Biological pathways are central to many biomedical studies and are frequently discussed in the literature. Several curated databases have been established to collate the knowledge of molecular processes constituting pathways. Yet, there has been little focus on enabling systematic detection of pathway mentions in the literature.Pathway Named Entity Recognition), for the systematic identification of pathway mentions in the literature. PathNER is based on soft dictionary matching and rules, with the dictionary generated from public pathway databases. The rules utilise general pathway-specific keywords, syntactic information and gene/protein mentions. Detection results from both components are merged. On a gold-standard corpus, PathNER achieved an F1-score of 84%. To illustrate its potential, we applied PathNER on a collection of articles related to Alzheimer's disease to identify associated pathways, highlighting cases that can complement an existing manually curated knowledgebase.We developed a tool, named PathNER (http://sourceforge.net/projects/pathner/.In contrast to existing text-mining efforts that target the automatic reconstruction of pathway details from molecular interactions mentioned in the literature, PathNER focuses on identifying specific named pathway mentions. These mentions can be used to support large-scale curation and pathway-related systems biology applications, as demonstrated in the example of Alzheimer's disease. PathNER is implemented in Java and made freely available online at  Systems biology places special emphasis on the large-scale integration of various data and "scattered pieces of knowledge" . This isBiological Named Entity Recognition (NER), specifically, is a key part of biomedical text mining as biological entities are basic actors in biological systems and processes. Promising and useful results have been achieved in recognising genes , chemicaAlthough the so-called "deep approach" to pathway curation is likely to remain dominant in the near future as automated methods for pathways reconstruction are yet to match human curators , severalThere are several systems that are closely related but not dedicated to the task presented here. For example, LitInspector  labels gIn the remainder of this paper, we first introduce PathNER and evaluate its performance on a gold-standard corpus we developed. We then demonstrate the potential of PathNER in a case study focused on pathways related to Alzheimer's disease.We focus on named pathway mentions that are referred in text by using a specific name . Such names often contain general pathway-specific keywords  and mentions of associated genes. However, many occurrences of general pathway keywords are ambiguous . Inversely, many pathway names do not contain obvious pathway-related keywords (e.g. "RNA degradation" [KEGG PATHWAY: hsa03018]).A dictionary of pathway names was compiled from the Pathway Ontology  and ConsWe note that there are some general names in the dictionary, such as "Disease", "Metabolism" and "Gene Expression". Those mentions mostly come from Reactome and KEGG. We decided to keep all entries for completeness and automated updates, although we note that they are ambiguous and can be used to describe generic biological concepts.To better understand the composition of pathway names, we have conducted a lexical profiling of six representative databases . We analysed frequent tokens and keywords and mentions of genes/proteins. The analysis revealed some interesting (but expected) differences between different pathway database sources (see the Results section).The workflow implementing PathNER is shown in Figure To match pathway mentions in text, we employed soft dictionary matching as term variations often cause exact string matching to fail . TypicalThe core of the soft dictionary matching method is calculating the similarity between two given strings  using appropriate distance metrics. PathNER utilises the SoftTFIDF method implemented in the open-source Java toolkit SecondString . SoftTFITo match against a whole document, a set of candidate query strings needs to be generated. We have developed a tailored method where the first step for candidate selection is to find possible starting points for potential candidates. To that end, each token is first sent to SoftTFIDF lookup configured with a relatively low threshold  is empirically set as 0.40). If the result set is not empty, then the token's position is recorded as a potential starting point for a possible match candidate. This prevents unnecessary matching attempts starting from tokens that do not appear in the dictionary. In the second step, we check each recorded starting point with different lengths of the window: each check starts with a window length of one and the window length is increased until it reaches the maximum . Tokens within the window form a query string for SoftTFIDF that is also configured with the lower threshold. If the result set is not null, the check continues; if more than four  consecutive window lengths find no matches by SoftTFIDF lookup, the check stops and a new check starts from the next possible position. During each check, the match results for different window lengths are stored. The longest possible match which scores over a high threshold  are returned. Generally speaking, a smaller value for the lower threshold slows down the processing speed, whereas a larger value can decrease recall; for the higher threshold, a greater value potentially increases precision but reduces recall and a smaller value increases recall but potentially hinder precision. We note that these thresholds are configurable in PathNER.The rule-based component aims to complement the soft dictionary lookup method in particular to address name variation and missing names. It works by integrating three types of information: general pathway-specific keywords, POS tags and gene/protein recognition results. The component is implemented using the Java Annotation Patterns Engine (JAPE) within the GATE framework.The pathway-specific keywords we used include "pathway", "signalling" , "transduction", "cascade" and "network". Specific combinations of those keywords are also considered as keywords . Gene/protein mentions are recognised by BANNER .We developed two types of rules: backward and forward. Backward rules start from a keyword found in the text. PathNER then scans backwards for one of the following five types of tokens: determiners , separators , prepositions or subordinating conjunctions , wh-words  and verbs. These tokens indicate potential starting points of a pathway mention. Forward rules scan forward from the pathway keyword position for pathway-specific verb phrases  until any punctuation character is met.Segments anchored by these rules are then integrated with the results generated by BANNER. If BANNER reports any mention of gene/protein-like names that are contained within a rule-detected segment, then the segment is tagged as a named pathway mention.We generate the final annotation set by merging the results from the above two components. Mentions detected by only one component are added directly to the final set. For overlapped annotations, a new annotation covering both is generated and added to the final set.There is no gold-standard corpus that annotates pathways at the mention level. We used the GENIA corpus  to generOur gold-standard corpus was derived from GENIA in two parts, which are both manually annotated for mentions of named pathways. The first part is a collection of 50 randomly sampled abstracts from the GENIA corpus. As randomly selected articles might contain too few pathway mentions for meaningful evaluation, we engineered the second part to ensure that there are adequate pathway mentions for evaluation. This part is derived from the GENIA sentences that contain both a G#other_name label and pathway-specific keywords. These sentences were manually checked and only named pathway mentions were labeled. As a result, the annotated gold-standard corpus , Recall (R) and F1-score (F1) defined by the following equations:TP is the number of true positives, FP is the number of false positives and FN is the number of false negatives.where To illustrate the potential of PathNER for curators and systems biologists who are interested in relationships between pathways and diseases, we applied PathNER to Alzheimer's disease (AD). AD-related pathways have been already manually curated within the AlzPathway database . The datgnEutils4j  can be captured by PathNER but it can be very hard for any dictionary-based method. While the rules were often successful, their coverage can still be improved. For instance, PathNER was not able to recognise a much simpler "microglial activation" (which is a pathway listed in AlzPathway) as it did not contain any of our pathway-specific keywords or gene/protein name. PathNER would therefore need to be extended with other potential clues. For example, Table PathNER is based on soft dictionary matching and additional rules, which enables it to address term variations. The performance improvement shows the complementarity of the two approaches. The rules, in particular, proved to be useful for spotting complex pathway mentions. For example, "If recognised pathway mentions are to be linked to a database entry, then rules will need to provide some additional matching. For instance, although 'the CD2 pathway' has been mentioned in many articles related to T lymphocytes, it was not an entry in our dictionary. CD2 is the acronym for "Cluster of Differentiation 2" and it represents a cell adhesion molecule. The closest relevant entry we found was "Cell adhesion molecules (CAMs)" [KEGG:hsa04514], but linking "the CD2 pathway" to "Cell adhesion molecules (CAMs)" would require significant background knowledge.The AlzPathway case study was used to illustrate the potential of PathNER in identifying relationships between pathways and disease, using a relatively small set of documents. Even within this restricted scenario, we have shown how PathNER can help identity relevant disease pathways, and thus facilitate the curation process. It generated some new suggestions that were not included in the human-curated AlzPathway database. PathNER could provide a curation support by enabling fast detection of pathway mentions based on a much more comprehensive collection of articles than used in AlzPathway (only 100 articles) or indeed in our case study (around 2000 articles). The results are interesting in two aspects: on one hand, PathNER could contribute to prioritising pathways for curation  and thus help address the limitations on time and resources available to carry out the curation. On the other hand, the successful application of PathNER on the AD corpora suggests that PathNER can be used to guide the curation of pathway maps related to other human diseases in a systematic way (e.g. the diseases where there are no organised community curation efforts).PathNER integrates dictionaries and rules, instead of relying on machine learning which is another popular