors of pathway-level behavior. The tool aims to provide a robust alternative to the search for single-gene-to-phenotype associations by accounting for the complexity of molecular interactions.http://pid.nci.nih.gov). The tool then allows a detailed exploration of these metrics through integrated visualization of pathway components and structure, hierarchical clustering of pathways and samples, and statistical analyses designed to detect associations between pathway behavior and clinical features.Molecular abundance data is used to calculate two metrics - 'activity' and 'consistency' - for each pathway in a set of more than 500 canonical molecular pathways (source: Pathway Interaction Database, The PathOlogist provides a straightforward means to identify the functional processes, rather than individual molecules, that are altered in disease. The statistical power and biologic significance of this approach are made easily accessible to laboratory researchers and informatics analysts alike. Here we show as an example, how the PathOlogist can be used to establish pathway signatures that robustly differentiate breast cancer cell lines based on response to treatment. Recent biomedical research has made great strides in unveiling the complexity of human disease. Technological breakthroughs and innovative methodologies now allow a much more detailed account of molecular behavior. Frequently however, such studies yield a plethora of data, with results too complex for traditional analyses designed to identify single genes associated with disease.Accordingly, many researchers are employing new frameworks to understand disease. One such framework is the concept of pathways - sets of molecular interactions that progress towards a given function. Analysis at the pathway level accounts for some of the data complexity by integrating information from across the entire genome while mirroring real biological processes. Central to pathway analysis is the idea that disruption of the benign behavior of a pathway as a whole, not necessarily a single gene component of the pathway, could be the basis for disease.The potential benefits of molecular analysis at the pathway level have gained increasing recognition recently, and consequently a number of tools have been developed to visualize pathway structures .The PathOlogist is a MATLAB-based application, which can be run as a GUI in the MATLAB environment or as a standalone executable . The objective of the PathOlogist is to transform standard gene or molecule-based data into meaningful, quantifiable information at the pathway level. Our method accomplishes this efficiently using a short sequence of analytic steps designed to maximize fidelity to the original data as well as comparability across studies. The PathOlogist then provides for in-depth analysis of the calculated metrics, through data visualization and statistical tests of association.The PathOlogist is designed to analyze normalized abundance data from any gene-based microarray platform, however special features are included to accept Affymetrix data in its raw state as well. The user may upload a set of .cel files reporting probe-level hybridization readings in an arrangement specific to the microarray chip used in the experiment. These .cel files as well as a chip-specific mapping file , are the sole input to the PathOlogist necessary to carry out the process of pathway analysis. Once loaded, raw data can be summarized into probesets and normalized using the robust-multichip averaging (RMA) method developed by Irizarry et al . This mehttp://rmaexpress.bmbolstad.com/). If raw files are not available or if other platforms were used, normalized probeset-based abundance data can be loaded in the form of a textfile.Data can also be summarized before input to the tool using RMAexpress Once the data is defined at the probeset level, a unique algorithm is applied which calculates the probability that each sample is in an 'up' (highly expressive) or 'down'  state, by fitting the set of intensity readings for a probeset to a mixture of two gamma distributions . The valThe PathOlogist uses normalized expression data to calculate two descriptive metrics for each pathway selected. For this purpose, a pathway is defined as a connected set of interactions, each consisting of one or more input molecules and one or more output molecules.Currently, the PathOlogist uses the PID (Pathway Interaction Database)  as the sMapping probe-level intensity values to molecules within a pathway is accomplished using a platform-specific text file listing the Entrez gene ID associated with each probe. This data is contained within the tool for a number of commonly used platforms. An option also exists allowing the addition of new user-created mapping files, extending the tool's capabilities to virtually any platform.p(A) indicates the probability of gene A being in the 'up' (highly expressive) state. Note that for inhibitory molecules, 1-p(A) is used instead.Users can select any subset of samples and pathways to include in metrics calculations. 'Activity' and 'consistency' metrics are calculated for each pathway selected, based on the normalized expression of input and output elements for each sample. Calculations are first performed at the interaction level and then averaged over all the interactions in a pathway to generate a final pathway score. For each interaction, the metrics are calculated as defined in Figure Essentially, activity is a measurement of an interaction's potential to occur, as determined by the expression of input molecules. An activity score of '1' for an interaction indicates that all positively regulating elements are being highly expressed, while inhibitory elements are unexpressed. Consistency scores compare this potential with the actual presence of output molecules, providing an account of deviations from expected pathway logic. Since these two metrics are averaged over all the interactions within a pathway to generate scores for the pathway as a whole, alterations anywhere within the pathway will have the same overall effect.At the end of the process, each sample will have two scores describing the behavior of each pathway. These scores can then replace individual gene expression values in any desired informatics or statistical analysis.After pathway metrics have been calculated, the PathOlogist facilitates a detailed investigation of the results.A heatmap feature displays activity and/or consistency scores as a bi-dimensionally clustered heatmap. This can be used as a summary view, to quickly identify subgroups within the data. Specific subsets of pathways and samples can be selected for a more directed view as well.For a specific pathway of interest, a pathway-drawing feature generates a directed network graphic for each of the samples selected, detailing the structure and behavior of the pathway. In the graphic, metrics for individual interactions are displayed visually using node color and size. If desired, the drawing may overlay gene-specific data such as copy number alterations or methylation status. An option also exists to extend the network to include all interactions from other pathways that involve genes within the pathway of interest. Graphics for multiple samples can be compared to identify specific points of differentiation within the pathway. Clicking on any gene within the pathway will link to more detailed information, courtesy of the CGAP Gene database . The netThe PathOlogist performs statistical analyses to determine the relationship between pathway behavior and sample features such as class, survival, etc. Sample data is entered through a simple copy-paste procedure or by uploading a two-column text file. Four types of analysis are possible:finds pathways whose scores can be used to differentiate two classes of samples . For each pathway, a two-sample ranksum test is performed to evaluate the null hypothesis that pathway scores of class A and class B are samples from normal distributions with equal means and variances. Significant pathways are those for which the null hypothesis is highly unlikely, indicating that the pathway behaves differently in these two groups of samples.finds pathways whose scores correlate well with a continuous variable . For each pathway, the Pearson's correlation coefficient is calculated for the linear relationship between the set of pathway scores and the set of sample data. A p-value is then calculated for each pathway, using a Student's t distribution to evaluate the null hypothesis that the correlation coefficient is zero. Significant pathways are those which show either highly positive or highly negative correlation with the associated variable.finds pathways that influence sample survival. The set of scores for each pathway are partitioned into two groups using kmeans clustering to minimize the squared Euclidean distance between group centroids. (A minimum group size can be set by the user.) Cumulative survival distributions are calculated separately for these two groups of samples using the Kaplan-Meier algorithm, and a logrank test is performed to evaluate the null hypothesis that the two sample groups are drawn from the same population. Significant pathways are those for which pathway behavior can be used as a marker dividing samples into groups with highly differentiated survival curves.finds pathways whose molecules are the target of some alteration . Gene-specific