High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information. High-throughput experiments, which generate large and complex data sets, are routinely performed in modern biological and clinical studies to unravel mechanisms underlying complex diseases, such as cancer. However, extracting reliable and meaningful results from these experiments is usually difficult and requires sophisticated computational tools and algorithms, which are challenging for experimental biologists to comprehend. A user-friendly software tool is extremely important for both bench and computational biologists to perform high-throughput data analysis related to cancer and other complex diseases.2. Pathway- and network-based data analysis approaches project information about seemingly unrelated genes and proteins onto pathway and network contexts, and create an integrated view for researchers to understand mechanisms related to phenotypes of interest.Many studies have shown that alterations in pathways or networks are better correlated with complex disease phenotypes than any particular gene or gene product3 for doing network-based data analysis. The FI network was constructed by merging interactions extracted from human curated pathways with interactions predicted using a machine learning approach. This tool can also be used to perform pathway-based data analysis by using high quality human-curated pathways in the Reactome database4, the most comprehensive open source pathway database.In this paper, we describe a software tool called ReactomeFIViz , which can be used to perform pathway- and network-based data analysis for data generated from high-throughput experiments. This tool uses the highly reliable Reactome functional interaction (FI) networkhttp://www.mysql.com). The middle server-side application uses hibernate (http://hibernate.org) to access the databases storing FIs and cancer gene index data (see below). The server-side application also uses the in-house developed Reactome API for Object/Relational mapping to access pathway-related contents stored in a database using the Reactome database schema. On the server-side, a lightweight servlet container, Spring Framework (http://projects.spring.io/spring-framework/), and a Java RESTful framework, Jersey (https://jersey.java.net), are used to power a RESTful API for the Cytoscape front-end. The front-end Cytoscape app uses this RESTful API to communicate with the server-side application. Almost all analysis features in the app are provided by this RESTful API, which should also facilitate their use by other front-end applications, such as a web browser or tablet app.We used conventional three-tier software architecture to implement ReactomeFIViz . The bachttps://wiki.nci.nih.gov/display/cageneindex) data into a MySQL database and then developed a hibernate API for the server-side application. The CGI data contains annotations for cancer-related genes. These annotations were extracted by using text-mining technologies and then validated by human curators (https://wiki.nci.nih.gov/display/cageneindex/Creation+of+the+Cancer+Gene+Index).For cancer data analysis, we imported the cancer gene index  is used in the server-side for executing network module-based survival analysis and other statistical computations. ReactomeFIViz uses Java based methods in the server-side to call functions in R. Users of our app don’t need to install R in their machines in order to perform the statistical analyses implemented in the app.R  to Java and R. For details about other algorithms and their implementations, please refer to our previous work7.ReactomeFIViz implements multiple features for users to perform network-based data analysis, including FI sub-network constructionThe majority of interactions in the Reactome FI network are extracted from reactions and complexes. In order to display semantic meanings  of these interactions, we created a Reactome FI network specific visual style. This visual style is registered as a service using the OSGi API supported by Cytoscape 3, and applied to newly constructed FI sub-network automatically for network analysis.8. In April 2014, we added a new experimental feature for performing integrated pathway analysis for multiple genomic data types by adapting a factor graph based approach called “PARADIGM”9 into ReactomeFIViz.Since version 4.0.0.beta, released in January 2014, ReactomeFIViz allows users to explore a list of high quality, human curated Reactome pathways, visualize Reactome pathways directly in Cytoscape, and perform pathway enrichment analysis on a list of genes based on a binomial test4. Pathway diagrams in Reactome are drawn based on biochemical reactions. A reaction usually contains multiple inputs and outputs, in addition to catalysts, inhibitors and activators. The network model in Cytoscape is designed to support simple graphs containing edges between two nodes only. In order to display Reactome pathway diagrams, we adapted the pathway diagram view in the Reactome curator tool4 into the Cytoscape environment, and wrapped it in a JInternalFrame so that a pathway view can be displayed along with a network view in the Cytoscape desktop . For this data set, we chose the 2009 version of the FI network, and picked genes mutated in three or more samples to construct a FI sub-network. We performed a network clustering, followed by survival analysis for each network module by splitting samples into two groups: samples having genes mutated in the module (Group 1) and samples not having genes mutated in the module (Group 0). Our results indicate that group 1 samples (11) have significantly longer overall survival times compared to group 0 samples  based on module 3. Pathway enrichment analysis results imply that module 3 is enriched with genes in calcium signaling pathway (http://www.genome.jp/kegg/pathway/hsa/hsa04020.html) and mitotic G2/M transition (http://www.reactome.org/cgi-bin/control_panel_st_id?ST_ID=REACT_2203.2). These results suggest that mutations impacting calcium signaling and the cell cycle may increase the survival of ovarian cancer patients. However, we may need more samples and independent data sets to validate our conclusion.As an example, we present results generated from network module based analysis for the TCGA ovarian cancer mutation data-3 from CoxPH), which implies that our method is fairly robust against updates of the FI network. For details, see the supplementary results.Using the same version of ReactomeFIViz but different versions of the FI network may yield different results because of updates of protein interactions in the FI network. We performed the same analysis with the latest version of the FI network (the 2013 version), and found that genes in module 3 from the 2009 version of the FI network have been split among several modules discovered using the newer version of the FI network. The module having the largest overlap with module 3 from the 2009 version of the FI network has the most significant p-value from the survival analysis .License: the Creative Commons Attribution 3.0 Unported License . Since then, both Cytoscape extensions, the plug-in and the app, have actively been used by users. This has also been evident by the positive reviews the App has received on the Cytoscape App Store. Considering the rich functionality and the ease of cancer genomics analysis that the App provides to the users, we believe this app is of interest to many researchers working in the fields of Computational Biology, Cancer Genomics and Systems Biology.The previous version of the App, which was compatible with Cytoscape 2, was introduced as a supplemental software tool in an earlier study... pathways from Reactome and other pathway databases...“. The paper, however, creates the impression that the App is highly dependent on the Reactome infrastructure and does not allow communication with other databases. We suggest that the authors remove“and other pathway databases” from the abstract or better clarify this point in the abstract.In the abstract, the authors say “The details about the type of analyses and the statistical tests the App enables are provided in an earlier paper by the same group , but not in this paper. For readers that are interested in learning these details, we suggest the authors to add a sentence to the manuscript and refer to their earlier work for details. If the functionality and the implementation of these tests have changed since then, we suggest that the authors clearly list these new improved items in the paper for more clarification.As an example use case, the authors provide the details of a re-analyzed data set and mention that the results of these two analyses differ due to changes in Reactome FI networks. Curated databases are, of course, subject to changes over time; but it is not clear from the text whether it was the changed network that was causing the problem or the new version of the extension. We suggest that the authors provide the results of these two runs as a supplement to their paper for users to compare and contrast.Implementation section says that the analyses were conducted in R, but can the authors clarify the requirements for this App? Do users, for example, need to install R to use this App? Related to this, the authors do not talk about the Cytoscape version that the App targets. Is the Cytoscape 2 PlugIn deprecated? Do authors suggest that users install the newer version in Cytoscape 3 as an App?The last sentence of theFigure 1 provides details about the implementation and the architecture of the ReactomeFIViz App, however we think the manuscript needs a simple user flow diagram that shows where different types of data are obtained, the functionalities of the App and the output the users get. The mutation-based module discovery and differential survival analysis examples mentioned in the paper are good use cases, however it is not clear from the text what the App supports other than these examples.Finally, we suggest the authors to provide a supplemental step-by-step guide to replicate the results that they describe in the paper. For a new user, this may provide a good base to start using the software and for many researchers it might be more convenient to have such an article provided as a supplementary file to this paper.There is already extensive documentation about the App on the Reactome web site, however the manuscript fails to provide a general overview for non-experienced users. We have the following suggestions for the authors and if addressed, we believe, these will considerably improve the manuscript:We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Thanks a lot for your review of our article. We have made changes according to your thoughtful suggestions. Please see details below:In the abstract, the authors say “... pathways from Reactome and other pathway databases...