NER approach. There are several reasons for that. Machine-learning approaches require a fairly large amount of annotated samples for training, which were not available in this case. On the other hand, public pathway databases with names are readily available and patterns commonly used for mentioning pathways are well understood. Therefore, an approach based on soft dictionary matching and rules becomes a natural choice.As a NER tool, PathNER aims to identify pathway name mentions. On the other hand, text-mining systems that aim to reconstruct pathways from literature aim to identify constituent entities in the text and then perform information extraction to identify the relationship between those entities. Consequently, we cannot compare PathNER with those systems as they are addressing different problems. However, PathNER can serve as a complementary/component tool in such systems as different entities  can interact with pathways to build new pathways. By tagging pathway mentions as a type of entity with PathNER, it would be possible to utilise existing interaction extraction systems (e.g. ) to retrIn this paper we presented PathNER, a tool that can systematically detect biological pathway mentions in literature with a lenient F1-score of 84%. In contrast to existing text-mining efforts that target the automatic reconstruction of pathway details from molecular interactions mentioned in the literature, PathNER focuses on identifying specific named pathway mentions. We also demonstrated that PathNER could provide a curation support by providing reliable curation suggestions from most frequent pathway mentions in the primary literature associated to a particular disease. Another potential audience of PathNER are systems biologists who want to investigate the relationship between pathways and disease or other biological entities. The future work will include extending the rule component, in particular through mapping of recognised mentions to most similar entries in pathway databases. In addition, we aim to develop a "pathway names crawler" that will crawl the literature for new pathway names to update our dictionary.http://sourceforge.net/projects/pathner/ under the Common Public License 1.0.PathNER together with the Alzheimer's corpora as well as the extracted results are available at POS: Part-Of-Speech; NER: Named Entity Recognition; AD: Alzheimer's disease; KEGG: Kyoto Encyclopaedia of Genes and Genomes; PID: Pathway Interaction Database; PO: Pathway Ontology; JAPE: Java Annotation Pattern Engine; PMID: PubMed ID.The authors declare that they have no competing interests.CW developed PathNER and deployed the detection on the Alzheimer's corpora, and drafted the manuscript. GN provided support and guidance from the text mining perspective and JMS from the systems biology perspective. GN and JMS conceived and supervised the project. CW, JMS and GN wrote the manuscript. All authors read and approved the final manuscript.Dictionary of pathway names. The dictionary is a .txt file, with each line represents one dictionary entry. The data comes from ConsensusPathDB and Pathway Ontology. (human data only).Click here for fileGold corpus. The gold corpus is an .xml file. To open, please download and install the freely available GATE developer program [http://gate.ac.uk/]. After installation, open GATE, and create a new 'Language Resources' with the 'GATE Document' type, select the path of this .xml file.Click here for fileAD corpora. The corpora are compressed in the .tar.gz file. Once decompressed, you will get four separate .txt files. Each txt file contains multiples lines of PubMed IDs or PubMed Central IDs, one ID per line.Click here for file


Variety of information relating between genome and the pathological findings in disease will yield a wealth of clues to discover new function, the role of genes and pathways, and future medicine. In addition to molecular information such as gene expression and genome copy number, detailed clinical information is essential for such systematic omics analysis.In order to provide a basic platform to realize a future medicine based on the integration of molecular and clinico-pathological information of disease, we have developed an integrated clinical omics database (iCOD) in which comprehensive disease information of the patients is collected, including not only molecular omics data such as CGH (Comparative Genomic Hybridization) and gene expression profiles but also comprehensive clinical information such as clinical manifestations, medical images , laboratory tests, drug histories, pathological findings and even life-style/environmental information. The iCOD is developed to combine the molecular and clinico-pathological information of the patients to provide the holistic understanding of the disease. Furthermore, we developed several kinds of integrated view maps of disease in the iCOD, which summarize the comprehensive patient data to provide the information for the interrelation between the molecular omics data and clinico-pathological findings as well as estimation for the disease pathways, such as three layer-linked disease map, disease pathway map, and pathome-genome map.With these utilities, our iCOD aims to contribute to provide the omics basis of the disease as well as to promote the pathway-directed disease view. The iCOD database is available online, containing 140 patient cases of hepatocellular carcinoma, with raw data of each case as supplemental data set to download. The iCOD and supplemental data can be accessed athttp://omics.tmd.ac.jp/icod_pub_eng Recent rapid advances in the human genomics and the subsequent “post-genomic” comprehensive molecular information collectively called “omics” ,2, such Furthermore, inspired by the rise of the systems biology in the biological science, also in disease study, needs for the systems approach to understand a disease as an integrated whole have been widely recognized. Except for rare monogenetic diseases, most of the diseases can be considered as an integrated system where aberrations of molecular, tissue/organic and individual level are closely interrelated to produce clinical phenotype. We call this perspective “systems pathology” view of disease With these backgrounds, it becomes accepted that the interrelation between various omics information and clinico-pathological findings of disease is of crucial importance to be clarified in order to develop a new possibility of medicine, which we call “omics-based systems medicine”.Cancer is now considered as systems dysfunction of cellular regulatory pathway which is caused by the combined effects of environmental/life-style related factor and genetic aberration such as somatic or germline mutations, SNPs, copy number alternation, epigenetic changes and so forth. For diagnosis and therapy of such diseases, not only the molecular information but also clinical, pathological and life-style information is indispensable. Without them, complex diseases such as cancer will not be able to be examined correctly -8. ThereRecently, some pharmaceutical companies announced that they will open the genomic data focus on lung and gastric cancers to rapidly increase knowledge of disease and disease process. So, we can expect that the field of research based on such clinical/environmental information will develop with our iCOD.http://www.sanger.ac.uk/genetics/CGP/) and Atlas of Genetics and Cytogenetics in Oncology and Haematology (AGCOH) [Cancer Genome Anatomy Project (CGAP) ), The Ca (AGCOH)  are relaThe contents of the iCOD are based on clinical, pathological, and environmental data obtained from patients who received medical care at Tokyo Medical and Dental University Hospital and other collaborating institutions since 2005. At present, we focus on cancer patients. With the tight collaboration with our University hospital, we have collected hepatocellular carcinoma, colon cancer, and oral cancer samples. Samples were collected just after surgery, and were snap-frozen in liquid nitrogen. Meanwhile, clinical research coordinator (CRC) obtained comprehensive clinical records, laboratory data, pathological findings, diagnosis and prognosis, and also performed interview to collect extensive information on medical history, lifestyle and so on. The collected information items are shown in figure We also collected gene expression data, array CGH data with detailed pathological information of the sample tissue obtained from each patient. DNA and RNA were extracted from the surgical specimen, after laser capture microdissection which was conducted if required. All of the expression data in the database was obtained using Affymetrix HG-U133 plus 2.0 array as described previously . We haveThe iCOD was made on the PostgreSQL Database system. This database is capable of storing and handling these clinical/omics data by using 2 dimensional 3 layered (2D-3L) map. The 2D-3L program script is running on the Apache-Tomcat web server.The back end data analysis programs were written by Java-servlet R statistical software which are available upon request.Figure The user can browse the patient list in the database by clicking "Display all case List" in the section of “Case Archive”. To see the details of specific cases, click “Show case information” button. The user will be able to examine further data of an individual patient such as clinical manifestations, medical images , laboratory tests, drug histories, and pathological findings as well as life style information. The case information items and their layered structures are listed in figure The iCOD provides users a convenient search engine to query keyword related to pathological/clinical findings and patient ID stored in the database. To search the individual patient cases in the database satisfying the conditions, enter key terms of the query in the “Search” box in the section “Case Archive”.