alterations are uploaded as a matrix of logical values describing which of the assayed genes were altered in each sample. For each pathway, a hypergeometric cumulative distribution function is computed for each sample to estimate the probability that genes within the pathway are altered more often than would be expected, given the overall distribution of gene alterations for that sample. An overall p-value for the pathway is calculated by applying a Fisher's Omnibus test to the set of probabilities across all samples. Pathways with a significant p-value are those which comprise a set of genes that are disproportionately altered in multiple samples .Each test can be performed on all samples or specific classes of samples, and returns a list of all pathways ordered by significance, along with corresponding p-values. These results can be plotted for visual confirmation of association, and then written to text files.The power of pathway-level molecular analysis, and the value of the PathOlogist in facilitating such analysis, has been explored using a number of datasets with various features; the results of two such analyses are reported here, using 1) expression and copy number data from a set of 28 cancer cell lines treated with an anti-cancer drug, and 2) expression and survival data obtained from 377 glioblastoma multiforme (GBM) tumor samples, and 10 unmatched normal samples from the publicly accessible TCGA database.A recently-analyzed data set assessed the expression profile and drug sensitivity of 28 cell lines maintained by the NCI60 Human Tumor Cell Line Screen  as part The set of .cel files for the 28 lines were loaded into the PathOlogist for RMA normalization. Up-Down Probability normalization was then applied to the RMA data, and the results were used to calculate activity and consistency scores for each pathway in the database. A bi-dimensionally-clustered heatmap of the scores is shown in Figure From the heatmap, it appears that the samples do not cluster naturally into separate groups when taking all pathway scores into account. Further analysis is needed to identify specific pathways that influence response to treatment. Thus, GI50 scores  for each of the cell lines were loaded into the PathOlogist, and a Pearson linear correlation coefficient was calculated for the set of activity or consistency scores associated with each pathway. An ordered list of pathways and Bonferroni-corrected p-values is shown in Table From this list we can see that a small set of pathways (n = 7) are significantly correlated with drug sensitivity at the 0.05 level. By contrast, when we shuffled the sample labels and re-performed the correlation analysis 10 times, none of these shuffled distributions produced a single significant correlation after Bonferroni adjustment. Additionally, the PathOlogist was used to depict these relationships in scatterplot form for a few of the top pathways in order to visually confirm the correlation Figure .When these pathways are used in a heatmap , and TNF-alpha and FAS signaling (p < 7 × 10-8). Other pathway-based analyses (Paradigm [-5) histone deacytelase (HDAC) signaling (p < 9 × 10-5), LPA-receptor mediated events (p < 3 × 10-4), p38 MAPK signaling (p < 1 × 10-4), and hif-1-alpha-mediated regulation (p < 2 × 10-4). This overlap suggests that our method is able to find the important features in the data, and identify pathways that are strongly correlated with disease. Furthermore, when we consider the most significantly altered pathways according to consistency scores, many of the pathways we find have extensive experimental evidence [From the heatmap, it appears that the 10 normal samples cluster naturally into a separate group, suggesting that overall pathway-level behavior has been altered in the tumor samples, and that our approach is able to capture these alterations. Further analysis identified specific pathways that are most altered, including many that have been previously implicated in GBM  such as Paradigm , SPIA [1Paradigm ) publishevidence ,17 for iWithin the set of tumor samples, we were also able to identify pathways associated with survival. For each pathway, samples were clustered into two groups based on activity or consistency scores, and a logrank test was performed comparing the Kaplan-Meier survival curve of the two groups. A number of pathways separate samples into groups with significantly different survival curves. Previous analysis with Paradigm identified pathways separating GBM samples into four subgroups, one of which had a significantly increased survival. This group was primarily characterized by upregulation of E2F. Our pathway-based analysis finds that the 'e2f transcription factor network' pathway is the only NCI/Nature pathway for which both activity and consistency are significantly associated with survival. Additionally, we find pathways involving p53, colorectal cancer, and prostate cancer among the most significant.We believe that the integrated analysis made possible by this tool will prove useful for pathway-based study of biological information. Many tools currently exist that infer networks from expression data. By contrast, the PathOlogist predicts expression based on network structure, and then assesses whether actual gene expression systematically deviates from this prediction. Activity and consistency scores provide a simple yet informative summary of biologically complex behavior in a manner immediately suitable for further statistical analyses. The metrics generated by the PathOlogist can be used to generate pathway signatures associated with clinical data, or identify specific pathways implicated in disease for further insight into disease pathology.There are however, some limitations to this type of analysis. The PathOlogist can only analyze established pathways, and assumes the accuracy of the pathways in the Pathway Interaction Database. Although the PID is carefully curated to contain only high-quality, well-documented pathways, there is still a certain amount of ambiguity inherent in each pathway's components, structure, and boundaries. On the other hand, the PathOlogist offers a possible check for pathway validity, as it stands to reason that a pathway returning consistently low consistency scores, even for normal samples, could be assumed to lack biologic fidelity. Additionally, since pathway scores are averages of the metrics associated with each interaction within the pathway, small subsets of interactions that have real association to a clinical feature may be overshadowed by other non-correlated interactions, especially in large pathways.Project name: PathOlogistftp://ftp1.nci.nih.gov/pub/pathologist/Project home page: Operating system: WindowsProgramming language: MATLABftp://ftp1.nci.nih.gov/pub/pathologist/The%20PathOlogist%20overview.doc .Other Requirements: The full version of the tool requires a copy of MATLAB as well as MATLAB's Bioinformatics and Statistics Toolboxes. The tool is most stable when using MATLAB version 2009b and later. The standalone version does not require MATLAB but the RMA normalization and single-pathway graphics features are not currently available. A step-by-step manual for use of the PathOlogist is located at License: noneRestrictions: NoneSG and SE developed and tested the application. SG drafted the manuscript. SE, CS and KB were involved in formulating the initial concepts, and KB coordinated the project. All authors read and approved the final manuscript.


The Pathway Ontology (PW) developed at the Rat Genome Database (RGD), covers all types of biological pathways, including altered and disease pathways and captures the relationships between them within the hierarchical structure of a directed acyclic graph. The ontology allows for the standardized annotation of rat, and of human and mouse genes to pathway terms. It also constitutes a vehicle for easy navigation between gene and ontology report pages, between reports and interactive pathway diagrams, between pathways directly connected within a diagram and between those that are globally related in pathway suites and suite networks. Surveys of the literature and the development of the Pathway and Disease Portals are important sources for the ongoing development of the ontology. User requests and mapping of pathways in other databases to terms in the ontology further contribute to increasing its content. Recently built automated pipelines use the mapped terms to make available the annotations generated by other groups.The two released pipelines – the Pathway Interaction Database (PID) Annotation Import Pipeline and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Annotation Import Pipeline, make available over 7,400 and 31,000 pathway gene annotations, respectively. Building the PID pipeline lead to the addition of new terms within the signaling node, also augmented by the release of the RGD “Immune and Inflammatory Disease Portal” at that time. Building the KEGG pipeline lead to a substantial increase in the number of disease pathway terms, such as those within the ‘infectious disease pathway’ parent term category. The ‘drug pathway’ node has also seen increases in the number of terms as well as a restructuring of the node. Literature surveys, disease portal deployments and user requests have contributed and continue to contribute additional new terms across the ontology. Since first presented, the content of PW has increased by