“. The paper, however, creates the impression that the App is highly dependent on the Reactome infrastructure and does not allow communication with other databases. We suggest that the authors remove “and other pathway databases” from the abstract or better clarify this point in the abstract.Although the app uses a software infrastructure running on Reactome, the curated pathways it uses for the network and pathway enrichment analysis come in equal parts from Reactome and other pathway databases. We have clarified this in the abstract.The details about the type of analyses and the statistical tests the App enables are provided in an earlier paper by the same group , but not in this paper. For readers that are interested in learning these details, we suggest the authors to add a sentence to the manuscript and refer to their earlier work for details. If the functionality and the implementation of these tests have changed since then, we suggest that the authors clearly list these new improved items in the paper for more clarification.We have added references to our original FI network paper in the paragraph introducing “Network analysis features”. Also, we added the date that version 4.0.0.beta was released in order to indicate that pathway analysis features are new features that have not been covered by our previous work.As an example use case, the authors provide the details of a re-analyzed data set and mention that the results of these two analyses differ due to changes in Reactome FI networks. Curated databases are, of course, subject to changes over time; but it is not clear from the text whether it was the changed network that was causing the problem or the new version of the extension. We suggest that the authors provide the results of these two runs as a supplement to their paper for users to compare and contrast.We modified the sentence in the last paragraph describing different results using different versions of the FI network to make it clear that the same software, but different versions of the FI network, were used. We also deleted this sentence in the implementation section, “Each version is handled by its own web application on the server-side for easy software maintenance.”, to avoid confusing the readers with technical details.As suggested, added a supplementary document to describe results from the 2013 version of the FI network. The last sentence of the Implementation section says that the analyses were conducted in R, but can the authors clarify the requirements for this App? Do users, for example, need to install R to use this App? Related to this, the authors do not talk about the Cytoscape version that the App targets. Is the Cytoscape 2 PlugIn deprecated? Do authors suggest that users install the newer version in Cytoscape 3 as an App?We have clarified the section to make the requirements clearer, and added a new paragraph to address the relationship of the application to Cytoscape 2 and 3.Figure 1 provides details about the implementation and the architecture of the ReactomeFIViz App, however we think the manuscript needs a simple user flow diagram that shows where different types of data are obtained, the functionalities of the App and the output the users get. The mutation-based module discovery and differential survival analysis examples mentioned in the paper are good use cases, however it is not clear from the text what the App supports other than these examples.We have added a new paragraph at the top of the Results section to highlight some major features in the App, and created a new diagram to show these major features as suggested.Finally, we suggest the authors to provide a supplemental step-by-step guide to replicate the results that they describe in the paper. For a new user, this may provide a good base to start using the software and for many researchers it might be more convenient to have such an article provided as a supplementary file to this paper.The application’s web-based tutorial already has provided very detailed, step-by-step instructions that replicate the results. We have added a new sentence in the Data availability section to point this out, and the README file contained in the zip file for downloading now points readers interested in replicating the analysis to the online tutorial.  This article describes version 4.1.0 of the Reactome Functional Interaction "plugin"/"app" for the Cytoscape network exploration/analysis Java platform. The Reactome Functional Interaction network is an established, well-received and widely used resource. The software described here provides useful resources both for exploring Reactome networks and for using them in network enrichment analyses.My only reservation about this article is that its brevity makes it sketchy. For example, how are pathway enrichment statistics calculated? Does the implementation of HotNet offered here differ in any way from the original?pread into several modules in the newer version of the FI network". Why does this happen and how can users guard against it?I am especially intrigued that the example use-case given is reported to work well with the 2009 version of the ReactomeFI network, but apparently the discovered modules cannot be found with the newer versions of the FI because the underlying interactions have been "sI was also not able to explore/reproduce the example presented in the paper, because (contrary to its ".txt"extension) the MAF file provided did not appear to be plain text and the needed survival data file was not included in the zip file provided. A 'README' file with instructions may be useful here.P.S. The multiple names given to this resource - "ReactomeFIViz " - are a little confusing and seem unnecessary.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. My only reservation about this article is that its brevity makes it sketchy. For example, how are pathway enrichment statistics calculated? Does the implementation of HotNet offered here differ in any way from the original?We have now stated that a binomial test is used for pathway enrichment analysis in the “Pathway analysis features” section and cited a reference (reference 8) for it. We also added a sentence to point out that our implementation of HotNet was done by porting the original Python and MatLab code to Java and R, and so the original algorithm is unchanged. I am especially intrigued that the example use-case given is reported to work well with the 2009 version of the ReactomeFI network, but apparently the discovered modules cannot be found with the newer versions of the FI because the underlying interactions have been "spread into several modules in the newer version of the FI network". Why does this happen and how can users guard against it?Because new interactions have been added into the latest version of the FI network, these new interactions may change the network clustering results. However, we still find some network modules, which overlap significantly with the original module and have significant p-values, though higher than the original one, from survival analysis. As suggested by another reviewer, we have added a supplementary document to describe results from the 2013 version of the FI network. As stated in the manuscript, we recommend that the user should use the latest version of the FI network, and check with previous versions of the FI network to investigate the stability of found network modules.I was also not able to explore/reproduce the example presented in the paper, because (contrary to its ".txt"extension) the MAF file provided did not appear to be plain text and the needed survival data file was not included in the zip file provided. A 'README' file with instructions may be useful here.We apologize for the problem. It turned out that the zip file was collapsed somehow. We have fixed the problem, and added a simple README file as suggested.The multiple names given to this resource - "ReactomeFIViz " - are a little confusing and seem unnecessary.Because of the development history of this software, the same app has been called different names. For example, prior to Cytoscape 3, all Cytoscape extensions were called “plug-ins”, but the nomenclature has now been changed to “app”. We have tried to minimize the confusion by using ReactomeFIViz consistently and referring to the names of earlier versions of the software just once. Thanks a lot for your comments. We have made changes according to your comments. Please see details below:


Modeling of signal transduction pathways is instrumental for understanding cells’ function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells’ biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways’ logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein–protein interaction networks and to provide meaningful biological insights. Signaling pathways are of the utmost importance for understanding cellular function and predicting response to environmental perturbations –7. ExtenMost of the computational methodologies for reconstructing signaling pathways based on proteomic data, first employ a modeling methodology to describe formally how signal propagates from one protein to the next in the pathway, and then employ a parameter estimation approach to identify optimal values of the model parameters, in an attempt to make the model best fit the measured data. Common approaches for modeling signal transduction include modeling via Ordinary Differential Equations (ODEs) –11, probDepending on the modeling formalism used, different parameter estimation methods are best employed for identifying the model parameters. Typically, ODE modeling is best coupled with sensitivity analysis methods, while logic modeling is best coupled with optimization methods. In sensitivity analysis, first bibliographic values for the kinetic constants are used, and then the model is simulated under small variations of these parameters providing an estimate of the parameter values that best reproduce the experimental data . A form These mechanistic methodologies, can be implemented without the incorporation of experimental data. Nevertheless, their predictive efficiency is limited due to constraints imposed by the accuracy of the proteins’s connectivity in the pathways used as a scaffold , 22. As The approach described herein, is based on graph theory, which serves as a powerful mathematical modeling tool for the analysis of biochemical networks. Such networks consist of entities that represent several type of biomolecules, as proteins and genes –32. OverOur approach constitutes a novel methodology for modeling medium and large scale signaling pathways based on experimental data and is meant to serve as an alternative to the logic modeling-optimization pipeline. More specifically, we start with a set of dependencies between signaling molecules as extracted from the experimental data and then using a breadth-first transversal of the graph, we compute the shortest paths of the prior knowledge network that fit these dependencies. Moreover, using several algorithmic approaches, we are able to handle conflicts in the dependencies to extract the most meaningful  pathways.V + E), let V stand for the total number of the vertices in the graph and E for the total number of the edges. As a downside, we cannot guarantee global optimum, but using heuristic algorithms, we are able to identify the minimum supersets of the graph, where the optimum solution lies in.Instead of an optimization algorithm or a sensitivity analysis approach, using a breadth-first transversal approach does not force the user to decide on a mathematical formalism, since most of the pathways are obtained in a graph form. Thus, adopting a mathematical formalism is a logic leap. Furthermore, since optimization algorithms are typically NP-hard problems, to impose the experimental dependencies, we take advantage of the breadth-first transversal complexity of O and relations between them . When it comes to modeling of biochemical networks, these entities typically represent proteins, genes or other type of biomolecules , 35. In xrc represents a Boolean variable signifying the measured state of the signal c upon stimulation with molecule r, where 0 indicates unchanged phosphorylation level and 1 activated phosphorylation level. Note that the proposed approach manages a qualitative description of the experiments and handles single treatment data (no stimuli combination). In In our framework, an experiment is defined as a set of experimental scenarios coupled with a set of measurements for each scenario and is represented as a data matrix, where each row (r) correspond to a stimulus used to perturb the cells and each column (c) to a measured signal. Each matrix element xrc = 1) in the data matrix, we infer a causal relationship or dependency between rth signaling molecule and cth signal. All the dependencies are collected, as a list of subordinate relationships between a source node (stimulus) and a target node  and are indexed by k. The symbol → implies a desired intermediate pathway. According to For every phosphorylation event observed  steps . B. BV3) sIn our toy model, we start with a list of fourteen dependencies, as presented in To identify Alternative Paths, satisfying the remaining dependencies, we developed a custom searching method, based on the Breadth-First-Search algorithm. The main idea is to find alternative starting points through BFS searching method and then to use Floyd-Warshall matrix to find conflict free paths. We begin from the stimuli level, exploring all the neighboring nodes. Then, for each of those nearest nodes, we explore their unexplored neighbor nodes, and so on, until we reach the measured signal. At each iteration of BFS, we check the Floyd-Warshall matrix for conflict free pathways, accessing new shortest paths from the new starting points towards the measured signals. If such pathways are not identified, we implement a pathway scoring method to satisfy the remaining dependencies.Our scoring method is a systematic way to check and score the paths that can satisfy the unmet dependencies. For each unmet dependency and for every potential pathway satisfying this dependency (starting from the shortest and continuing to the Alternative Paths produced in the previous steps) we check whether the number of dependencies served by a specific pathway is greater or equal to the number of the conflict nodes introduced to the network. If this is the case, the path is included in the compressed model and the searching continues to the next unmet dependency.Finally, if there is still a list of unmet dependencies, we continue applying the same scoring methodology, only this time we nullify the detected conflict nodes, which have already been included in the pathway reconstruction of previous steps. The main idea underlying this intuition is that some conflict nodes may not facilitate the satisfaction of a single dependency, but a combination of them, contributing in the cumulative satisfaction of multiple dependencies.cth measured node is reachable under stimulation of rth perturbed node, based on the compressed model’s topology, we set the corresponding matrix element Having identified the subset of reactions comprising the derived model, we access the quality of our solution. To do so, we calculate the goodness of fit, as a metric of the quality of our approach. We compute a compressed model matrix, where each row corresponds to a stimulus used to perturb the cells and each column to a measured signal. Apparently, this new matrix and the experimental data matrix have the same size. If the nst be the total number of stimuli, nsig the total number of the signals and ns the total number of the experimental measurements, we calculate the percentage error as:Let http://www.graphviz.org/), as an overview graph allows users to quickly visualize hypotheses and shows how they are related to each other. MatLab source code for this method, along with a detailed manual and examples of use, is available at http://ntuabiolab.wikispaces.com/Software.We aim to develop a readable and maintainable code that can serve as an entry point into computational biology. All programming procedures were written in the MatLab environment, therefore run in all three most applicable operating systems: GNU/Linux, Microsoft Windows and Apple Os X. The developed algorithm is very easy to use; the user has to provide four files to define network training problem: (i) the network topology in a.txt format using a tab perimeter between the two components of each reaction, (ii) a.txt file containing the stimuli of the experiment, (iii) a.txt file containing the signals of the experiment, and (iv) a.txt file containing the experimentally measured state changes for each scenario. The user may then run the implemented code, as described herein, and a.dot file is produced for the visualization of the final solution. Graph visualization, which is a way of representing structural information as diagrams of abstract graphs and networks, holds a great impact in our approach. Thus, the analysis results are communicated using the open source graph visualization software Graphviz  and the inflammatory ligand IL1B cause several activations, including both stimulation factors implying a P38MAPK, NFKB and CREB1 activation. On the other hand, both Tumor Necrosis Factor alpha (TNFA) and Interleukin 6 (IL6) activate only NFKB signal. Additionally, it is likely that PKNs often lack interactions that are supported by data. Indeed, we observe this particularity in the case of the TNFA → SHP2 causal dependency, a pathway not supported by the initial topology. As stated above  to reach the measured signal CREB1. Additionally, with the edge MAP3K → IKK, the model structure comprises an activation route from IL6/TGFA stimuli to IKBA/NFKB signals. We also observe two