“Clinical omics data analysis” provides various maps to observe the interrelation (correlation) between clinico-pathological phenotype and gene expression using multivariate statistical analysis applied to the molecular and clinico-pathological information of the patients.Click on the “Clinical Omics Data Analysis” button from the top page. The user will be able to choose two different analysis methods, which are 2 dimensional 3 layered (2D-3L) map and Pathome-Genome map (CCA).The 2D-3L map consists of two types of views. The left side view shows the overview of the plot of each patient which provides the relative position of the patient’s information in each of the molecular, pathological and clinical layer. For each layer, principal component analysis (PCA) is used to create 2D map by summarizing the multivariate data into the first and the second principal component scores. The right side view shows the detailed data list in the each layer of the selected patients. Molecular layer displays the result of gene expression profile by a heatmap diagram. In this map, patients are grouped by user-specified criterion selected in the “Parameter Settings” diagram. The screenshot in the figure In pathological layer and clinical layer, each plot represents patient position in the corresponding 2 principal components coordinate system. By selecting a patient in a certain layer, the 2D-3L map draws connecting line between corresponding points of different layers of the same patient, by which the user can intuitively understand the relationship among different layers of an individual patient. The user can choose multiple patient points at the same time, and the selected patients are shown in the data list; this can be operated by specifying the region including the entire designated patient in the layer with a simple mouse operation.This map has a parameter setting function for a customized analysis. To use this function, the user only have to change detailed parameter values in three buttons “Data selection”, “Parameter setting” and “Display setting” at the head of the 2D-3L map page. In “Data selection” page, select the type of cancer you wish to analyze . In “Parameter setting” page, the user will be able to specify the any group of clinical/pathological items to be applied by principle component analysis to determine layer axis.In “Display setting” page, shapes and colours can be adjusted in accordance with various parameters; so that characteristics of a specific group of patients can be obtained by changing these factors for comparison.Figure Pathome-Genome map shows the relation between clinical/pathological information and gene expression, which was calculated by the regularized canonical correlation analysis (CCA) method figure . CCA is Our international version is available now, containing 140 patient cases of hepatocellular carcinoma. The number of cases are increasing and containing the other disease cases such as colon and oral cancer. We also plan to prepare retrieval page that displays the correspondence table of arbitrary gene and its p-values of all criterions used in this data base. We are preparing to accept clinical omics data from other public projects as a repository. We are also preparing to disclose our web based analysis tool for microarray called “Microarray Analysis Workflow”, used to build our database.Many cancer related databases which stored a variety of molecular information have been developed, as described before. However, more detailed clinical/environmental information in combination with the molecular information is needed to elucidate the whole process of the complex diseases such as cancer. From this point of view, our iCOD is the first database which provides the comprehensive clinical, pathological and life-style information in addition to the molecular biological information as well as their estimated interrelation. The iCOD database is useful both for clinical researchers who intend to have knowledge about molecular basis of disease which could be used for diagnosis, therapy and prognosis of the diseases, and for molecular biologists who intend to know the function and phenotype of the molecular pathways and their interrelation through the knowledge in the cases of their dysfunction. Our subproject aims to develop the model disease database in the “omics” era which has a standardized database organization being able to cover the multi-hierarchical  information concerning the diseases.http://omics.tmd.ac.jp/icod_pub_eng/download. Raw data files consist of raw gene expression data by Affymetrix .CEL binary format, and detailed clinical information of each case is stored by CSV text format.We prepared the download page of raw gene expression data for users who want to analyze them with his/her own tool. The supplemental data can be found at KS drafted the manuscript. KS is responsible for achievement and the organization of the web design. KM calculated all p-values and canonical correlation analysis concerning hepatocellular carcinoma. SS and AH checked clinical information, and translated them into English. HM organized the molecular biology experiment. HT and HM provided advice and supervised the research group. All authors read and approved the final manuscript.The authors declare that they have no competing interests.


There are web tools available that allow viewing and editing pathways, as well as representing experimental data on them. However, few methods aimed to identify the signaling circuits, within a pathway, associated to the biological problem studied exist and none of them provide a convenient graphical web interface. We present PATHiWAYS, a web-based signaling pathway visualization system that infers changes in signaling that affect cell functionality from the measurements of gene expression values in typical expression microarray case–control experiments. A simple probabilistic model of the pathway is used to estimate the probabilities for signal transmission from any receptor to any final effector molecule (taking into account the pathway topology) using for this the individual probabilities of gene product presence/absence inferred from gene expression values. Significant changes in these probabilities allow linking different cell functionalities triggered by the pathway to the biological problem studied. PATHiWAYS is available at:  These repositories offer diagrams that constitute a static representation of the different relationships between the molecules involved in the pathways. Despite its usefulness, this information cannot be edited or easily used for modeling, simulation or any other type of analysis. Therefore, the full exploitation of such information requires of complementary developments that convert such information into a useful tool.Signaling pathways represent the way in which the combined effect of gene activity elicits cell-level responses by activating/deactivating specific functionalities in response to particular stimulus through a chain of intermediate molecules. Different repositories, such as KEGG , Reactomhttp://www.R-project.org), which drastically limits its use only to experienced data analyzers.Different software tools have been developed to help visualizing, editing and superimposing gene expression values over the pathway structures . Such toHere, we present a new web server, PATHiWAYS, which can be used to easily infer changes in signaling with consequences for cell functionality from the measurements of gene expression values in typical expression microarray case–control experiments. Contrarily to other tools, PATHiWAYS use a probabilistic model to transform differences in gene expression levels into differences in the probability of signal transmission from the nodes that receive the stimulus to the final nodes that trigger the response. In this way, gene expression data, of often difficult interpretation, are transformed into meaningful functional information regarding changes in the different pathway responses triggered by particular stimulus.http://www.babelomics.org) server  as well as raw data as CEL files. The ‘Experimental design’ section allows users to upload another text file containing the labels (case/controls) that define for the individual microarrays. Files in these formats are generated by many software packages, including the Babelomics  server . An exam) server  is pre-lA simple probabilistic model of the pathway is used to calculate the probabilities for signal transmission from any receptor protein to any final effector protein  using for this the individual probabilities of protein presence/absence. Gene expression values are taken as proxies of gene activity and, consequently, the presence/absence of the corresponding protein . More thHaving the individual probabilities of the nodes already estimated, the probability of signal transmission along a stimulus–response circuit can be inferred from the probabilities of activation of all the connecting nodes, taking into account that inhibitor nodes allow signal transmission when they are deactivated. Therefore, a simple product of probabilities (using the principle of inclusion/exclusion when bi- or multi-furcating stretches are present) provides an easy and reasonably accurate approach to estimate the probability of signal transmission sought. However, such probabilities of signal transmission by themselves are meaningless. What is interesting is the comparison of such probabilities in two different conditions . We apply a conventional multi-level analysis  that allThe output consists of a summary of the pathways selected for the analysis with scores of global activity and the number of individual stimulus/response circuits with a significantly altered activity between the cases and the controls. Additionally, for all the pathways, a detailed list of the changes in the activities of all the stimulus–response circuits in the case–control comparison is provided, along with a graphical representation of the multi-level test. Also a graphical representation of the pathway with the stimulus–response circuits color coded according to their corresponding activity changes . Pathways constitute probably the best representation of biological knowledge of specific cellular processes experimentally validated by hundreds of researchers. This knowledge cumulated over years has been codified into a graphical representation, which is available through different repositories ,2. In paAvailable web-based graphic tools essentially represent data onto the pathway structure, leaving the functional interpretation to the researcher . HoweverPATHiWAYS is a unique web tool that provides an interpretation of the functional consequences of signaling activity changes in a pathway using for this raw gene expression values. PATHiWAYS offers for the first time a rigorous testing framework in a friendly and easy-to-use graphical web-based environment.The unprecedented increase in the genomic data is not running in parallel with the development of tools for the exploitation of the information contained in such data. This proposal points to an interesting direction: turning data (gene expression measurements) into valuable biological information . Signaling pathways are on the basis of many diseases and account for many key biological processes. However, the information coded in them remains largely unexploited. Actually there is a lack of tools that use the knowledge of the circuitry that allows signal transduction to provide sophisticated genomic data analysis within a graphical environment. PATHiWAYS offers a straightforward manner of converting genome-scale gene expression data into significant changes in cell signaling  that can, therefore, be easily related to the specific biological problem studied.Spanish Ministry of Economy and Competitiveness (MINECO) [BIO2011-27069 and INNPACTO (IPT-010000-2010-43) CITRUSGENN]; Conselleria de Educacio of the Valencia Community [PROMETEO/2010/001]; National Institute of Bioinformatics (www.inab.org); CIBER de Enfermedades Raras (CIBERER), both initiatives of the ISCIII, MINECO and the Bull Chair in Computational Genomics (http://bioinfo.cipf.es/chair_compgenom).Funding for open access charge: Conflict of interest statement. None declared.