over 75%.ftp://rgd.mcw.edu/pub/ontology/pathway/ or from the National Center for Biomedical Ontology (NCBO) BioPortal website at http://bioportal.bioontology.org/ontologies/PW.Ongoing development of the Pathway Ontology and the implementation of pipelines promote an enriched provision of pathway data. The ontology is freely available for download and use from the RGD ftp site at  The Pathway Ontology (PW) originated and is being developed at the Rat Genome Database (RGD) . Its goaPomc while decreasing the expression of appetite-stimulating Agrp genes. The ‘peptide and protein hormone signaling pathway’ term is in turn a child of the more general term ‘hormone signaling pathway’, as other classes of compounds with very different physico-chemical properties can also act as hormones. For instance, the steroid hormones and the eicosanoids which, as the names suggest, are hormones, are lipid molecules. The signaling pathways they initiate are children of the ‘lipid hormone signaling pathway’ term which in turn, is a sibling of ‘peptide and protein hormone signaling pathway’ and child of ‘hormone signaling pathway’ terms  fall under the disease node. Finally, the set or sets of reactions/interactions representing the system’s response to and handling of treatment(s) geared towards dealing with those perturbation(s) are housed in the drug node. Thus, the main nodes of the Pathway Ontology are: metabolic, regulatory, signaling, disease and drug pathway Figure A. Two tys Figure B. The nos Figure C.The “pathway” and the “process” concepts, although at times interchangeably used, are distinct. A pathway conveys the idea of a set of interacting molecules, of the reactions and interactions underlying its functioning. A process on the other hand, conveys the idea of the end result, the conclusion of a plan of action, whether the consequence of the combined work that the set of reactions and interactions produces, in the case of a simpler one, or in the case of a more complex one, the combined work of pathways that contribute to or in some fashion modulate the end result. At the same time, a given pathway can participate in and/or regulate several processes . In the The provision of terms for the altered versions of pathways and the representation of disease pathways and diagrams as collections of altered pathways are unique to PW and its use at RGD. An altered pathway is one where defects in one or several components of the pathway affect its normal functioning with potential implications for a diseased phenotype. The severity of an altered pathway or the convergence of several altered pathways can overcome the ability of the system to adjust and is manifested in the diseased state. Viewing diseases from a network- rather than a gene-centric perspective, from the systems level of pathway cross-talk and alterations within, is an approach increasingly being considered -15.As an example, a large-scale study carried out on a number of pancreatic tumors identified several sets of genes that were altered in the majority of tumors. Of these, many were associated with core signaling pathways and altered in 67% to 100% of tumors . PerhapsThe use of the ontology allows for the standardized annotation of rat, human and mouse genes to pathway terms. Generally, annotations are made for the term rather than on a gene-by-gene basis; thus, what is being targeted for annotation is the pathway itself – like the ontology the overall pathway curation process is network-centered ,17. ImpoAn ontology search, accessed through the “Function” entry point  or other websites. Beneath that is a tabular list of annotated genes by species with each entry linking to its report page and other links. As applicable, the altered version of the pathway and additional elements in the diagram can also be found in this section. The next section contains tabular lists of genes in the pathway that have been annotated to disease, other pathway and phenotype terms with links to corresponding report pages. The user has the option of toggling between terms and genes and can follow links to ontology report pages for terms and to gene report pages for genes. Rounding out the diagram page are a list of references with links to the RGD reference report page (which links to PubMed), and a view of the ontology tree Figure A-D. PathPrimarily the review and research literature published in major journals, but also database searches and users requests, are sources for the addition of terms in the ontology, the representation of pathways in interactive diagrams and the annotation of genes within pathways. In addition, the restructuring of the drug node and pipelines for importing pathway data from external databases helped, and new disease portal releases continue to help increase and improve upon the content, structure and use of the ontology. These more recent developments are presented in the next section.To further expand the information content that the Pathway Portal provides, RGD has recently developed automated pipelines to bring in data from external sources. The building of the pipelines, along with the ongoing development of the ontology and the deployment of disease portals, has led to further developments within the pathway ontology. Many of the new terms added for the pipelines are within the signaling and disease pathway nodes of the ontology. The restructuring of the drug pathway node was accompanied by the addition of new terms, particularly for the drugs within the antineoplastic category. User requests led to additions across the metabolic, signaling and regulatory pathway nodes. Since last presented , the ontThe Pathway Interaction Database (PID)  at the NThe pipeline extracted the data from the PID master file and mapped it to PW terms via synonyms. 51 individual PW terms have synonyms that map to PID entries. The human genes in the PID file were matched to human genes in RGD and assigned to the mapped PW term with evidence code EXP (Inferred from Experiment). The annotations were propagated to the rat and mouse orthologs with the evidence code ISO (Inferred from Sequence Orthology). The use of evidence codes to indicate how the annotation of a gene to an ontology term is supported originates from the development of GO. Evidence codes are used for all ontology terms and objects that are annotated to them at RGD. Over 7,400 pathway gene annotations from PID are available at RGD (See Table The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a large and important pathway resource and provides a host of other biological information across a spectrum of phyla and species ,23. The Pathway data was extracted from the KEGG master file and mapped to PW terms via synonyms. 215 PW terms have synonyms mapping to entries in KEGG. Genes from the KEGG annotations in the species files were matched to the RGD genes for rat, human and mouse and assigned to the corresponding PW term with evidence code IEA (Inferred from Electronic Annotation). Over 31,000 pathway gene annotations from KEGG are available at RGD.  Classification System. The system is used for the classification of drugs and is controlled by the World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology ,25. In tAs described, a disease pathway is represented by the altered pathways implicated in the disease process  is being built using the OBO-Edit ontology editor, a freely available Java-based tool developed and maintained by the GO Consortium ,29. UpdaThe pathway diagrams are being built using the Ariadne Genomics Pathway Studio version 8, originally from Ariadne Genomics and currently available at Elsevier . The tooThe KEGG and PID pipelines are Java 1.6 standalone applications. The pipelines communicate with an Oracle database, the RGD database, using the Oracle JDBC thin client driver. The Spring framework v.1.2 is used for dependency injection, and parameters are loaded from an external file allowing for easy pipeline customization . Apache API: Application programming interface; ATC: Anatomical therapeutic chemical classification system; CMS: Content management system; DAG: Directed acyclic graph; EXP: “Inferred from experiment” evidence code; GO: Gene ontology; IEA: “Inferred from electronic annotation” evidence code; ISO: “Inferred from sequence orthology” evidence code; JDBC: Java database connectivity; KEGG: The kyoto encyclopedia of genes and genomes; miRNA: Microrna; NCBO: National center for biological ontology; PID: Pathway interaction database; PNG: Portable network graphics file format; RCC: Renal cell carcinoma; RGD: Rat genome database; SMPDB: Small molecule pathway database; WHO: World health organization.The authors declare that they have no competing interests.VP wrote the manuscript, originated the PW ontology, composed most of the PW diagrams, assisted with portal design, diagram publications and with pathway pipeline construction. PJ assisted with construction of pathway pipelines. MT assisted with PW uploads, ftp file placement and reviewed manuscript. GTH composed some of the PW diagrams, assisted with diagram publications and reviewed manuscript. JRS assisted with diagram publications. JDP supervised pipeline construction. TFL reviewed manuscript. RN is part of the RGD team. SJFL reviewed manuscript. SJW reviewed manuscript. MS supervised PW portal and pipeline projects. MRD, DHM, EAW, HJJ co-supervised RGD projects. All authors read and approved the final manuscript.


Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases.In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease.The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner. During the past decade major advances in biological research, mainly in the field of high throughput analysis (e.g. –omics), has led to an exponential increase in available experimental data, produced through a variety of techniques including DNA, miRNA and antihttp://www.kupkb.org.The renal biology field is facing similar problems. Although kidney diseases have been extensively studied in different species , this wealth of information remains hidden across several layers of public data and/or literature repositories. Although considerable effort has been devoted in aggregating data from several resources-7, the rThe iKUP comprises a powerful tool in terms of speed, selectivity and descriptive power. Nevertheless, its nature restricts the user to viewing the query results in tabular format, only skimming the surface of the rich interconnected data otherwise available in the KUPKB. In addition, although it succeeds in displaying findings hidden in scattered repositories, such as the expression of a set of genes under a very specific combination of kidney tissue, cellular type and disease, it remains unable to map this information to interaction networks available as background information and in a multiple species manner.The value of biological network representations has been extensively analyzed in bioinformatics and systems biology literature,11. SomeIn this article, we describe KUPNetViz, an interactive biological network querying and visualization application. Its main purpose is to assist renal scientists to extend their research by providing an alternative KUPKB data image depicting interactions among the queried molecules and their neighbors, coupled with functional and biochemical pathway annotation in both a species dependent and independent manner. The main tasks supported and promoted by KUPNetViz are: i) the display, exploration and manipulation of general protein-protein interaction as well as functional and biochemical pathway association networks for a number of widely studied mammalian species, ii) the transformation of these general networks to kidney specific networks through their association with related gene/protein/miRNA expression datasets, iii) the mining of possibly hidden relationships among co-regulated and/or directly interacting (“neighboring”) entities under several combinations of kidney anatomies and/or disease models and iv) the extrapolation of both expression data and network interactions across different diseases, anatomies but also different species facilitating the quick formulation and screening of biological hypotheses and the design of new experiments.The molecule and interaction visual representations in KUPNetViz have been inspired by previous work in the field with sevhttp://www.jquery.org) coupled with static HTML for the client side. The backend of the application is a MySQL database hosting the background knowledge used to build the mappings among entities, the network interactions, annotation data and properly parsed experimental data that make up the most important part of the KUPKB. The backend database can be very easily updated and maintained as it can be rebuilt in an automated fashion with a single one-line command, through a series of Perl scripts that download, parse and import KUPKB data and the background knowledge resources into the schema, using a wrapper script and a simple YAML (http://www.yaml.org) configuration file. The resulting interaction networks from user queries along with experimental data mappings  are rendered, visualized and controlled using the Cytoscape Web graph visualization library)The reasoning behind the construction of a MySQL backend database to support the application instead of using the KUPKB semantic web repository includes three main arguments: firstly, during the development we sought to maintain software modularity and re-usability even though in its current version, the visualization application is adjusted to kidney. Thus, we considered that an RDB model  can be adjusted easier to other similar experimental data sources (e.g. a different organ) or data sources of different structure (e.g. multi-omics data from different types of cancer), requiring minor modifications only in the user interface part. Secondly, not all the background knowledge data sources that are used by the KUPNetViz were initially incorporated in the semantic web model (e.g. the protein-protein interactions or the reference pathways). This does not suggest a KUPKB model design problem but rather highlights the different initial purpose of the model which was not network visualization, but the detailed knowledge representation of kidney anatomies and disease models and the experimental data mapping and annotation to this knowledge representation model. Finally, during the design process, we realized that not all of the knowledge stored in the semantic web repository  was required for the minimalistic approach promoted by KUPNetViz. Thus, we used an RDB model to reduce query complexities and running speeds. In addition to the latter, the backend database building time, including the download and parsing of the background knowledge files and the re-parsing of the experimental data requires less than three hours while the rebuild of the semantic web repository requires over a day (the times were measured on the same computer). This allows the easier maintenance of the KUPNetViz and the more frequent synchronization of the backend database with the latest releases of the incorporated background knowledge sources.KUPNetViz is a user-friendly biological network visualization tool dedicated to renal research. This tool uses data gathered from multiple resources on several renal pathological states and background knowledge elements regarding biomolecular interactions as well as functional and biochemical pathway associations and creates biological network snapshots that can shed light on mechanisms involved in renal disease. It provides renal biologists with an alternative network data representation, complementary to the functionality of the iKUP exploration tool with a small cost regarding the display complexity. Moreover, maybe its most important feature is the biomolecular interaction extrapolation across species which allows the researcher to quickly formulate and screen several hypotheses in a simple manner. We have demonstrated the value of KUPNetViz in two case studies, further investigating the role of calreticulin as a key player in a gene network mostly up-regulated in renal graft rejection and newly investigating the potential involvement of IL6 and the IL6 receptor in the progression of polycystic kidney disease.The value of the KUPNetViz in kidney research will be increased in parallel with the number of related public multi-omics datasets which will be also available in the KUPKB. The expertise of the KUPNetViz hosting group in renal biology guarantees its constant curation and addition of related datasets, as these are getting published. This will potentially lead to increased confidence in the observed networks as additional biological evidence will eventually pile up from those constantly added datasets. The addition of datasets is easy and straightforward, since data are entered in ready-to-fill Excel sheets by biologists and incorporated by a single command line. We are aiming to the inclusion of the latest omics data sets in the KUPKB at least once a year from which the KUPNetViz will directly benefit from. Future extensions include the incorporation of additional biomolecule annotation and interaction resources (KEGG compounds) and the addition of a module that will identify highly enriched and over-represented biological functions and biochemical pathways using the StRAnGER algorithm.Home page: Accessible through the website of KUPKB and the iKUP browser  athttp://www.kupkb.org or directly athttp://www.kupkb.org/vis/index.php.Operating system: KUPNetViz is a web-based application thus it is platform independent.Requirements: KUPNetViz is best used and viewed under Internet Explorer 8 or higher, Mozilla Firefox, Google Chrome, Apple Safari and Opera. The use of Internet Explorer 7 and lower is not recommended.License: KUPNetViz is free for academic use but requires a license from the authors for any commercial purposes. The software is available without user registration.Further information: An analytical user’s guide coupled with usage examples is available at the application’s homepage and as supplementary material online (Additional fileKUP: Kidney and urinary pathways; KUPKB: Kidney and urinary knowledge base; KUPNetViz: Kidney and urinary pathway network visualizer; GO: Gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; PKD: Polycystic kidney disease; CKD: Chronic kidney disease; IL6: Interleukin 6; IL6R: Interleukin 6 receptor; RDB: Relational database.The authors do not declare any conflicts of interest.PM conceived, designed and implemented the software as a whole, tested the software and drafted the manuscript. JK and JPS tested the software, suggested interface modifications, performed the case studies and contributed in drafting the manuscript. SJ and RS contributed to the database design and the integration of KUPNetViz to the iKUP browser. JB contributed in drafting the manuscript and data management. All authors have read, revised and approved the manuscript.An example of KUPNetVis multispecies view. Three snapshots of a simple KUPNetViz network presenting the multispecies view, either with ‘supergenes’ encompassing orthologs among the supported species, or with collapsed ‘supergenes’ for simplified view.Click here for fileThe KUPNetVis user guide. An extensive user guide presenting all the features of the application with additional examples.Click here for file