alternative routes  to reach MAP3K node starting from GRB2 node.The current approach aims to adapt the network structure to the data in a automatic way in order to resolve the discrepancies between model and data. Our algorithm was able to detect such discrepancies in the original topology, such as the measured node SHP2 in the experimental dependencies IL6 → IKBA and IL6 → NFKB and IKBA in the dependencies TNFA → NFKB and TGFA → NFKB. To handle these mismatches, our model applies a ranking method for the remaining dependencies, as detailed in Methods. For example, for the case of the SHP2 mismatch in the IL6 → IKBA/NFKB pathways: 2 dependencies  served by introducing 1 conflict (SHP2) and for the case of the IKBA mismatch in the TNFA → NFKB pathway: 1 dependency (TNFA → NFKB) served by introducing 1 conflict (IKBA). The latter demonstrates that the draw cases are favoured by our approach, since there is limited evidence to support these causal links. In any case, dedicated experiments are required to support or prove these suggestions.Training against data generally wields a model having a substantially better fit than the PKN, which is also the case with our data and model. The untrained ensemble containing all possible interactions exhibited a poor fit (59% across the dataset), whereas our trained model halved the initial error (29%), in a total time of 5 seconds. Exploring possible explanations and further estimations for a better fit of our trained model, we inspect A view of this new solution is presented in To summarize, through our computational framework, we validated essential biological patterns of the network structure, indicating important aspects of the signaling pathways in hepatocytes. For instance: (1) STAT3 is not activated by TGFA; (2) Phosphorylation of the autocatalytic domain of P70S6 (termed P70S6K in the model) is independent of ERK1; (3) The activation of CREB1 in response to TGFA is likely to be caused by a MEK1 independent route; 4) Phosphorylation of AKT1 is regulated to PI3K activity; and (5) The activation of MEK1 and ERK1 is dependent to MAP3K activation. These results, generated in a automated way, confirm several conjectures formulated in [ PhosphorAll the connections in the solution are biologically relevant as they have been mined from literature. However to validate the functionality of the connections in this specific context, follow up experiments are required where key signaling proteins are blocked and the activation of downstream proteins is monitored to deconvolute their connectivity in the network, in similar fashion to the validation study performed in . In thisIn the “Supporting Information” section we provide additional arguments to support the interpretation of the medium scale network discussed in this section. To evaluate the sensitivity of our model, we performed a cross-validation analysis, to assess how the results of our method generalize to independent data sets. We tested our algorithm using 500 randomly generated datasets emulating actual experimental data. The number of activations in these matrices was kept constant and equal to the original matrix presented in Next, we evaluate the performance of our methodology in reconstructing large topologies based on phosphoproteomic data. Large scale networks represent cellular function from a systems perspective and integrate in full detail all signaling events that determine cellular response . They tyIn this article, we introduce a novel approach, based on a graph algorithmic formulation, in order to construct cell-type specific pathways and to link signaling data to cellular responses. For the pathway construction, we take some generic networks of different scales and we combine them with experimental data. As a result, we present a new framework for interrogating and training signaling networks based on measurements from stimulus-signals experiments.To do so, we investigated the possibility of shortest and alternative pathways finding, through the fundamental algorithms of graph theory. Applying this approach showed that through Warshall-Floyd algorithm and a Breadth First Search (BFS) traversal of the network, our method manages to construct a final topology, which satisfies as many experimental dependencies and achieves a significant fit of the data at hand. We defined the concept of network conflicts, as the inconsistencies between measurements and prior topologies . TherefoWe presented our algorithmic procedure via a toy model demonstration, to make sure of the accuracy and the consistency of the solutions provided. Then, we combined data from realistic biological experiments with initial signaling topologies in order to eventually create networks that simulate the cell signaling process. We chose a medium scale and a large scale network, to evaluate the method’s features and to ascertain its ability to model any scale network. The products of our approach are computable models, which constitute a proof-of-principle that the proposed methodology can efficiently interrogate protein expression data.To assess the computational sensitivity of our approach, we have also applied a cross-validation statistical analysis, in order to prove that our outputs are in conjuction only with the measurements and, thus, the probability of random solutions is excluded. We reached statistical distributions of expected forms describing in proper way the behavior and the incidence of the interrogated network’s reactions. Finally, to demonstrate the power of our approach, we proceeded to a comparison with the ILP method , 44. SevV + E) to impose the experimental dependencies. Furthermore, since this approach is only algorithmic, it does not require any optimization software  or environment to run and can be executed by any computer in any programming language implementation.To the best of our knowledge, the method introduced here, is the first that uses such an approach directly on interaction graphs to systematically interrogate and train the wiring diagrams of signaling networks. Since our methodology is strictly graph theory based, it displays limitations of static quality . AnSignal transduction combines information regarding the activity state-concentration of the signaling components and the location of these components in the biochemical topologies. In the Introduction section, we referred to several modeling methodologies , which are valuable in describing the spatial dynamics of these signaling components and how the spatial signaling information is transduced from upstream to downstream components, within a known topology. However, when it comes to signal transduction, the role of the network topology is not easily deduced from such studies . On the Overall, the approach described, successfully addresses the reconstruction of medium and large-scale signal transduction networks and provides a natural framework to enable the integrated analysis of proteomics. It allows the fast prediction of signaling topologies by combining the nature of graph theory with the flexibility of the pure programming, providing results comparable with the state-of-the-art optimization method ILP.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.S3 Text(DOCX)Click here for additional data file.S1 FigObservable-controllable part of the original topology. Numbers 57 species, 88 reactions and serves as starting point for the Cross-Validation analysis described in the “Results” section.(EPS)Click here for additional data file.S2 FigWe present the statistical analysis for the medium scale network. We ran the Cross-Validation analysis 500 times. In Y-axis we present the reactions incidence, while in X-axis we present the network reactions classified from the larger incidence to the smaller. Additionally, in blue we visualize the network reactions incidence after the 100% of the total runs, while in red we visualize the network reactions incidence after the 50% of the total runs. The main purpose is to demonstrate that our computational framework is sensitive to changes in experimental design (hence the random data generation), preserving the same generic topology and, thus, it does not favor the selection of specific network subsets. The inclusion of 50% and 100% cases, held to reach a convergence threshold prediction of these incidences.(TIFF)Click here for additional data file.S3 FigWe present the statistical analysis for the medium scale network. We ran the Cross-Validation analysis 500 times. In Y-axis we present the reactions incidence, while in X-axis we present the network reactions classified from the larger incidence to the smaller. Additionally, in blue we visualize the network reactions incidence after the 100% of the total runs, while in red we visualize the network reactions incidence after the 50% of the total runs. The main purpose is to demonstrate that our computational framework is sensitive to changes in experimental design (hence the random data generation), preserving the same generic topology and, thus, it does not favor the selection of specific network subsets. The inclusion of 50% and 100% cases, held to reach a convergence threshold prediction of these incidences.(EPS)Click here for additional data file.S4 Fighttp://www.graphviz.org/).The canonical pathway was constructed from literature. The experimental scenarios consist of 25 stimuli and 88 measured key phosphoproteins, as described properly in “The species translation challenge-A systems biology perspective on human and rat bronchial epithelial cells” . Numbers(EPS)Click here for additional data file.