Summary: The CluePedia Cytoscape plugin is a search tool for new markers potentially associated to pathways. CluePedia calculates linear and non-linear statistical dependencies from experimental data. Genes, proteins and miRNAs can be connected based on in silico and/or experimental information and integrated into a ClueGO network of terms/pathways. Interrelations within each pathway can be investigated, and new potential associations may be revealed through gene/protein/miRNA enrichments. A pathway-like visualization can be created using the Cerebral plugin layout. Combining all these features is essential for data interpretation and the generation of new hypotheses. The CluePedia Cytoscape plugin is user-friendly and has an expressive and intuitive visualization.Availability: http://www.ici.upmc.fr/cluepedia/ and via the Cytoscape plugin manager. The user manual is available at the CluePedia website.Contact: bernhard.mlecnik@crc.jussieu.fr or jerome.galon@crc.jussieu.frSupplementary information:Supplementary data are available at Bioinformatics online. The plugin automatically recognizes a variety of identifiers for genes, proteins or miRNAs that can be updated with the latest NCBI information.CluePedia comes with human and mouse interaction data. New pre-compiled files as well as data for >20 other organisms can be automatically downloaded after the installation. The plugin is easily extendable for additional organisms and identifiers in a plugin-like manner.CluePedia calculates statistical dependencies (correlation) for markers of interest from experimental data. Four tests for investigating linear and non-linear dependencies between variables are implemented: Pearson correlation, Spearman’s rank, Distance correlation  and the Experimental data can be normalized and visualized next to the nodes using adjustable thresholds. Filter methods based on the expression level, standard deviation and the number of missing values are available. Another feature allows the extraction of a subset of expression data corresponding to a pathway or term, from a dataset into a new file.CluePedia visualizes custom correlation weights as well as known interaction and miRNA-binding scores as edges on the network.The network can be enriched with markers with the highest interaction score for all or each of the selected nodes. Furthermore, the network can be enriched with hub markers that have the highest connectivity with all selected nodes. Different edge score types can be used together for the enrichment, and the network will be updated with the specified number of top scored interactions meeting all threshold criteria. Customizable filters allow the visualization of highest common/specific interaction scores within the data sources selected. The user can easily modify the action type displayed , as well as the color of nodes and edges.Importantly, this enrichment can be used within ClueGO networks of pathways. Newly enriched genes already known to be associated with a pathway will be automatically linked to it. Another original feature of CluePedia expands ClueGO terms into nested networks. Like this, a pathway can be investigated in detail to reveal how known gene interrelations are modulated within the experimental context used, and which could be the newly associated genes/miRNAs.CluePedia automatically extracts the cellular location of markers from GO terms and maps it on pre-defined cellular compartments. Based on this information, a pathway-like view can be created using the Cerebral plugin layout ; European Commission 7FP ; LabEx Immuno-Oncology.Conflict of Interest: none declared.


The advent of Systems Biology has been accompanied by the blooming of pathway databases. Currently pathways are defined generically with respect to the organ or cell type where a reaction takes place. The cell type specificity of the reactions is the foundation of immunological research, and capturing this specificity is of paramount importance when using pathway-based analyses to decipher complex immunological datasets. Here, we present DC-ATLAS, a novel and versatile resource for the interpretation of high-throughput data generated perturbing the signaling network of dendritic cells (DCs).Pathways are annotated using a novel data model, the Biological Connection Markup Language (BCML), a SBGN-compliant data format developed to store the large amount of information collected. The application of DC-ATLAS to pathway-based analysis of the transcriptional program of DCs stimulated with agonists of the toll-like receptor family allows an integrated description of the flow of information from the cellular sensors to the functional outcome, capturing the temporal series of activation events by grouping sets of reactions that occur at different time points in well-defined functional modules.The initiative significantly improves our understanding of DC biology and regulatory networks. Developing a systems biology approach for immune system holds the promise of translating knowledge on the immune system into more successful immunotherapy strategies. Dendritic cells (DCs) orchestrate a repertoire of immune responses that endow resistance to infections and tolerance to self. DC plasticity has a prominent role in eliciting the proper immune response. Different DC subsets display different receptors and surface molecules and express different sets of cytokines/chemokines, all of which lead to distinct immunological outcomes. Among the receptors are the innate pattern recognition receptors (PRRs) that mediate the initial sensing of an infection. These include Toll-like receptors (TLRs), RIG-I-like receptors (RLRs), NOD-like receptors (NLRs), and C-type lectin receptors (CLRs) . TLRs reBecause of their essential role in the initiation of an adaptive immune response, DCs are an attractive target for therapeutic manipulation of the immune system . In factImmune systems biology is defined as the comprehensive and quantitative study of interactions between hosts and microbes over time, leading to the generation of models describing their dynamic behavior of immune cells and pathogens.Many studies investigated immune cell since these cells are particularly suited to functional genomics analyses because their responses to specific stimuli in a controlled environment can be clearly categorized. Innate responses against pathogens however cannot be considered as a set of discrete signaling pathways activated by a pathogen binding to a receptor; but rather such responses are composed of many interconnected pathways depending on multiple factors.Important initiatives based on systems biology are arising to collect high throughput data and to develop sophisticated bioinformatic methods to compare and analyze these data. In this respect, the Immunological Genome Project initiative  represenhttp://www.dc-atlas.net and they cover a plethora of cell surface receptors  and DC-relevant processes . To illustrate the potential of this new resource, we have selected as paradigmatic the set of TLRs pathways. We describe how they were curated and show the advantages of our approach through their validation both "in silico" and "in vitro".Here we describe DC-ATLAS, a collection of pathways specifically curated in DC, that can be exploited, using pathway analysis based approach, in deciphering the complex network of interactions occurring in DCs upon activation. The pathways are available at The database contains both human and mice data and the modular structure of DC-ATLAS led to unravel of the major differences between these two systems. The knowledge provided by DC-ATLAS permits the conversion of genomic research into accurate and robust biological hypotheses by generating signatures that serve as valuable tools to understand DC physiology and contribute to the design of new strategies in immunotherapy.DC-ATLAS is one of the first immunological and bioinformatics integrated project which complies with the Systems Biology Graphical Notation (SBGN) . It is cEvery specific gene and reaction were annotated providing information on the organism, the organism part, the cell type and the experimental details in which the evidence has been obtained. The community of curators within DC-ATLAS manually annotated the pathways providing also the most updated reference available in existing databases and literature, as well as generating experimental proofs in their own laboratories where these were lacking. The curation procedure itself is described in more detail in Additional file To ensure that the results of the curation process would also be fully used for representation and data analysis, a DC specific data format, the Biological Connection Markup Language (BCML), was developed to represent pathways according to the specification proposed by the Systems Biology Graphical Notation (SBGN). BCML prIn addition to a full implementation of the PD specification, BCML provides a series of optional features. First of all, BCML can include additional information on the entities that compose the network: each entity can be described by a series of species specific database identifiers, e.g. Entrez Gene or Uniprot accession numbers. Furthermore, each entity or reaction can have a set of facts or "Findings" associated. "Findings" are collections of biological information that are relevant to that entity or reaction. The current specification includes support for organism, organism part (tissue), cell type, the specific biological environment in which the evidence was proven, and the type of the experiment used to gather evidence. To reduce ambiguity and promote consistency among different "findings", the schema enforces a controlled vocabulary built from current medical ontologies.The specification of BCML is accompanied by a series of programs (BCML software suite) that enable the use and manipulation of the format both for the bioinformatician and the biologist. First of all, the software suite permits validation of pathways described using BCML, to ensure consistency and the proper enforcement of the SBGN rules. Secondly, the software can create a fully SBGN compliant graphical representations by transforming the BCML XML into other formats (GraphML) which can be then saved as images with third-party software.The format also permits filtering of the pathway data creating a new network containing only elements with user-defined characteristics, allowing the production of tailored made pathways, allowing individualized analyses. The tools