There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in these fields is the accurate exchange of data, which is increasingly accomplished through the use of ontologies. Ontologies are formal representations of objects and their properties using a logic-based ontology language. Many such ontologies are currently being developed to represent objects across all the domains of science. Ontologies enable the definition, classification, and support for querying objects in a particular domain, enabling intelligent computer applications to be built which support the work of scientists both within the domain of interest and across interrelated neighbouring domains. Modern chemical research relies on computational techniques to filter and organise data to maximise research productivity. The objects which are manipulated in these algorithms and procedures, as well as the algorithms and procedures themselves, enjoy a kind of virtual life within computers. We will call these information entities. Here, we describe our work in developing an ontology of chemical information entities, with a primary focus on data-driven research and the integration of calculated properties (descriptors) of chemical entities within a semantic web context. Our ontology distinguishes algorithmic, or procedural information from declarative, or factual information, and renders of particular importance the annotation of provenance to calculated data. The Chemical Information Ontology is being developed as an open collaborative project. More details, together with a downloadable OWL file, are available at http://code.google.com/p/semanticchemistry/ (license: CC-BY-SA).Cheminformatics is the application of informatics techniques to solve chemical problems  Cheminformatics, also known as chemoinformatics, is the field of applied informatics which uses representations of chemical entities, manipulated by software, for the determination and prediction of properties of chemical entities.There are many areas in biology where cheminformatics plays an important role in computational research. For example, in the elucidation of whole-organism metabolism and metabolic processes: metabolite databases and computational processes for metabolite identification require extensive use of cheminformatics libraries Cheminformatics has been one of the earliest success stories for the development of novel informatics methods to enhance and supplement the traditional scientific experimental and laboratory-based methods screening, which is the pre-selection of interesting structures for given purposes from the large chemical libraries. Surrounding these innovations and applications, an extensive domain-specific terminology has grown which names and describes these chemical information formats, properties, algorithms, and techniques.The reliable link between chemical structures and chemical properties facilitates research into algorithms and techniques which operate on these structural representations and produce reliable predictions of properties redundancy, ambiguity and ‘silos’ in the eventual terminological system. While this was not so much of a problem as long as all chemical data was locked away behind commercial firewalls, and each individual company working with chemical data had the task of standardising its own internal terminology, in recent years the tide has started to shift towards open data and open algorithms and toolkits in the chemistry domain. In particular, we are seeing the advent of the Semantic Web However, as is often the case during the development of a new scientific discipline, this terminology has been developed and formalised by many different groups in many different publications and other forms of communication, creating A key challenge which arises from this novel environment, as compared to traditional in-house data-warehouse approaches, is managing the heterogeneity of publicly available data with special considerations for provenance and reproducibility of data dependent computational experiments Firstly, different terminologies may refer to the same data with different labels or identifiers. These different labels obscure the fact that the data is comparable and should be integrated, thus ‘hiding’ portions of the data from algorithmic processes of extraction.Secondly, multiple implementations of an algorithm may use the same terms, they can produce different outputs due to heuristics, optimizations, errors or outright differences in the interpretation of the terms. This can lead to incorrect deductions when the results of calculations are made available under the same terminological label without further provenance as to which implementation was used to generate the data.ontologies, which are formal representations of objects and their properties within a logic-based ontology language An emergent and rapidly growing solution to terminological ‘silo’ problems on the web is found in the form of logic-based Chemical Information Ontology (CHEMINF) While ontologies provide formal meaning to terms in a vocabulary, community-based ontologies aim to address the diverse requirements of the members of a community, thus promoting convergence on meaning over a more comprehensive vocabulary than individual efforts might provide. With this in mind, several groups who have independently developed terminologies in the domain of chemical information, have formed a semantic chemistry working group provenance of the information – from detailing the algorithm which was used to generate calculated property values to the specified running parameters and the version of the software implementation.Cheminformatics data that is brought onto the semantic web is derived from a variety of sources, including direct experimental measurement, algorithmic approximation, and model-based prediction. Approximations and predictions can serve as a guide in the absence of experimental verification of property values. In some cases, such predictions may come close to the experimental values; in other cases they may be far off due to the weakness of the correlation between the best algorithm available to perform the calculation and the actual values. Both measurement and prediction of property values are ways to derive information about chemical or biological properties and represent them in such a fashion that they can be accessible for research which furthers the understanding of biological phenomena. Properly reproduced on the semantic web, such values can be used and reused in multiple scientific analyses and data-driven research projects. Reproducibility of results is of key importance in the scientific method in use across many domains. When such research makes use of data originating from the semantic web, this highlights the importance of maintaining the Our goals in the development of the CHEMINF ontology are thus twofold:Create a reference for the definition and disambiguation of terminology in use in the cheminformatics domain.Provide a framework for the automatic integration of data on the semantic web, including annotation of provenance (for reproducibility), automatic reasoning for classification of data, and query support through semantic web technologies such as SPARQL In this paper, we present the background, theory, structure and rationale of the CHEMINF ontology. We describe the content of the ontology, both in terms of the class hierarchy and the relationships used to axiomatise the complex interrelationship between algorithms, data types, data formats, procedural parameters, and files stored on computers. Finally, we illustrate an application scenario which makes use of the CHEMINF ontology.skeleton graphs, and these form the most common format for basic chemical information storage and exchange. cyclohexane. Note that the graph includes only carbon atoms as nodes, while in reality cyclohexane molecules have two hydrogen atoms for every carbon atom, with molecular formula Mathematical graph theory, which studies the properties of connected objects, has found many applications in chemistry logP, which is defined as:From these chemical graphs, many molecular properties, such as mass, charge, and shape, can be determined computationally. An example of such a property is the Definition 1The logP is the logarithm of the octanol-water partition coefficient, which is determined from ratio of the molecules dissolved in octanol to those dissolved in pure un-ionized water upon mixture equilibration.In silico research exploits these associations to make computational predictions of the activity of molecules which have not yet been synthesised, in order to decide which molecules should be synthesised for bench research, and to allow the computational screening of known molecules for new targets to reduce the costs of screening by pre-filtering the molecules which are to be included in the screen. The more effectively such properties can be predicted computationally, the more effective in silico research can become.Such properties are strongly linked to the activity of the molecules within living systems Quantitative Structure-Activity Relationship (QSAR) descriptors are calculated numeric values, based on structural aspects of a molecule, which can be mathematically correlated with the activity of the molecule Definition 2A chemical descriptor is the final result of a logical or mathematical procedure which transforms chemical information from a symbolic representation of a molecule into an useful number or the result of some standardized experiment .The field of QSAR descriptors is a very active area of research, with the goal of discovering better performing QSAR descriptors in terms of predicting certain kinds of bioactivity, such as toxicity http://www.obofoundry.org/.The OBO Foundry continuants  and occurrents ; and between dependent and independent entities .  Together with the Relation Ontology (RO) which provides fundamental relations between BFO entities The Basic Formal Ontology (BFO) copied. BFO distinguishes specifically dependent entities, which cannot be copied, from generically dependent entities, which can. Information entities are thus kinds of generically dependent continuant in BFO.Information entities, such as those which we include in the chemical information ontology, are a kind of dependent entity. Dependent entities are those which cannot exist without a bearer. For example, colour is a dependent entity since there can be no colour without there being something that it is the colour of. Similarly, information entities are dependent entities – there can be no information without it being stored somewhere, such as on my computer – but this dependence functions in a slightly different fashion to that of hair colour, since information can be in general is being addressed by the Information Artifact Ontology (IAO) The domain of information entities about something, which is encoded in the IAO as:Crucial to the IAO definition of information entities is that they are Information content entityis_about.Entityis_about the thing that it is a name of.For example, a name is an information entity, and a name in silico research all information entities, rather than just chemical information entities, we do not attempt to take up this debate further here, but the interested reader can see This ‘aboutness’ relationship is problematic in some cases, for example, in chemistry, information content entities may be created for chemical entities which do not yet exist during the course of classes and the relationships between them (properties), and on the other hand specific