DNA methylation is a well-established epigenetic biomarker for many diseases. Studying the relationships among a group of genes and their methylations may help to unravel the etiology of diseases. Since CpG-islands (CGIs) play a crucial role in the regulation of transcription during methylation, including them in the analysis may provide further information in understanding the pathogenesis of cancers. Such CGI information, however, has usually been overlooked in existing gene-set analyses. Here we aimed to include both pathway information and CGI status to rank competing gene-sets and identify among them the genes most likely contributing to DNA methylation changes. To accomplish this, we devised a Bayesian model for matched case-control studies with parameters for CGI status and pathway associations, while incorporating intra-gene-set information. Three cancer studies with candidate pathways were analyzed to illustrate this approach. The strength of association for each candidate pathway and the influence of each gene were evaluated. Results show that, based on probabilities, the importance of pathways and genes can be determined. The findings confirm that some of these genes are cancer-related and may hold the potential to be targeted in drug development. DNA methylation (DNAm) occurs when a methyl group is added to the cytosine in CpG dinucleotides. The presence of such methyl groups can modify DNA and thus alter gene expression. Because DNAm is more stable than gene expression and can represent long-term environmental influences, it may serve as a good candidate biomarker for disease diagnosis, disease prognosis or therapy response prediction113DNAm patterns depend on whether CpGs locate in CpG islands (CGIs)567781011611pathway topology, the Impact Factor was later proposed to measure changes in a given pathway when perturbation occursAnother issue of concern in the analysis of DNA methylation, as in any analysis of genetic studies, is the need to incorporate biological information for more intuitive and appropriate interpretations. For instance, since most genes do not work alone, inclusion of known information about relationships among a set of genes can help scientists to better evaluate the effect on disease development of the set of genes working jointly. In their consideration of pathways as gene-sets, most current analyses treat the genes located in the same pathway as independent entities. Examples include the knowledge-based pathway analyses, such as over-representation analysis (ORA) and functional class scoring (FCS), which investigate whether certain genes of interest are differentially expressed in given pathways. These approaches consider the pathway simply as a set of independent units, ignoring possible interaction and regulation between components in the same pathway. To account for the position of a gene in a pathway, sometimes called the et al.20Another concern arising in the analysis of DNA methylation is confounders. Factors underlying observed variability in DNA methylation include tissue type, cell type, age, and gender16171819162122in situ (HG-DCIS), from a breast cancer studyTo simultaneously include both CGI status and pathway information in the association analysis, as well as to account for heterogeneity across subjects and possible confounders, we propose functions to model the CGI information and pathway knowledge, under an age-matched case-control study design. We adopt the Bayesian approach for its flexibility in modeling stochastic epigenetic variation, such as random individual effects52520β values. The procedures for data management—outlier detection and removal of batch effect, normalization, and matching—are explained in An overview of the procedures, including subject matching, examination of CGI status, and construction of gene-set information, is outlined in in situ of 23 breast cancer patients and 5 normal controlsThe second application involves methylation profiling from tumor and adjacent normal tissues of 32 patients with lung adenocarcinomaθij from 100 randomly selected probes were constructed , supporting the assumption that CGI probes and non-CGI probes are not homogeneous. Moreover, probes of the same CGI status tend to have similar values of θij. βj for probe j can be assumed to come from one of two distributions, depending on the CpG island status of the probe. Similarly, the pattern of heterogeneous variation is apparent in the differences in DNAm among the 32 pairs of the lung cancer data. The boxplots of θij from 200 randomly selected probes in CGIs and from 200 not in CGIs show different degrees of variability, as seen in To examine by figures whether DNA methylation varies according to CpG island status, boxplots of the pair-wise differences in DNAm structed  for the For the UKOPS study, we selected ten competing pathways defined in KEGG (Kyoto Encyclopedia of Genes and Genomes). The ten sets involve 1,675 probes in 795 genes and the ten pathways have been reported to associate with ovarian cancerβj for each probe Pj, where j = 1, …, 1,675 for the UKOPS data. Each probe Pj was examined first to see if its corresponding gene Gj falls in the kth pathway where k = 1, …, 10. Note that the same gene can occur in more than one pathway, i.e. the indicator function Ik(P)j can be 1 for more than one k. Ik(P)j = 1) if Pj is in a CGI, Ejk is the number of neighbors of gene Gj in the kth pathway. No differentiation is made between the incoming and outgoing edges in the current settings. For selecting the best model, we adopt deviance information criterion (DIC), a common measure for Bayesian model selection. Other details of the specifications, computations, and the codes to be used in the R package R2OpenBUGS are listed in To illustrate how to evaluate the pathway effects, we denote first the CGI-dependent effect γk, we derived the estimated posterior probability of positive γk for each pathway, conditioning on the observed methylation levels θ = θij. If the probability P(γk > 0|θ = (θij)) is close to 1, it implies that, on average, genes in this pathway were more hypermethylated in cancer patients. On the other hand, a probability close to zero indicates that γk was mostly negative, implying hypomethylation for the genes in the kth pathway.To infer the pathway effect γk > 0) among cancer patients for each pathway under different models. The top two pathways, the pathways in cancer and the cell cycle pathway, correspond to the two most extreme probabilities. For the pathways in cancer, the probability was close to zero ), indicating a general pattern of hypomethylation in this pathway. In other words, cancer patients were the least hypermethylated in this group. This pathway consists of several signaling networks  related to cancer prognosis, including those of tissue invasion and metastasis, sustained angiogenesis, evading apoptosis, proliferation, failed repair of genes, insensitivity to anti-growth signals, genomic damage, resistance to chemotherapy, and blocking of differentiation. Several genes within this pathway have been reported to show association with ovarian cancer, including Cyclin D1 (CCND1), CDK2, ERB2, and EGFR. The cyclins function as regulators of Cyclin-dependent kinases (CDKs) and play an important role in the cell cycle G1/S transition. The four aforementioned genes in this pathway have all been suggested as biomarkers for various cancers.ATM, CCND1, CDK2, CCNB2, and CHEK2 that are known to be involved in carcinogenesis.The probability of the cell cycle pathway was close to 1 , implyin30An alternative way to compare these pathways is to rank the distances between the probabilities in To evaluate the strength of pathway effect by assigning it a probability, The boxplots in λj. This quantity summarizes the influence from both CGI status and the pathway. If λj locates far away enough from zero that either one of the posterior probabilities Pr(λj > 0) or Pr(λj < 0) is large, say greater than 0.975, then this probe is considered a methylation variable probe. Consequently, its corresponding gene is defined as a differentially methylated gene (DMG). In Bayesian statistical inference, the magnitude of the probability ; while none among the 14 DMGs in the cell cycle pathway passed the test. Next, we searched PubMed with the query string “human methylation ovarian cancer” and with “human ovarian cancer” and tallied, for each gene that appeared in more than one study, the total number of studies in which a given gene had been reported to be associated with ovarian cancer as of 10 April 2014. Among the 61 DMGs, 32 had been previously reported to associate with ovarian cancer; while 9 among the 14 DMGs in the cell cycle pathway had been previously reported. Examples are CDKN2A and PTEN, known tumor suppressors in multiple cancers; EGFR and KDR, known cancer genes; and RUNX1 and STAT3, recently discovered to affect epithelial cancers353637CCND1, BCL2, ERBB2, and RARB genes containing hyper- and hypo-methylation probes, and FRAP1, RUNX1, STAT3, ATM, CCNB2, CHEK2, and COL6A1 genes containing probes that are more hypermethylated in cases than in controls. Under model (b2), 61 DMGs in the pathways in cancer and 14 DMGs in the cell cycle pathway were identified. Among the 61 DMGs identified in the pathways in cancer under model (b2), only 7 have passed the single-marker paired t-test with Bonferroni correction  terms for further investigation of their molecular function.two-stage inference. Most current methods use online bioinformatics tools and consult databases for annotations. For the purpose of comparison, we considered two procedures. The first applied paired t-tests with Bonferroni correction and then used DAVID for pathway analysis. No pathway was found statistically significant, unless a less stringent significance level of 0.10 was used: pathway of vascular smooth muscle contraction (p = 0.067), pathways in cancer (p = 0.073), and pathway in prostate cancer (p = 0.084). Except for the pathways in cancer, which