in the BCML software suite allow specific "filtering" of the pathway, taking advantage of all the information stored. For example, nodes and edges can be selected for a specific cell type or organism, permitting the construction of customized network maps to represent specific biological contexts. When a filter is applied to the pathway, elements are marked as "included", "excluded", or "affected". An element of the pathway is included or excluded in the resulting map if it matches with the selected filter criteria or not. The "affected" state is used to indicate elements that may not be present depending on the filtering; for example. in a specific cell type a complex may not form if one or more of its proteins are not present. Filtering may be used to assist data analysis and interpretation and might point to gaps in current knowledge.The BCML format can incorporate any kind of experimental measurements that can be matched to the identifiers of an element. This allows modification of the BCML map, facilitating incorporation of high-throughput data coming from transcriptomic or proteomic experiments. The outcome will be visualized in different color on the graphical map.Finally, BCML allows transformation of the pathways into different data formats, which may be needed for further analysis. Tools provided within the suite allow the generation of identifier (gene) lists from a BCML file, enabling their use with analysis tools such as Gene Set Enrichment Analysis (GSEA), Fisher's Exact Test. Additionally, the format can be converted to a form amenable for impact analysis through the SPIA R package. This conversion can take into account the filtering applied to the elements of the pathway, to carry out individualized analyses.A detailed description of BCML format is available as Additional file At present, the human TLR pathway set in DC-ATLAS is a network organized in an ensemble of 8 pathways , subdivided in 10 sensing modules, 32 signal transduction modules and 30 outcome modules. In contrast to what is present in existing databases, TLR7 and TLR8 were curated separately. Although their genes lie in close proximity on chromosome X and are highly homologous, recent evidences suggest they have distinct roles in DC mediated immune response -13. For Expert-guided, manual curation of the pathways has been a crucial part of the DC-ATLAS initiative, leading to a substantial "reshaping" of the existing pathways. For example, curation of TLR3 pathway led to the validation of only about 50% of the genes included in the list originally retrieved from public databases Figure  and 1B. Another example demonstrating the importance of DC-ATLAS curation is exemplified by the fact that in the sensing module of TLR9 we found a new element, UNC93B1, whose involvement in signaling was demonstrated already in 2007 . All theA summary of all the new genes and/or connections, not previously annotated in TLR pathways, and present in DC-ATLAS is presented in Table To facilitate meaningful analysis of "omics" data, the pathways in DC-ATLAS are organized in a modular structure. Every signaling cascade downstream a specific receptor was divided into 3 types of modules in which the very last component of one module is also the first component of the subsequent module. The first type of module is the receptor and sensing module and comprises component(s) of the pathway directly interacting with the stimulus. The second transduction module, encompasses all components transducing the incoming signal from the sensing module downstream to the nucleus. This module generally starts with a molecule interacting with the receptor and ends with a transcription factor. The third and final module is the outcome module: it describes the end result of the signaling process. This last module begins with a transcription factor and includes target genes whose expression is altered after activation of the receptor. Complex cell functions, such as apoptosis, migration and differentiation are also described as outcomes.According to the previous module definition, the pathways in DC-ATLAS may contain more than one of each type of modules. As an example, Figure The modules, as we defined them, have been subsequently tested using gene expression data as described in the following paragraphs. It should be emphasized that the transduction modules are not independent but are highly interconnected and partially overlapping. Furthermore, a given outcome may result from activation of more than one transduction module.The data format we used to describe the pathway allowed us to depict interactions in the cellular organelles where they occur as well as to specifically mark genes and interactions according to the biological system  where they took place. Thus, we were able to create a map of the TLR3 pathway for example clearly showing which genes and interactions were described in DCs and which were not  that respectively activate TLR4 and TLR7/8. We calculated pathway signatures for each of these datasets and subsequently clustered resulting pathways (see Methods).By clustering pathway signatures using publically available TLR pathways, it proved virtually impossible to obtain information of individual potentially affected elements within the TLR pathway, despite clear up-regulation at the pathway level. Instead, clustering of DC-ATLAS based results readily showed a separation of different stimulatory conditions Figure . The totAs we expected both the TLR7/8 and TLR4 modules were affected upon specific stimulation, with R848 and LPS respectively . At earlAlso in time course experiments, the modular structure of DC-ATLAS allows to appreciate time-dependent changes in expression providing a more informative analysis. The TLR4-sensing module is repressed at 3 hours of LPS stimulation. After 12 hours of stimulation, the MyD88 dependent signaling module is less abundant when compared to MyD88 independent transduction modules during the earliest time points. As can be seen in Figure As can be seen from the analysis, in addition to a single TLR specific pathway, a number of other TLR pathways can be affected by the stimuli used. This is because several of the TLR pathways, such as the TLR4 and TLR7/8 pathways, share some elements, although this does not necessarily mean that their engagement leads to identical outcome. When analyzing the LPS dataset at 3 hours, 135 genes were found to be differentially expressed within the DC-ATLAS pathways and 47 of them belonged to the TLR4 signaling pathway Figure . Among tGiven the modular structure and DC specific annotations of DC-ATLAS we can also evaluate individual elements involved in TLR specific signaling. For example, we mapped differentially expressed genes upon 3 hours-LPS stimulation from our data set to the TLR4 pathway Figure  and becaThese results thus illustrate that the modular structure of DC-ATLAS allows a better and more detailed understanding of TLR mediated signaling in time course experiments.Currently, studies on mouse DCs outnumber those on human cells; however, comparisons between mouse and human models have been somewhat biased due to biological differences between both species  as well An immune response forms a complex biological system with many possible inputs, influences and outcomes, in which DCs play a critical role. The relationship among the different immune cells subsets and model systems are currently under active debate, highlighting the importance of annotating signaling pathways in respect of both the cell type and the species in which the pathway was found.Zanoni et al. reviewed the divergent responses of DCs and macrophages upon LPS , and desStudying DCs using an immune systems biology approach facilitated by DC-ATLAS, holds promise to dissect the integrated signals from these cells. This allows us to build models of the complex process of DC regulation and generate predictions and hypotheses about DC function under physiological and pathological conditions. However, the road forward is not without obstacles. There is a strong need of a greater coverage of network data, improved accuracy and standardization of annotation. The extraction of signal transduction maps from gene expression data requires well-structured pathway definitions. Similarly to the recently published DC pathway map , DC ATLAThe presented results show how DC-ATLAS allows temporal dissection of events within the signal, represented by 3 different modules grouping the sensing/receptor, the transduction of the signal and outcome pool. The pathway analysis perturbations, with these modular pathways, visualize the signal propagation, keeping track of the flow of information.As demonstrated by LPS and R848 stimulation of DCs Figure , the uprThis type of dissection of the flow of information described as changes in gene expression is made possible solely by the use of BCML. It provides a suitable format to store the information collected and organized by the curators and to build the modular pathways. Our goal was to provide a data format that was easily extensible and manipulable for computational analyses, but at the same time intuitive and user friendly for both cell biologist and immunologist communities. BCML satisfies these needs thanks to its flexibility, which permits its use in computational analysis and in the conversion to a SBGN compliant graphical map. The possibility of filtering permits the creation of "customized" networks, better suited to identify specific biological problems or to highlight gaps in current knowledge. At this moment, BCML only covers the SBGN Process Description. In the near future we will also integrate. the SBGN Entity Relationship and Activity Flow representation. in order to provide a complete representation of SBGN at the data level.We developed our own format since neither of the many existing formats such as KGML, BioPAX  or SBML Being SBGN compliant and machine readable, BCML provides a convenient and precise way to represent biological pathways, in an intuitive and user friendly format to both the biologist and the bioinformatician.Because of the more refined modular description of pathways in DC-ATLAS, the results of statistical analysis are much improved with respect to the results one can obtain from existing pathway databases.The modular pathway of DC-ATLAS allows more accurate capture of signaling pathways. In many cases pathway modules are not specific for just one stimulus. This overlap is expected given the combinatorial nature