entities called individuals, that belong to classes. For example, a particular person called Mary is an individual, and she belongs to many classes including the class of all humans. Classes are predominantly the subject matter of ontologies, as the ontologies are being developed to support multiple annotations of scientific data Human which are the subject of multiple scientific experiments, and the actual individuals who participate in the experiments are exchangeable as long as they are of the right type. Ontologies consist of, on the one hand, general entities called However, with respect to information entities, it is not as straightforward to decide what to model as classes and what as relevant individuals. This is because information may be copied and transferred between bearers, in a way that material individuals cannot. A single computer file can be copied between multiple computers. Should we model it as an individual? Or should we rather model a class of files containing the same information content? The Blue Obelisk Descriptor Ontology BODO In the next section we describe the structure of the ontology.http://semanticscience.org/resource/CHEMINF_XXXXXX, and include labels (rdfs∶label) and definitions (dc∶description). The ontology is versioned using owl∶VersionInfo. All CHEMINF resources are Linked Data nodes, and their URIs are dereferencable.The chemical information ontology (CHEMINF) is implemented with the Web Ontology Language (OWL2) D), thus contains atomic concepts and roles, transitive roles, conjunction, disjunction, existential and value restriction, role hierarchies, inverse roles, number restrictions and datatypes The expressivity of the ontology is The ChEMINF ontology includes entities such as:Chemical graphs, and various formats for encoding them.Chemical descriptors, with definitions and axioms describing what they are specifically about.Specifications for certain descriptors.Algorithms and their software implementations and axioms describing their inputs and outputs.Chemical data representation formalisms and formats.Additionally, we have identified a hierarchy of chemical qualities, which are needed to specify exactly which quality a chemical descriptor is describing. However, in keeping with the OBO Foundry's principle of orthogonality of ontology application domains, we have submitted these chemical quality terms to the Phenotype Quality ontology (PATO) We explicitly exclude from the scope of CHEMINF:Actual chemical entities, parts, ions, groups, etc which are included in the ChEBI ontology Any aspects of protein or nucleotide sequence information which are included in the Sequence Ontology We include named algorithms, but do not give the algorithmic steps. The relevant paper describing the algorithm is linked to from the definition where possible.Similarly, for format specifications  http://semanticchemistry.googlecode.com. To preserve modularity and ease of maintenance, the ontology consists of multiple files, with one such separate file, for example, providing mappings to the Blue Obelisk Descriptor Ontology. These separate files are referenced from the primary ontology file cheminf.owl using the OWL import mechanism.The ontology is licensed as Creative Commons Share-Alike By Attribution and is freely available from the Google Code project site agent.Not illustrated in this diagram are the processual executions of the software implementations, as these fall within the hierarchy of processes rather than information entities. The link between processes which are software executions, and the software implementation that is executed, is that the process has the software as atom count to more complex descriptors such as topological polar surface area. Descriptors give information about qualities of chemical entities, and to formalise this association, we have added 50 chemical entity qualities to the quality branch of the ontology, including polarizability and relative permittivity. Format specifications, such as MDL molfileCMLThe key entities in our ontology, situated beneath their appropriate superclasses in the referenced ontologies, and including their number of subclasses, are given in The key relations in our ontology are:is about some entity; an entity is described by some information content entity.  The IAO is about relationship is further specialized into different subrelations, of which one example is the is quality measurement of relationship, which relates a measured datum to the particular quality that it is a measurement of. While this relation is close to what we need in order to relate chemical information entities to properties of the chemicals that they are about, we allow chemical information that is both measured and calculated, and we therefore introduce a distinct relation is descriptor of.An information content entity is descriptor of some specifically dependent continuant ; a specifically dependent continuant has descriptor some chemical descriptor.A chemical descriptor conforms to some directive information entity (i.e. specification); the directive content entity specifies an information entity.An information content entity has attribute some data item; the data item is attribute of some entity.An entity bearer of relation which links independent entities to the dependent entities  which inhere in them, and the has part and part of mereological relations, which are inherited from the Relation Ontology, and the has value data relation which links a data item to its value.Other relations which we make use of in the ontology including the We now discuss the ontology model in more detail for the specific topic areas of format specifications, chemical descriptors, and algorithms and implementations.specify formats for data exchange or the operational requirements of a particular procedure. These information objects have a kind of normative content, creating – in their information content – a requirement on the information objects that conform to them. We model this type of information object as directive information entity.In cheminformatics, many information objects are created in order to standardise or Definition 3A directive information entity is an information content entity that explicitly states essential attributes/requirements for a product or procedure, and may also be used to determine that the product/procedure meets its requirements/attributes.data format specification.One special kind of directive information entity is that which specifies the format for the encoding of information such that it can be encoded and decoded in a standard way. This is a Definition 4A data format specification provide directives regarding the syntax of information such that it can be encoded and decoded in a standard fashion.Some examples of data format specifications are the MOL and SD file format specifications commonly used for chemical graph storage and exchange These data formats are then used in the definition of different types of chemical descriptors.chemical descriptor.The most general type of chemical information entity is that which captures some sort of data about some chemical entity. We use the term Definition 5A chemical descriptor is a data item  whose syntax and semantics conforms to some data format specification and provides information about chemical entities including, but not limited to reactions, substances, molecular entities, and their parts .Note that the term ‘descriptor’ has a narrower meaning in some cheminformatics communities, i.e. restricted in use to only those descriptors which have numeric values and which can be used in quantitative structure-activity-relationship models. For these types of descriptor, we propose the subtypes ‘numerical chemical descriptor’ which is defined in terms of the data type of the descriptor, and ‘QSAR chemical descriptor’ which is described in terms of the applicable usage of the descriptor.Chemical descriptors may enumerate material or processual parts, quantify qualities or realizables including dispositional probabilities. For example, a SMILES descriptor, which conforms to the SMILES specification for unambiguously describing molecular structure using short ASCII strings, can be created for aspirin  with value CC( = O)Oc1ccccc1C(O) = O.The following example shows, in Manchester OWL syntax Class: ‘acetylsalicylic acid’   SubClassOf:   has_attribute ‘acetylsalicylic acid InChI’,   has_attribute ‘acetylsalicylic acid InChIKey’,   has_attribute ‘acetylsalicylic acid SMILES’Individual: ‘acetylsalicylic acid InChI’ Types:  ‘InChI Descriptor’,Facts:  ‘has value’   “InChI = 1/C9H8O4/c1-6(10)13-8-5-3-2-4-7(8)9(11)12/h2-5H,1H3,/f/h11H”Individual: ‘acetylsalicylic acid InChIKey’ Types:  ‘InChIKey Descriptor’,Facts:  ‘has value’ “InChIKey = BSYNRYMUTXBXSQ-WXRBYKJCCW”Individual: ‘acetylsalicylic acid SMILES’ Types:  ‘SMILES Descriptor’,Facts:  ‘has value’ “CC( = O)Oc1ccccc1C(O) = O”decide to give a different structural descriptor to a particular chemical entity, as they can for a name. The structural descriptor is constrained by the format specification and the structure being described. Chemists could, of course, decide to change the format specification, and many new structural descriptors are born through the invention and specification of new formats. Note that in many cases, the structure of a chemical entity may not be known at the time that the chemical is named. In other cases, a structure is presented but is later found to be incorrect, and needs to be revised throughout public databases. Chemical entities are therefore not identical with their structural representations . Indeed, structural representations give a static view of the nature of chemical structures, which is an approximation to the actual dynamic reality.Descriptors for chemical entities often describe aspects of the structure of chemical entities. Structural descriptors have the additional property that, while remaining within the rules of the structural representation formalism, cannot change value without representing a different entity. To put this differently: chemists cannot have a meeting and Our model allows the explicit linking not only of a descriptor to the kind of entity it is about (such as a molecule), but also to the particular property of that entity that the descriptor is representing. For example, a charge descriptor is descriptor of the electrical charge quality of a molecule. In this way, descriptors can be grouped together based on the nature of the properties that they describe. However, there are some descriptors for which the exact molecular property that the descriptor is describing is unclear; in these cases we remain agnostic and make no assertion above the claim that the descriptor is about the molecule, with the possibility to pick