was identified with our proposed model, these pathways are not associated with ovarian cancer, at least not according to the current literature. In the second comparison, we considered the model with the null pathway effect (a), selected the genes with large probability of hypermethylation or hypomethylation, and then applied DAVID for pathway analyses. A total of 116 genes passed the criterion and two pathways were identified as significant, including the pathways in cancer (p < 0.001), and the p53 signaling pathway (p = 0.025) based on the modified Fisher exact tests in the DAVID annotation system. Although this procedure did not pick up the cell cycle pathway as our proposed model did, the p53 signaling pathway, which it did identify, can be activated by stress signals, resulting in three major outputs: cell cycle arrest, cellular senescence or apoptosis.Traditional approaches usually perform single-marker tests in the first stage, and next conduct a hypergeometric test for pathway analysis using those gene symbols that have passed the test performed in the first stage. In the second stage, each pathway is tested separately, and no intra-pathway information is considered. We call this When applying the proposed analysis, other functional forms of pathway topology can be considered. For instance, correlations among genes can replace the number of connecting genes to describe the interaction. Pathways can be extended to networks so that the crosstalk between pathways can be examined, including a network of networks or even the whole interactomeθij denotes the observed difference in DNA methylation levels at the j th probe (Pj) between the ith case-control pair recruited. Each θij follows a normal distribution with mean μij, where μij depends on the probe-specific parameter λj. This λj can be decomposed into two parts, one (βj) for the effect of CGI status and the other (Φ) for the pathway effect. Those probes located in CGI share a common effect from the distribution In our proposed model for the matched case-control study, each γ; Pj, Λ), Pj is the probe and Λ contains all pathways {Λ1, Λ2, …, ΛK} of interest with γ =  as the corresponding parameters. To simplify the notation, we assume that the joint-effect Φ can be partitioned into fK (Pj), where k = 1, …, K. That is, For the function of the pathway effect Φ(fK (Pj) can be defined according to various scenarios. If each pathway has no effect on methylation, then Φ = 0. This is the null effect model, denoted as model (a). For the case where all genes share the same pathway effect, we consider fK (Pj) = Ik(Pj) × γk, and called the CGI-independent constant pathway effect model (b1). However, inside the same pathway, if the effect differs because of CGI status, then the pathway effect would be decomposed into two terms, Pj is in CGI and Each Ejk may imply that this gene is a hub gene and is influential in maintaining the integrity and normal function of the pathwaydegree or connectivity, which can influence the performance of the network. Thus we consider fK (Pj) = Ejk × γk, and call it the CGI-independent degree effect model (c1). When such an effect is modified by CGI status, then it is assumed Pj is in CGI and It is possible that the role of a gene depends on its position in a given pathwayγk measures the strength of association between the kth pathway and the disease phenotype. A large value of its posterior probability P(γk > 0|θ = (θij)) suggests that genes in the kth pathway tend to be more hypermethylated in cancer patients (cases) than in healthy subjects (controls); while a small value implies more hypomethylation in cases than in controls. Similarly, large values of the posterior probabilities All the statistical inference was made based on the posterior probability of the parameter of interest, where the probability is evaluated based on samples derived from the Markov chain Monte Carlo method (MCMC). In the above modeling, Gj and the phenotype, we examined the association between the j th probe and the disease by evaluating the conditional probability P(λj > 0|θ = (θij)). A probability close to one suggests greater hypermethylation among the cases; a value closer to zero suggests hypomethylation.To evaluate the degree of association between the gene How to cite this article: Chang, C.-W. et al. Gene-set Analysis with CGI Information for Differential DNA Methylation Profiling. Sci. Rep.6, 24666; doi: 10.1038/srep24666 (2016).


PathVisio is a commonly used pathway editor, visualization and analysis software. Biological pathways have been used by biologists for many years to describe the detailed steps in biological processes. Those powerful, visual representations help researchers to better understand, share and discuss knowledge. Since the first publication of PathVisio in 2008, the original paper was cited more than 170 times and PathVisio was used in many different biological studies. As an online editor PathVisio is also integrated in the community curated pathway database WikiPathways.Here we present the third version of PathVisio with the newest additions and improvements of the application. The core features of PathVisio are pathway drawing, advanced data visualization and pathway statistics. Additionally, PathVisio 3 introduces a new powerful extension systems that allows other developers to contribute additional functionality in form of plugins without changing the core application.http://www.pathvisio.org and in 2014 PathVisio 3 has been downloaded over 5,500 times. There are already more than 15 plugins available in the central plugin repository. PathVisio is a freely available, open-source tool published under the Apache 2.0 license (http://www.apache.org/licenses/LICENSE-2.0). It is implemented in Java and thus runs on all major operating systems. The code repository is available at http://svn.bigcat.unimaas.nl/pathvisio. The support mailing list for users is available on https://groups.google.com/forum/#!forum/wikipathways-discuss and for developers on https://groups.google.com/forum/#!forum/wikipathways-devel.PathVisio can be downloaded from  PLOS Computational Biology software article.This is a A picture says more than a thousand words. For many years biologists have been drawing pathway diagrams to gain a better understanding of the underlying biology. These diagrams are found everywhere: in textbooks, research articles, posters, lab journals or presentations and they have proven themselves as powerful tools to organize, share and discuss knowledge. Pathway diagrams have also become immensely useful for computational analysis and interpretation of large-scale experimental data when properly modelled. Complex diseases like cancer or heart failure are known to be caused by malfunctioning pathways instead of individual genes, so the study and collection of biological pathways is crucial to get insights into complicated disease mechanisms. Nowadays, computers allow researchers to use tools to draw pathway diagrams that are much more than just pictures; they contain annotations, literature references and comments for each element and interaction in a pathway. These enriched pathway diagrams open the possibilities to perform advanced pathway analysis and data visualization to get a more comprehensive understanding of experimental data.In 2008, we presented the first version of our pathway visualization and analysis tool PathVisio . Since tPathVisio has undergone active development and grown beyond a simple tool into a comprehensive and extendable pathway analysis toolbox. Besides its standalone graphical desktop version, PathVisio is often used as a library to read, write, store, convert and model pathway information. It is also used in different websites and workflows to act as a pathway editor and visualization tool. For example, a light-weight applet version of PathVisio is integrated in the community curated pathway database WikiPathways  and ProfIn previous versions PathVisio provided a simple but limited interface for extensions through plugins. A plugin is a small software component that adds a specific feature to an existing application. In the case of PathVisio, a plugin could provide for example a new statistical method, a new drawing standard or additional information about elements in the pathway. The usage of available plugins enables users to refine the pathway analysis workflow in PathVisio and build an application with all the necessary modules relevant for their research.Here we introduce the third version of the pathway visualization and analysis tool PathVisio. The aim is to present the newest additions and improvements of the application, especially the new plugin extension system, as well as the plugin repository and the integrated plugin manager. PathVisio is a freely available, open-source tool allowing independent developers to contribute plugins to provide new functionality. PathVisio is implemented in Java and thus runs on all major operating systems. The focus of this new version of PathVisio lies on modularity, extensibility and improved usability.In the last six years, PathVisio has been substantially extended and the core application was refactored using the OSGi framework (Open Service Gateway initiative) to achieve a better, modular system that can be easily extended with so called plugins . OSGi alFirst, we will discuss the new modular structure of PathVisio 3, then the plugin repository will be introduced, and last the usability and advantages of the new plugin manager will be shown.PathVisio 3 consists of eight OSGi modules that build the core application, each being responsible for one crucial part of the application. As illustrated in core module of PathVisio 3 contains the non-user interface backend, including the data model, import and export functionality and general settings and preferences. This module can also be used as a library by other software tools for reading, editing and writing pathway files in PathVisio’s native GPML  format. The gui  module implements