of the module structure and definition that describes the biological nature of signal transduction in DCs. This is an important feature, as the decision making process of DCs often integrates signaling from multiple receptors and temporal integration of multiple sets of stimuli.New computational methods such as DC-ATLAS will contribute to fill in current gaps in the analysis of genomic data. Furthermore, the ability of DC-ATLAS to identify gaps in our current knowledge will foster future research within the immune system, and lead to the design of novel experiments aimed at reconciling interactions and findings documented in human and mouse DCs. In addition, DC-ATLAS will establish the relationships between pathways operating within DCs and other cell types of different species.In conclusion, DC-ATLAS provides a knowledge base on DC biology with the potential to decipher the complex network of interactions occurring within these cells in response to activation stimuli. This knowledge allows the conversion of genomic research into accurate and robust biological hypotheses. Extracting results from large expression datasets using DC-ATLAS will enable us to validate experimentally defined pathways and generate signatures that will serve as valuable tools in the design of new strategies for DC-based immunotherapy.We selected several pathways of interest for immunology, in particular for DC activation, as TLRs pathways and curated them. We handled the currently available information from the literature and from public available pathway databases, such as KEGG, Reactome and GenMAPP, evaluating the quality of the data, as well as experimental evidence generated in our laboratories to curate/design the pathways in a cell specific manner. Detailed information are available on line as Additional file Pathways were drawn following the SBGN Process Description (PD) 1.1 specification . Followihttp://www.inoh.org), (http://www.dc-atlas.net).The DC-ATLAS pathways were also represented in GPML (using an in-house modified version of the PathVisio program ) and INO6 DCs were cultivated with LPS (100 ng/ml) or R848  or without any stimuli. After 3, 6, 12 and 24 hr, cells were collected. RNA preparation, labeling with Cy5, hybridization on a Human HT12 array (Illumina), and scanning were performed according to the Illumina reference protocols.Peripheral blood mononucleated cells (PBMC) were isolated from buffy coat blood sample from healthy donors from the Transfusion Unit Erlangen hospital  by Ficoll-Hypaque density gradient centrifugation (Biochrom AG). The experimental plan was approved by the local Ethical Committee, and informed consent was obtained from all donors. Monocytes were isolated from low density PBMCs by magnetic enrichment with anti-CD14 beads (Miltenyi Biotec). Cells were cultured in the presence of granulocyte macrophage colony stimulating factor  and recombinant IL-4 (1000 U/ml) for 6 days to allow DC differentiation . 2 × 106Bead-summary data saved from Illumina BeadStudio was pre-processed in several steps. Firstly, the background signal was assessed and corrected using the intensity signal from the control probes present on the array, then quantile normalization was performed. In addition to background correction, Illumina probe identifiers were converted to nucleotide universal IDentifiers (nuIDs)  specificMicroarray data have been submitted to the Array Express repository, with the accession number E-MTAB-448.http://rmaexpress.bmbolstad.com) or with the R programming language (other platforms).Publicly available data sets were retrieved from the Gene Expression Omnibus (GEO) database. After retrieving, they were normalized with the Robust Multi-array Average (RMA) method  in the cet al., unpublished data), over the compendium of DC-ATLAS TLR pathways. Prior to the analysis, microarray raw data were transformed into absolute-scale values and processed following the procedure outlined by [Pathway analysis was performed with PathStudio ; grey elements indicate entities whose presence has not been demonstrated in DCs. Blue elements highlight reactions that depend on non present (grey) elements and thus may not occur.Click here for fileFigure S2: SBGN representation of the TLR3 signaling pathway highlighting the reactions that occur only in macrophages. Black elements are entities whose presence has been demonstrated in dendritic cells (DCs); grey elements indicate entities whose presence has not been demonstrated in DCs. Blue elements highlight reactions that depend on non present (grey) elements and thus may not occur.Click here for filePathways analysis results of LPS vs R848 comparison: input matrix. A matrix of Pathway Enrichment Factors (PEFs) obtained from the transformation of the signed p-values derived from the pathway analysis. This matrix can be used for clustering using multiscale bootstrap resampling or other methods.Click here for fileFigure S3: Enriched genes found to be part of TLR4 signaling upon LPS stimulation superimposed to the SBGN pathway map. Differentially expressed genes of DCs stimulated for 3 hours with LPS present in the TLR4 signaling superimposed to the pathway map. Red nodes indicate that the respective genes are up-regulated, and green nodes indicate down-regulated genes.Click here for fileFigure S4: Enriched genes found to be part of TLR4 signaling upon LPS stimulation superimposed to the SBGN pathway map. Differentially expressed genes of DCs stimulated for 6 hours with LPS present in the TLR4 signaling superimposed to the pathway map. Red nodes indicate that the respective genes are up-regulated, and green nodes indicate down-regulated genes.Click here for filePathways analysis results of human vs mouse comparison: input matrix. A matrix of Pathway Enrichment Factors (PEFs) obtained from the transformation of the signed p-values derived from the pathway analysis. This matrix can be used for clustering using multiscale bootstrap resampling or other methods.Click here for file


CSBL INteractive Pathway BuildER), to assist a user to build a user-specified gene network for a prokaryotic organism in an intuitive manner. CINPER builds a network model based on different types of information provided by the user and stored in the system. CINPER’s prediction process has four steps: (i) collection of template networks based on  known pathways of related organism(s) from the SEED or BioCyc database and the published literature; (ii) construction of an initial network model based on the template networks using the P-Map program; (iii) expansion of the initial model, based on the association information derived from operons, protein-protein interactions, co-expression modules and phylogenetic profiles; and (iv) computational validation of the predicted models based on gene expression data. To facilitate easy applications, CINPER provides an interactive visualization environment for a user to enter, search and edit relevant data and for the system to display  results and prompt for additional data. Evaluation of CINPER on 17 well-studied pathways in the MetaCyc database shows that the program achieves an average recall rate of 76% and an average precision rate of 90% on the initial models; and a higher average recall rate at 87% and an average precision rate at 28% on the final models. The reduced precision rate in the final models versus the initial models reflects the reality that the final models have large numbers of novel genes that have no experimental evidences and hence are not yet collected in the MetaCyc database. To demonstrate the usefulness of this server, we have predicted an iron homeostasis gene network of Synechocystis sp. PCC6803 using the server. The predicted models along with the server can be accessed at http://csbl.bmb.uga.edu/cinper/.We present a web-based network-construction system, CINPER ( Like other similar programs, P-MAP also faces the same pathway-hole problem as outlined above. In addition, the template pathways may represent only the core part of the target pathway so some genes of the target pathway may not be covered by its homologous pathways in other organisms. Hence, utilization of additional information such as operons, protein-protein interactions and gene expression data represents an important task in pathway prediction. Clearly if gene expression data is available, it can be used to possibly validate a predicted pathway. All these will need to be carried out in order to build a reliable and useful pathway model. To the best of our knowledge, no computer program is currently available in the public domain, which integrates all these data sources and capabilities into one software package to assist a user to construct and validate a biological pathway/network.We have previously developed a computational tool P-MAP http://csbl.bmb.uga.edu/cinper/. Throughout this paper, we will use “pathway” and “networks” interchangeably, with the same meaning.We present here an interactive web platform, CINPER, for prediction of a user-specified pathway in a prokaryote, which integrates multiple types of information, including (i) homologous template pathways of multiple related organisms that are in public pathway databases or published papers, (ii) functional annotation of genes, (iii) predicted operons, (iv) protein-protein interactions, (v) phylogenetic profiles and (vi) gene-expression data if available, for the target organism. CINPER essentially automates a manual prediction process ftp://ftp.ncbi.nih.gov/genomes/Bacteria, 01-24-2009). The DOOR database http://csbl1.bmb.uga.edu/OperonDB_10142009, v2). The SEED ftp://ftp.theseed.org/genomes/SEED/, 10-10-2010). The STRING database http://string-db.org/, v8.3). All the data will be updated annually or when a new release is available.All the genome sequences and annotations used here were retrieved from the NCBI FTP server  and the Django web framework (http://www.djangoproject.com). The advanced interactive features including name completion, dropdown menus, keyword filtering and multiple-column sorting in HTML tables are developed using JavaScript and the jQuery library (http://jquery.com). All the template information and the user account information are stored in a PostgreSQL (http://www.postgresql.org) relational database. CINPER runs on a Linux box (8 Intel Xeon 2.80 GHz and 8 GB RAM), and can also run on Microsoft Windows and Mac OS X. The web site includes a step-by-step tutorial (with screenshots) for general users. Also, to help new users to get