out those specific attributes which formed the input to the descriptor calculation.Chemical descriptors can be obtained from physical experiments, in which something is quantitatively measured. We say that an experiment involves some chemical substance as input and produces some chemical data as output. For instance, the structure of a chemical substance can be investigated using nuclear magnetic resonance (NMR); this requires as input some chemical substance in buffered solvent within some concentration range and produces as output resonance frequencies.in silico from the analysis of computational representations of chemical entities by software applications.On the other hand, descriptor values can be generated When using software to predict chemical attributes, software applications consume some kind of data and produce some kind of data. Software, modules and methods are expressed as source code using programming languages that are subsequently compiled into a machine interpretable format. These software methods are specified by one or more algorithms, or sequences of steps. Like format specifications, algorithms are directive information entities.Definition 6An algorithm is a directive information entity that consist of a finite sequence of instructions to accomplish a task, which may be expressed in pseudocode, textual description, or a process flow diagram.Chemical descriptors are distinguished from the algorithms which generate them, although in many cases they share a common name, since algorithms specify procedural information, while descriptors are declarative information. In some cases, the same descriptor can be calculated by several different algorithms.Named algorithms may have different versions. For example, the Kabsch algorithm for calculating the optimal rotation matrix for alignment of two chemical structures was first presented in Algorithms are also distinguished from the software which implements the algorithms. This is because it is possible for an implementation to contain errors, or to be more or less faithful to the designed algorithm which it implements. Programming languages have different constructs and performance profiles which lead to subtle differences in different implementations of the same algorithm. For this reason, to correctly associate provenance with calculated descriptor values, we suggest at minimum the annotation of calculated values to the software implementation rather than directly to the algorithm, and preferably with detail about the fully specified process execution, as discussed below in the context of data transformation operations.has agent the programming language.Software implementations can be stand alone single software methods, or they can be packaged into software libraries. For example, the Chemistry Development Kit (CDK) Definition 7A software implementation is a machine-executable set of instructions in some programming language. Software implementations generally belong to some named library, which is a collection of related software modules. Individually executable methods or components of a software implementation take input parameters, execute some operations using such input values, and produce some output parameters.Considering the software maintenance lifecycle, most software implementations are continuously evolving. Different versions of software arise from this maintenance cycle, each being a different manifestation of the relevant source code, in that they are variants of each other.data transformation operation is the execution of a software module with specific parameters as inputs.When software is actually executed within some pipeline or towards some objective, its execution is a process. The outcome of this process depends on many factors in the execution environment, including the hardware platform on which the process is executed and the operating system and other supporting libraries which are installed on that platform. For example, a Definition 8A data transformation is a planned process that realizes some agent-specified objective. It requires the software which is being executed and the hardware on which it is executed as participants, and may require data items as input, and may produce data items as output.defined classes, i.e. classes which are fully logically defined through the specification of necessary and sufficient conditions. These logical definitions allow the use of a reasoner to compute subsumption (classification) beneath differently defined parent classes. This avoids the need to maintain separate classification hierarchies by hand in order for the result to include classification along multiple possibly orthogonal axes.In the CHEMINF ontology, we create different axes of classification, such as the axis of classification based on the type of entity that a descriptor is about, through the use of chemical substance descriptor and molecular entity descriptor, which are defined as those descriptors which are about chemical substances or molecular entities respectively. Note that a chemical substance is a bulk collection of molecular entities, such as a portion of water compared to an individual water molecule.Examples of classes which we have defined using necessary and sufficient conditions in this fashion are We have presented an ontology for the domain of chemical information entities, with primary application to the disambiguation of data types in data integration tasks and the assocation of provenance with data especially in the context of the semantic web.Chemical InferenceEarly ground work in the area of the ontology of chemical information was laid by Gordon in his 1988 series entitled The CHEMINF ontology describes chemical descriptors, software and algorithms. As such, we investigated in which other formalisms such objects are described. Chemical entities are often described and exchanged in the MDL SDF file format eChem eChem RDF schema (i.e. their ontology) only defines a vocabulary with basic types such as Molecule and Property, and the different types of property are associated with different predicates such as has-name and has-SMILES for name and SMILES respectively. Going this route would require adding a predicate  for every different descriptor, and is thus much more difficult to maintain and develop related applications around than the approach of allowing an extensible hierarchy of descriptors such as we adopt in CHEMINF.Closely related to our work on the ontology of chemical information is work on the chemical semantic web, in which data is being brought online on the Internet in the form of Resource Description Framework (RDF) ‘triples’. The RDF vocabulary Combet al. focused on representing chemical structure and being able to associate simple computed attributes et al.To demonstrate the potential of semantic web technologies for semantic data integration, Konyk Our work is thus an extension of these earlier RDF offerings in providing an ontology for the classification and axiomatisation of such properties, while keeping to the more valuable elements of the model such as allowing explicitly for different vendors providing different implementations and explicitly stating the parameter values used in calculations.The semantic web makes it possible to publish, share and integrate data online. However, making data available on the semantic web is only the first step towards the vision of seamlessly distributed and integrated data being available for application consumption. In a data warehousing approach without the semantic web, each application downloads and consumes the source data it requires in whichever proprietary format that data is made available in. Extensive in-house processing is required to transform all the disparate sources of data into a common format which allows for comparison and integration, and custom rules are required for performing such integration. This leads to a huge maintenance overhead, since in order to keep the data up-to-date, the process must be regularly repeated, and application changes are required every time something changes in the representation format used in any of the source databases.syntactic aspects of the challenge of on-the-fly data integration. However, this goes only a part of the way to resolve the underlying issue, since there is also a semantic challenge in data integration, since data items may be named and identified differently by different data providers . For example, one database may provide IUPAC names for chemical entities in a field entitled ‘iupacName’, while another may provide the IUPAC name in a field entitled ‘systematic name’. Without human intervention, it is impossible for computers to integrate these two data items - i.e. to unify them in the resulting application view which has consumed the data from both sources.The semantic web approach is to replace custom in-house data warehousing with distributed data sources which are integrated on the fly by applications as they perform their required functions. The data is thus consumed when it is needed, and local copies are not maintained, avoiding the large maintenance and redundant storage overheads associated with the data warehousing approach. One of the requirements for such an endeavour to work is that all the data is made available in a standard format that can be processed in a uniform fashion - which is RDF in the context of the semantic web. Data provision in RDF addresses the reasoning which serves the diverse purposes of consistency checking (i.e. automated checks for modelling and data errors), classification , and sophisticated question answering.At the very least, community standards for the different identifiers that are to be used for different types of data are required so that the data from different data providers can be automatically unified where applicable. A ‘flat’ list of standardised identifiers for different types, such as a dictionary or controlled vocabulary can provide, would be sufficient for this purpose. Since data that is brought onto the semantic web is linked by relationships, e.g. the name is associated with the chemical that it is the name of, a similar need for standardisation applies to the relationships used and to the types of entity that can be related by those relationships. This type of specification goes beyond that which can be provided by a flat dictionary, thus a schema definition language such as RDFS is required. However, logic-based ontologies such as CHEMINF which are represented in OWL provide both of these functions and have an additional benefit besides: they allow for complex The use of OWL ontologies for the annotation of data brought onto the semantic web thus supports and facilitates the semantic web vision in several different ways - through