the basic user interface which is shared between the standalone and the applet version of PathVisio. The applet version is integrated in WikiPathways as an online pathway editor. The more advanced, full-powered graphical user interface for the standalone application is provided by the desktop module. It is also the central connecting point for plugins. The plugin manager module handles the connection to the plugin repository as well as installing and uninstalling plugins. The gex module contributes the functionality for importing experimental data together with the data module which defines the interfaces for storing and handling experimental data. The visualization module then provides a simple but flexible way to visualize the experimental data on the data nodes in the pathways. To identify significantly altered pathways in an experimental dataset, the statistics module contributes a standard over-representation analysis algorithm based on a hypergeometric test [The ric test .The new PathVisio plugin repository consists of two separate parts, (i) the repository itself which stores all necessary plugin files as well as their dependencies and (ii) the PathVisio plugin database and front-end.http://repository.pathvisio.org. It contains all plugin files and third-party dependencies. The RepoIndex library (https://github.com/osgi/bindex) builds a complete dependency structure of the repository and writes it in an XML file named repository.xml.The PathVisio repository is located at http://wordpress.org/) to take advantage of some of the built in functionalities of WordPress, like capabilities to tag, browse, search, comment and evaluate plugins.The PathVisio plugin database is an independent mySQL database containing location information and metadata, e.g. description, authors and release notes, about each plugin. The database is integrated into the WordPress framework  pathway drawing, (2) data visualization and (3) pathway statistics. The integrated identifier mapping framework BridgeDb  allows pThe data visualization and pathway statistics modules have been first introduced in PathVisio 2 and further improved and extended in PathVisio 3.Pathway drawing. Biological pathway diagrams represent the sequence of events in biological processes. They often contain different biological entities, like genes, proteins or metabolites, and interactions between them, like conversion, stimulation or inhibition. As illustrated in Data visualization. The visualization of experimental and other data is a crucial aspect in the analysis and investigation of biological pathways. PathVisio allows users to import their experimental data and visualize it on the data nodes and interactions in the pathway. The integrated identifier mapping framework takes care of mapping the data points to the intended pathway elements, therefore the user is not restricted to a specific identifier system. In integrative studies, transcriptomics, proteomics and metabolomics data can be visualized simultaneously to provide a more complete view of the underlying biology [ biology .As detailed in The visualization options in PathVisio 3 can be used to visualize time-series data (one column for each time point) , tissue Pathway statistics. The goal of pathway statistics is to find pathways that are altered in an experimental dataset. The basic pathway statistics implementation in PathVisio is an over-representation analysis based on the statistical methods used in the MAPPFinder tool [der tool .First, the user defines a criterion to select the differentially expressed genes in the dataset. In The statistics module calculates the total number of genes measured in the dataset (N) and the number of genes meeting the criterion (R). All genes in N and R are present in at least one pathways. Genes that are not found in any pathway are ignored in the analysis. The Z-Score is calculated for each pathway in the collection. Therefore the statistics module counts the total number of elements in the pathway , the number of genes in the pathway measured in the experiment (measured → n) and the number of genes in the pathway meeting the criterion (positive → r) see .A commonly used score for over-representation analysis is the Z-Score. The Z-Score is the score calculated by a standard statistical test under the hypergeometric distribution. It indicates if a particular pathway shows a difference in the ratio of genes meeting the criterion as compared to the complete dataset. It is calculated by subtracting the expected number of genes meeting the criterion from the observed number divided by the standard deviation of the observed number of genes:The pathways are ranked based on their Z-Score. A positive Z-Score indicates a pathway with more genes meeting the criterion than expected based on the complete dataset. A negative Z-Score indicates that less genes meet the criterion than expected. In the example in PathVisio 3 provides a powerful and flexible way for plugins to integrate new functionality into the application. The variety of plugins shows that PathVisio can be extended in a lot of different ways and although initially PathVisio started as a pathway editor, it grew into an advanced and extendable pathway visualization and analysis toolbox.The implementation of different pathway related standards is crucial to fulfil the requirements of a state-of-the-art pathway editor. BioPAX is a standard language to exchange biological pathway data . The BioPathway databases still only cover 48% of all human protein-coding genes see . TherefoAlso the integration of additional data about the elements in the pathway is useful when creating and curating biological pathways. The BiomartConnect plugin queries the Ensembl database for additional information about gene products, like chromosomal position, %GC content or known variants. The MetInfo plugin provides more data about the metabolites in a pathway, like InChI key or predicted MS and NMR peaks. Plugins connecting to UniProt, Protein Data Bank (PDB) and interaction databases are under development.http://projects.bigcat.unimaas.nl/pathvisiorpc/) to be able to call PathVisio from other programming languages through an XML-RPC server. It enables users to programmatically draw pathways, visualize data on pathways and perform pathway statistics. This is especially convenient and time-saving when studying multiple datasets or datasets with many different comparisons.To enable the integration of PathVisio in an automated workflow, we developed PathVisioRPC  and editor (under development) for biological pathways. The viewer converts GPML source data into JSON for easier handling in JavaScript and then renders it as an SVG image in the users browser. The result is an interactive and searchable image with external reference linkouts via BridgeDb . In futuPathVisio 3 is a freely available, open source pathway editor, visualization and analysis toolbox implemented in Java. It runs on all major operating systems as a Java webstart program or as a binary installation.http://www.pathvisio.org/downloads/Download: http://www.pathvisio.orgDocumentation and tutorials: http://developers.pathvisio.orgInstructions for core and plugin developers: http://www.pathvisio.org/plugins/plugins-repo/Plugin repository: http://svn.bigcat.unimaas.nl/pathvisio/, see Source code: http://www.bridgedb.org)Integrated identifier mapping framework: BridgeDb (https://github.com/wikipathways/pathvisiojsPathvisiojs code repository: Future development will focus on (1) more advanced pathway analysis methods, (2) improved data integration and visualization and (3) automated update mechanisms.(1) Advanced pathway analysis methods. The default pathway analysis method in PathVisio 3 is a simple over-representation analysis. Users can also use the Gene Set Enrichment Analysis (GSEA) plugin which implements a functional class scoring method which does not require a specific threshold for splitting up significant and nonsignificant measurements. This method uses all the molecular measurements and their expression levels. The next step for PathVisio is the implementation of an topology-based pathway analysis method. While over-representation analysis and functional class scoring only consider the number of genes in the pathways, topology-based methods also look at the interactions between the elements in the pathways [pathways .(2) Improved data integration and visualization. PathVisio 3 supports the visualization of transcriptomics, proteomics and metabolomics data on the elements in the pathways. Recently a plugin has been developed to allow visualization of fluxomics data on the interactions in the pathways. Integration of other experimental data like genetic variation, methylation or phosphorylation states is needed to be able to study biology in all its complexity. For most of these additional data types new advanced visualization methods are needed.(3) Automated update mechanisms. In the next major release of PathVisio, we are planning an automated update mechanism for the main application and the installed plugins. The application can be upgraded as soon as a new release is available. We will provide installers for all major operating systems that will facilitate the installation of new PathVisio versions.S1 Code(RAR)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Tablehttp://bridgedb.org/data/gene_database/). We used the Reactome, KEGG and WikiPathways webservices to retrieve the gene lists and map them all to Ensembl identifiers. We only included genes that can also be mapped to UniProt to focus on protein coding genes. This gives a basic indication of the gene coverage in the pathway databases. The scripts for the calculations can be downloaded from https://github.com/mkutmon/wp-scripts/blob/master/PathwayResourceGeneCoverage/src/org/wikipathways/Stats.javaAll numbers were calculated with the BridgeDb mapping database build on 1 July 2013 (Hs_Derby_20130701.bridge → (PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.