started quickly, CINPER provides a sample template and network for the users to try. When a user has no template yet, a clickable hyper link to sample template networks will pop up, and the user can click on the link to make a copy of the sample template network in their work space and try the system with all functionalities that CINPER provides.CINPER is implemented using the Python programming language  retrieve relevant  template pathways by searching the entered keywords against the BioCyc CINPER supports two application modes for different purposes: the Template collection is the first step in pathway prediction. In this step, a user can import relevant (homologous) pathways from the specified public pathway databases, or enter the relevant genes that have been experimentally determined to be in the target pathway into the pathway-template table through the dropdown menu: “Add genes” on the template-pathway page. To do this, the user can search BioCyc All identified genes and interactions from the template pathways are listed in a table of the current session, which contains, for each gene and interaction, the gene symbol, its functional description as extracted from the NCBI Genome Database, and supporting evidence that the gene is relevant to the target pathway. Genes from different organisms are merged into one table, leaving to the user to decide the ones to keep or leave out through the user interface. Specifically, the user can select the relevant genes through specifying keywords in the search box in the display window. CINPER provides a utility tool to allow the table to be sorted according to any specified column. For example, a user may wish to find out the number of orthologous genes from different organisms for a particular gene in the table so she/he can sort the table by gene symbols and quickly find out the number. The user can also remove genes and interactions deemed to be irrelevant by clicking on the cross icon or edit the gene and interaction information by clicking the pencil icon.−6 as the default) for orthologous gene search against each organism if needed using the “Organism-specific mapping” form. For the user-specified target genome, CINPER retrieves the operon information from the DOOR operon database CINPER maps the template pathways onto the target genome through the menu “Derive a new model” in the dropdown menu “Go! Model” in the template-pathway page, which activates the P-MAP program E. coli as the template organism and B. subtilis as the target, and mapped 17 such pathways using BLAST E-value ≤10−6 as the cut-off, as shown in precision and recall defined as follows:TP is the number of template genes that are mapped to the known target genes, FP is the number of template genes that cannot be mapped by P-MAP or that have no matched target genes, and FN is the number of missed target genes by P-MAP. For example, for Chorismate biosynthesis I, Precision is equal to 100% by 6/(6+0) (0 is not shown in precision (90%) and recall (76%) rates in all 17 pathways correctly, indicating the high quality of the initial models.We have assessed P-MAP in terms of the number of genes mapped correctly to the target organism on all the pathways having at least 3 genes in MetaCyc The initial model generally represents the core part of the target network; the network expansion step aims to fill the pathway holes and recruit additional genes to make the model more complete. Our strategy is to recruit through “guilt-by-association”, which can be done using the dropdown menu “Expand” in the pathway-model page. CINPER uses four types of information to identify association relationship: co-operon For each pathway model, all the recruited genes are labeled with the source of the association information in the “Source” column in the pathway-model page, and the relevant connections in the graphic network display are color-coded, which allows the user to verify the reliability. If a gene is recruited by different information sources, it is listed in multiple lines accordingly. Notably, the genes recruited by association information may not always be related with the target network recall rate improved from 76% to 87% compared with the initial model. Co-operon information is the most useful while protein-protein interactions and protein-protein interaction seem to be complementary to the operon information based on the number of recruited genes that are verified in MetaCyc. Co-evolution and co-expression data have no contribution possibly due to the nonconserved phylogenetic profiles of the involved genes and the small coverage of conditions used to generate the available gene expression data.We have assessed the usefulness of each information type used for gene recruiting, using the 14 out of the 17 pathway-mapping examples in recall rate (87%) but a lower average precision (28%) compared the initial models at 76% and 90% respectively. We believe that the reduced precision rate of the expanded model is mostly an artifact resulted from the fact that the pathway models in MetaCyc are far from being complete. For example, we found that the following items are relevant with the target pathways but are not in MetaCyc, such as HemY (BSU10140) that is a peripheral membrane protein essential for protoheme IX synthesis heme biosynthesis from uroporphyrinogen-III pathway in ThiF (BSU11700) that is a thiamine biosynthesis protein HisH (BSU34890), an imidazole glycerol phosphate synthase and HisZ (BSU34930) a histidyl-tRNA synthetase, both of which are involved in the histidine biosynthesis It should be noted that the expanded models have a higher average CINPER allows a user to assess the consistency between a predicted network and the available gene expression data using the dropdown menu “Validate” in the network pathway-model page. From the menu, the user can select and upload a one-column file consisting of up- or down-regulated genes under conditions known to trigger changes in expressions of genes of the target pathway, which are identified in advance by the user from some relevant microarray data. If a gene in the expanded network model is included in the uploaded file, this gene is marked as “validated” in the column of “Evidence”, otherwise “predicted”.Synechocystis PCC6803. Iron plays an important role in living organisms in general, and it is an integral part of the heme structure and a cofactor in Fe-S proteins, which protect the cell from oxidative and nitrosative stresses and are involved in nitrogen fixation, hydrogen production and consumption, photosynthesis, and methanogenesis We have applied CINPER to predict the iron homeostasis network in Sinorhizobium meliloti (simply S. meliloti), Escherichia coli K12 (E. coli), Prochlorococcus marinus MED4 (P. marinus) and Synechocystis PCC6803 (S. PCC6803)  and protein-protein interactions (20 genes), respectively, as shown in nifS, sdhB, trxA) but different synonyms. We also validated our prediction using public transcriptomic data under iron limitation conditions We have collected, through an extensive literature search, 27 template genes known to be involved in the iron response systems in four bacteria: 803) see . 57 ironCreate a new blank template by clicking on the “New template” button in the home page of template networks.Collect template networks as listed in Add the template genes in two ways through the “Add genes” menu: (i) search the SEED and BioCyc databases using keyword “iron”; and (ii) enter manually any identified genes.Add the template interactions through the “Add interactions” menu: (i) search the KEGG pathway database using keyword “iron”; and (ii) enter manually any identified interactions.Revise the template networks by examining the genes and interactions in the corresponding panels and delete irrelevant genes and interactions.Construct the initial network model by clicking on “Derive a new model” in the “Go! Model” menu and adjust the parameter values for each organism if needed. The user will be directed to the web page of the derived initial network model.Expand the initial network by using different association information through the “Expand” menu, including the DOOR operon database, the STRING functional relation database, or user’s own regulon information.Validate the predicted network by uploading and applying the available gene expression data through the “Validate” menu.S. PCC6803 by the above procedure consists of the following components (see The model predicted for iron homeostasis in ents see :Fur (sll0567 and sll1937) Global regulator: AfuABC  FecABCDE  Iron transporters: bfr (slr1890);Iron storage protein: hemAB  and other iron utilization proteins, namely sdhAB (slr1233 and sll1625) and bioF (slr0917).Iron utilization proteins: Heme proteins including http://csbl.bmb.uga.edu/~xizeng/research.php?p=cinper1.All the data relevant to this example can be downloaded freely from We have developed an interactive web-based platform CINPER for gene network prediction, based on information from homologous pathways from related organisms, as well as specific information about the target pathway and general information about functional associations relevant to known genes in the target pathway. CINPER essentially automates an iterative manual prediction process by a domain expert using a set of tools and experimental data, and does so through an iterative process, which involves data analysis, computational prediction and user inputs. CINPER can be used for multiple purposes, including (i) construction of a desired gene network and (ii) pathway-hole filling. Evaluation of the program on 17 well-studied pathways in the MetaCyc database shows that it achieves an average recall rate at 87%, and an average precision rate at 28%, which the low precision rate, we believe, is mostly due to the fact that the pathway models in MetaCyc are far from being complete.CINPER is specifically designed to help to minimize the manual work by a user when reconstructing a network model using a set of automated tools and based on available data. We believe that this program provides a useful capability to prokaryotic biologists to build pathway models in a reliable and efficient manner. Further development of the program will be focused on updating data regularly whenever new versions are available and including additional pathway and functional relation information and improving its graphic network displays.File S1Template networks of iron homeostasis.(DOC)Click here for additional data file.