standardisation of identifiers, through standardisation of modelling schemas, and through the ability to perform logic-based reasoning for classification, consistency checking and question answering. We have already described the classification of entities in the ontology through use of the logical definitions, and consistency checking is regularly used as part of the ontology development process to ensure that released versions of the ontology are error-free. Here, we further describe our efforts to annotate publicly available semantic web data with CHEMINF, and detail an example of question answering on annotated data using reasoning over the ontology.CHEMINF has been used to standardize the annotation of SMILES and InChI strings in the recently introduced RDF version http://semanticscience.org/resource/>PREFIX cheminf: <SELECT count(?molecule) as ?moleculeCount WHERE { ?molecule cheminf:CHEMINF_000200    }http://s3.semanticscience.org/ and a faceted browsing interface is available at http://bio2rdf.semanticscience.org:8035/fct/. The SPARQL endpoint is at http://bio2rdf.semanticscience.org:8035/sparql/. All of the descriptors which are included in ChEBI have been annotated with their respective CHEMINF values, thus unambiguously identifying their type.Previously, we have demonstrated the use of Bioclipse and SPARQL to extract QSAR datasets from the ChEMBL database Integration of data from disparate data sources and several domains, as well as subsequent querying, as enabled by CHEMINF, has recently been demonstrated with the Chemical Entity Semantic Specification formalism (CHESS) http://www.ebi.ac.uk/hastings/downloads/cheminfpopulated.zip.In order to illustrate question answering over an annotated knowledge base, we transformed a set of 90 antidepressant drug molecules from PubChem We queried our sample knowledge base using the DL Query tab of Protégé. A query to evaluate drug-likeness according to the Lipinski Rule of Five and ‘has attribute’ some and ‘has attribute’ some and ‘has attribute’ some This query retrieves from the knowledge base all those chemical entities that are compliant with the Rule of Five, that is, they have molecular mass no more than 500; they have an XLogP of less than 5; they have a hydrogen bond acceptor count of not more than 10 and a hydrogen bond donor count of not more than five.In our knowledge base of 90 antidepressants, we would expect that many of them, being known drugs, would be compliant with the Rule of Five, and indeed this is the case: 88 molecules match the query. The result of applying the query in the DL query tab are shown in Another query example is to retrieve all the descriptors calculated by a particular software library. We can use the following query to retrieve this:‘data item’ and ‘is output of’ some(‘software execution’ and ‘has agent’ some ‘Chemistry Development Kit’ )In our annotated knowledge base, this query returns 361 instances, which include the CDK-generated XLogP and HBond descriptors.instance checking  and classification for subsumed classes. This type of querying is very useful against knowledge encoded in OWL ontologies, but has limits when compared to the expressivity of querying in the Semantic Web context, in which SPARQL is usually used as the query interface. However, SPARQL is not OWL-semantics aware, so it is not possible to query OWL constructs using SPARQL. A query mechanism based on SPARQL but which does interpret OWL-DL semantics is SPARQL-DL The above queries have been executed using the DL query tab in Protégé, which allows the construction of queries which are evaluated using SPARQL-DL is useful for, like SPARQL, allowing the tabulation of results based on querying the knowledge base – that is, not only finding which entities match a given class description, but retrieving ordered lists of attributes of entities, like a database query. For example, the following SPARQL-DL query retrieves the names (within the knowledge base) and associated descriptors and values which were calculated using OpenEye. The key difference to a straightforward SPARQL query in the below is that SPARQL-DL is able to interpret the semantics of the rdfs:subClassOf operator and check this against the OWL hierarchy.select ?compname ?desctype ?descname ?descvalueWHERE { ?compound ci:CHEMINF_000200 ?X ;   rdfs:label ?compname.?X rdf:type ?Y ;  ci:CHEMINF_000012 ?descvalue ;  rdfs:label ?descname ;  ci:CHEMINF_000606 ?Z.?Y rdfs:subClassOf ci:CHEMINF_000186 ;  rdfs:label ?desctype.?Z ro:has_agent ci:CHEMINF_000267.}?compound) which has a label (?compname) and has an attribute (CHEMINF_000200 ?X) which is a subclass of XLogP descriptor (CHEMINF_000186) and was calculated using OpenEye (has_agent CHEMINF_000267). The query returns the name of the compound (?compname), the type of the descriptor (?desctype), the name of the descriptor, which is unique for each descriptor in the knowledge base (?descname), and the numeric descriptor value .In this query, the prefix ‘ci’ refers to an entity in the CHEMINF ontology and ‘ro’ the relationship ontology. The query retrieves a compound .Provenance was captured in terms of the software (and version) plus parameters used to generate the descriptors.http://www.ebi.ac.uk/hastings/downloads/cheminfpopulated.zip.The full knowledge base is available for download from:


Streptococcus pyogenes during 1999–2009. Maintaining drug use below a critical threshold corresponded with low-level macrolide-resistant S. pyogenes and an increased number of erm(A)-harboring emm77 S. pyogenes with low fitness costs.In Belgium, decreasing macrolide, lincosamide, streptogramins B, and tetracycline use during 1997–2007 correlated significantly with decreasing macrolide-resistant  Streptococcus pyogenes results primarily from modification of the drug target site by methyltransferases encoded by erm genes, erm(A) and erm(B) or by active efflux mediated by a mef-encoded efflux pump. Of these, erm(A) is inducibly expressed (B), which share overlapping binding sites, remain active against erm(A)-harboring S. pyogenes (erm(B) can be constitutively or inducibly expressed and confers high-level resistance to MLSB (mef(A) also is constitutively expressed but confers low to moderate resistance to 14- and 15-membered macrolides and susceptibility to 16-membered MLSB  and to outpatient use of MLSB and tetracycline in Belgium.That macrolide use is the main driver of macrolide resistance in streptococci has been well demonstrated at the population and individual levels  (395 [46.5%]) or mef(A) (383 [45.1%]). We detected erm(A) in only 85 (10.0%) resistant strains; however, their proportions among macrolide-resistant strains increased from 1 (1.2%) of 81 in 1999 to 36 (76.6%) of 47 in 2009. erm(A)-harboring S. pyogenes isolates primarily belonged to emm77 (50/85[5.8%]). mef(A) was mostly associated with emm1, emm4, and emm12 and erm(B) with emm11, emm22, and emm28 (mef(A)- and erm(B)-associated emm types decreased gradually, whereas those of erm(A)-harboring emm77 (erm(A)-emm77) )increased steadily from 2006 onward (erm(A)-emm77 became predominant in 2008−2009, representing 10−28 (32.2%−59.6%) of total macrolide-resistant S. pyogenes isolates during those 2 years (erm(A)-emm77 belonged to the same pulsed-field gel electrophoresis cluster and harbored tet(O), indicating gene linkage.We screened 11,819 6 onward ; most isnd emm28 . During 6 onward . erm(A)- 2 years . Most  to model the data obtained for macrolide-resistant S. pyogenes. MLSB and tetracycline use was expressed in packages/1,000 inhabitants/day, a better proxy for prescriptions than defined daily doses in Belgium, where the number of defined daily doses per package or prescription had increased during the previous decade  and remained stable at this level  from 2004 onward . Using an interval of 2 years, we observed a highly significant positive correlation between decreasing use of MLSB and tetracycline during 1997−2007 and decreasing levels of macrolide-resistant S. pyogenes during 1999−2009 (p<0.0001). The consistent decrease in MLSB and tetracycline use since 1997 was further accentuated by the start of public health campaigns in December 2000 that also were directed toward prescribers and successfully reduced antimicrobial drug prescribing in Belgium . A semm-types might differ if antimicrobial drug use remains below a certain threshold. In concordance with the models, we found a negative correlation between use of MLSB and tetracycline and proportions of erm(A)-emm77 among macrolide-resistant S. pyogenes (p = 0.0002), and we identified 0.62 packages/1,000 inhabitants/day as the critical threshold volume of MLSB and tetracycline use below which proportions of erm(A)-emm77 among macrolide-resistant S. pyogenes would increase significantly (p<0.0001). Next, we compared the fitness of erm(A)-emm77 with that of 6 other major macrolide-resistant geno-emm-types in Belgium during 1999–2009 (erm(B), erm(A), or mef(A) in 50 randomly selected colonies per plated mixture. Number of generations and relative fitness of competed pairwise strains were calculated as described (erm(A) in an emm77 background was more fit (67%) than most of the geno-emm-types that predominated during the previous years of higher MLSB and tetracycline use (mef(A)-emm1 and erm(B)-emm28 geno-emm-types were equally as fit as erm(A)-emm77. Foucault et al. (vanB gene had no effect on fitness of enterococci and might explain the low fitness cost of erm(A) carriage in emm77 strains. A predominance of erm(A)-harboring strains during 1993–2002, with 30% in an emm77 background, was also reported in Norway, a country with a low prevalence of resistance (2.7%) and antimicrobial drug use (erm(A) and emm77 as geno-emm-type because the fitness benefit  was not as remarkable for other erm(A)-harboring emm types (data not shown). The mechanisms underlying the higher fitness benefit conferred by an emm77 versus another emm background for the erm(A) genetic element remain to be investigated and might be related to differences in basal gene expression or compensatory changes in the emm77 genome or might result from differences in the genetic element harboring erm(A) in emm77.Notwithstanding clonal changes, the fitness costs  associated with particular resistance mechanisms is another major factor governing the relation between use and resistance. Mathematical models have shown threshold levels of antimicrobial drug use below which the frequency of resistance would not increase if resistance imposes a fitness cost for the bacteria -emm77 geno-emm-type. Maintaining antimicrobial drug use below a critical threshold might facilitate stabilization of low-level antimicrobial drug resistance and of milder resistance mechanisms with lower fitness costs.Using macrolide-resistant