Pathways in biological system often cooperate with each other to function. Changes of interactions among pathways tightly associate with alterations in the properties and functions of the cell and hence alterations in the phenotype. So, the pathway interactions and especially their changes over time corresponding to specific phenotype are critical to understanding cell functions and phenotypic plasticity.With prior-defined pathways and incorporated protein-protein interaction (PPI) data, we counted PPIs between corresponding gene sets of each pair of distinct pathways to construct a comprehensive pathway network. Then we proposed a novel concept, characteristic sub pathway network (CSPN), to realize the phenotype-specific pathway interactions. By adding gene expression data regarding a given phenotype, angiogenesis, active PPIs corresponding to stimulation of interleukin-1 (IL-1) and tumor necrosis factor α (TNF-α) on human umbilical vein endothelial cells (HUVECs) respectively were derived. Two kinds of CSPN, namely the static or the dynamic CSPN, were detected by counting active PPIs.A comprehensive pathway network containing 37 signalling pathways as nodes and 263 pathway interactions were obtained. Two phenotype-specific CSPNs for angiogenesis, corresponding to stimulation of IL-1 and TNF-α on HUVEC respectively, were addressed. From phenotype-specific CSPNs, a static CSPN involving interactions among B cell receptor, T cell receptor, Toll-like receptor, MAPK, VEGF, and ErbB signalling pathways, and a dynamic CSPN involving interactions among TGF-β, Wnt, p53 signalling pathways and cell cycle pathway, were detected for angiogenesis on HUVEC after stimulation of IL-1 and TNF-α respectively. We inferred that, in certain case, the static CSPN maintains related basic functions of the cells, whereas the dynamic CSPN manifests the cells' plastic responses to stimulus and therefore reflects the cells' phenotypic plasticity.The comprehensive pathway network helps us realize the cooperative behaviours among pathways. Moreover, two kinds of potential CSPNs found in this work, the static CSPN and the dynamic CSPN, are helpful to deeply understand the specific function of HUVEC and its phenotypic plasticity in regard to angiogenesis. The advent of high-throughput technologies has encouraged the appearance of a lot of analysing tools, which were intended for interpreting gene expression data and extracting biological insight. No longer being limited to produce a ranked list of differentially expressed genes, most of those analysing tools are designed from the perspective of system biology and identify gene sets over-represented in biological processes -6. A genIn recent years, several efforts have been made to surmount the limitation of those methods. Global pathway crosstalk network (GPCN) based method  calculatActually, from current literatures, we found that pathway may interact with one other by means of five ways: sharing components , componeSo we incorporated distinct PPIs to derive interactions among pathways. By counting PPIs between corresponding gene sets of each pair of pathways, pathway interactions could be assessed. Furthermore, since no gene is involved in specific pathway in any case and no PPI works under any condition, we incorporated gene expression data to infer active PPIs, which have at least one differentially expressed corresponding gene, in certain case for distinct phenotype, such as angiogenesis. And then we constructed phenotype-specific pathway networks with the derived sets of active PPIs. Two sub phenotype-specific pathway networks were detected as angiogenesis-related characteristic sub pathway networks (CSPNs) to manifest the specific function of the cell and its phenotypic plasticity with regard to angiogenesis.Angiogenesis studied in this work, as the process of new blood vessel formation, is fundamental to reproduction, development, and repair. Strictly regulated by many inducers and inhibitors, it could be aroused when the local equilibrium between them is changed. At the phenotype level, angiogenesis is critical to inflammatory responses and cancers . In our To complement substantial information loss of restricting the pathway interactions to those by means of physical PPIs like binding or phosphorylation, we considered all the five kinds of pathway interactions. We gathered the PPI data from Human Protein Reference Database  and KyotGenerally, established pathways from various databases can be approximately divided into three categories: metabolic pathways, signalling pathways, and pathways of specific diseases or drugs . At leng(i) Generate the PPI network with 57092 interactions among 10204 proteins, which are gathered from HPRD and KEGG. (ii) Generate the reference of pathways from 'Environmental Information Processing' and 'Cellular Processes' categories of KEGG Pathway Maps. (iii) Exploiting GeneMerge method [(iv) Option: incorporate gene expression data to infer active PPIs. Under certain condition, only a part of the PPIs could be regarded as active interactions. We obtain the set of active PPIs with Microarray data. Then, the phenotype-specific pathway network could be constructed. (v) Count (active) PPIs between each pair of distinct pathways. A common gene between any two pathways will be treated as two discrete genes, which interact with same genes as that original common gene, belonging to these two pathways respectively. But the relationship of sharing itself is not considered as interaction. (vi)Assess the effect of PPI network itself as background to the number of PPIs between any pair of pathways. Firstly, those enriched pathways are permuted by randomly exchanging two distinct genes from two different pathways for 100,000 times. Both exchanged genes should interact with at least one gene. Secondly, perform step (iv) to count PPIs between each pair of pathways after permutation. This whole pipeline is repeated for 1,000 times. And then the background distribution of the number of PPIs between each pair of pathways is obtained. Additionally, there is a second way to permute pathways: just exchange pairs of genes which have the similar number of PPIs. And the results of these two approaches in step (iv) are approximately same. (vii) Determine which of the pathway interactions exist by calculating the corresponding empirical p-value. For interaction between any pair of pathways, firstly, count the number of permutations after which the PPIs between the pair of permuted pathways is higher than or equal to that of the pair of real pathways. Secondly, divide the number by 1,000 which is the total number of permutations and obtain the empirical p-value of that interaction. All interactions with p < 0.05 are used to construct the pathway network. (viii) Option: detect CSPNs from phenotype-specific pathway networks. To detect static CSPN, we firstly identify the common static sub pathway network where nodes and edges appear at each time point of each network, then identify highly connected pathways in this sub pathway network. Along with prior biological knowledge and the degree distribution of pathways in comprehensive pathway network, part of these highly connected pathways and their interactions could be detected as static CSPN. To detect dynamic CSPN, at first, find pathways among which interactions alter obviously, then, from found pathways, further discover pathways which have relatively low degrees in comprehensive pathway network. Along with prior biological knowledge, part of these discovered pathways and their interactions could be viewed as dynamic CSPN.The workflow for constructing pathway network was illustrated in Figure e method , produceSince angiogenesis could be aroused by different stimulus, the CSPNs for angiogenesis would be more credible if more data about different stimulus were used. Finally, two series of time course gene expression data, GSE973 and GSE9055, about human umbilical vein endothelial cells (HUVEC) after stimulations of IL-1 and TNF-α were obtained from the Gene Expression Omnibus (GEO). GSE973  has fourWe first collected 48 pathways from 'Environmental Information Processing' and 'Cellular Processes' categories of KEGG Pathway Maps. We identified 43 pathways which show significant enrichment in the list of 10204 genes involved in the generated PPI network. To some extent, these 43 pathways have relatively high ratio of proteins which have at least one interaction with other proteins, which could explain why they are enriched in the genes set of PPI network. Then a comprehensive pathway network is constructed . We will go further in the future to refine our work from this respect. In addition, the detection of CSPNs for specific phenotype requires ruling out the effect of other factors such as the difference of stimulus and experimental bias. As a result, the quantity and quality of corresponding time-course microarray data are asked to be appropriate to obtain reliable CSPNs. But now even the demand of quantity can not be met. We also will go further in the future to provide related active PPIs for each pathway interaction and then predict the pathway interaction type.The comprehensive pathway network helps us realize the cooperative behaviour among pathways by providing most of possible interactions among pathways. By combining phenotype-specific gene expression data, two kinds of potential CSPNs detected in this work, the static CSPN and the dynamic CSPN, are helpful to deeply understand the specific function and phenotypic plasticity of angiogenesis.The authors declare that they have no competing interests.Y. Huang performed the computational works. S. Li conceived and designed the research. Y. Huang and S. Li analyzed results drafted the manuscript.Comprehensive pathway network. Red nodes: hub pathways.Click here for fileComparison of dynamic CSPN at different time points. A: Dynamic CSPN at 0.5 hour in IL-1 case. B: Dynamic CSPN at 1 hour in IL-1 case. C: Dynamic CSPN at 2.5 hours in IL-1 case. D: Dynamic CSPN at 6 hours in IL-1 case. E: Dynamic CSPN at 0 h 15 min in TNF-α case. F: Dynamic CSPN at 1 h 15 min in TNF-α case. G: Dynamic CSPN at 3 h 15 min in TNF-α case. H: Dynamic CSPN at 8 h 15 min in TNF-α case.Click here for filePathway interaction at different time point(s) in IL-1 case. First and second columns are end nodes (pathways) of corresponding edge (pathway interaction). Third column is the time points at which interaction exists.Click here for filePathway interaction at different time point(s) in TNF-α case. First and second columns are end nodes (pathways) of corresponding edge (pathway interaction). Third column is the time points at which interaction exists.Click here for file