Despite the recent identification of several prognostic gene signatures, the lack of common genes among experimental cohorts has posed a considerable challenge in uncovering the molecular basis underlying hepatocellular carcinoma (HCC) recurrence for application in clinical purposes. To overcome the limitations of individual gene-based analysis, we applied a pathway-based approach for analysis of HCC recurrence.p < 0.01), from our microarray dataset composed of 142 HBV-positive HCCs. In relation to the public HBV- and public hepatitis C virus (HCV)-positive HCC datasets, we detected 46 (71.9%) and 18 (28.1%) common recurrence-associated pathways, respectively. However, overlap of recurrence-associated genes between datasets was rare, further supporting the utility of the pathway-based approach for recurrence analysis between different HCC datasets. Non-supervised clustering of the 64 recurrence-associated pathways facilitated the classification of HCC patients into high- and low-risk subgroups, based on risk of recurrence (p < 0.0001). The pathways identified were additionally successfully applied to discriminate subgroups depending on recurrence risk within the public HCC datasets. Through multivariate analysis, these recurrence-associated pathways were identified as an independent prognostic factor (p < 0.0001) along with tumor number, tumor size and Edmondson’s grade. Moreover, the pathway-based approach had a clinical advantage in terms of discriminating the high-risk subgroup (N = 12) among patients (N = 26) with small HCC (<3 cm).By implementing a permutation-based semi-supervised principal component analysis algorithm using the optimal principal component, we selected sixty-four pathways associated with hepatitis B virus (HBV)-positive HCC recurrence  contains supplementary material, which is available to authorized users. Various gene signatures related to the survival and recurrence of hepatocellular carcinoma (HCC) have been identified using microarray analysis, and proposed to supplement clinicopathological prognostic factors for prediction of patient outcomes -4. In faOne way to overcome the limitations of the gene-based approach is the use of functionally related predefined gene sets, such as pathways, instead of individual genes ,11. PathAmong the diverse factors implicated in HCC development, hepatitis B (HBV) and hepatitis C viruses (HCV) are the major etiological risk factors. Although HCCs infected chronically with HBV and HCV are not distinguishable from histological and clinical evaluations , studiesIn the present study, we implemented the pathway-based supervised principal component analysis (PCA) with random permutations to identify recurrence-associated pathways of HBV-positive HCC. Comparison of our HBV-positive recurrence-associated pathways with those from two public datasets of HBV and HCV positive-HCCs revealed significant overlap of pathways, despite limited common genes between the datasets. The set of recurrence-associated pathways identified in this study effectively facilitated the stratification of HBV-positive HCC patients according to risk of recurrence. Our pathway-based approach should therefore provide clinically useful molecular insights into the mechanisms underlying HCC recurrence.To determine the biological pathways associated with HCC prognosis, we established a genome-wide gene expression dataset via cDNA microarray of 142 HCC cases positive for HBV. A schematic diagram of the overall procedure for selection of pathways associated with recurrence is presented in Figure p-value < 0.01) , respectively. Upon further differentiation of these patients based on 2 years after surgery, defined as a cut-off for early recurrence [Initially, using the microarray dataset and long-term follow-up clinical data, we assessed the implications of gene expression on HCC recurrence. In our cohort, median overall and recurrence free-survival times were 73.0 and 26.5 months, respectively. As an initial attempt, we extracted a subset of 209 genes correlated with HCC recurrence . Comparison of the recurrence-related genes between datasets  disclosed a limited number of common genes   and HCV-p-value < 0.0001). However, the incidence of recurrence in our HBV-HCC dataset showed a biphasic pattern  for our HBV-HCC, public HBV-HCC and public HCV-HCC datasets, respectively  were associated with early recurrence within two years, with area under curve (AUC) > 0.6. For example, in Kaplan-Meier plots and receiver operating characteristic (ROC) curves of the axon guidance pathway, one significant pathway common to all datasets clearly distinguished patients according to recurrence risk, with AUC > 0.65  .Since our approach using optimal principal components was dependent on data structure, such as microarray platform and experimental conditions, we could not directly apply coefficients obtained from our microarray dataset to the other independent microarray datasets. Therefore, only pathway information from our HBV-HCC dataset was used to validate the effectiveness of pathway analysis in the public microarray datasets. Using our 64 recurrence-associated pathways, sample scores from the first, not optimal, principal component were measured and clustered in public HBV Figure E and pubp-value < 0.05) associated with the high-risk subgroup classified according to pathway analysis included high levels of aspartate aminotransferase (≥40 IU/L) and alanine aminotransferase (≥40 IU/L), and presence of vascular invasion and lobular activity. Absence of capsule and narrow resection margin (<2 cm) were also marginally associated with the high-risk subgroup . Univariate analysis showed that the 64 pathways , together with clinicopathological parameters, including tumor stage , tumor number , tumor size , vascular invasion , lobular activity , Child-Pugh score , Edmonson Steiner’s grade  and capsule formation , are significantly or marginally associated with HCC recurrence in our present cohort. Multivariate analysis further led to the definition of these pathways  as an independent prognostic factor for recurrence together with clinical variables, including tumor number , tumor size  and Edmondson Steiner’s grade , in our HBV-positive HCC dataset. In particular, focusing on tumor size among the variables  was recently shown to partly overcome the heterogeneity of microarray analysis ,13,14. TData from the present study confirmed that the pathway-based approach is more beneficial than the gene-based approach in identifying common biological features in HCC recurrence. For this purpose, we developed a pathway-based PCA algorithm. Previously, PCA using a subset of genes was reported to accurately predict clinical outcomes of patients . Here, wOur cohort exhibited a biphasic pattern in which the majority of recurrence occurred within the first two years, similar to the public HBV-HCC cohort . ConsideAlthough the 16 pathways were common in both HBV- and HCV-HCC datasets, their clinical advantage in patient classification was not clear. Since these common pathways were not optimal, we obtained reduced performance in classification of patients. In addition to its clinical value, the overlap of significant pathways between HBV- and HCV-positive datasets further suggests that recurrence of HBV- and HCV-positive HCCs is commonly affected by dysfunction of common biological pathways . Notably, these pathways have already been reported in association with development or prognosis in diverse types of human cancers, including HCC. For example, hemostasis and platelet-related pathways are critical in cancer development ,22. SignIn contrast to the common pathways discussed above, tight junction, focal adhesion and integrin pathways were more significantly associated with HBV-positive HCC. These membrane mobility and intercellular interaction pathways are involved in HCC invasion, metastasis and angiogenesis -34. IntePreviously, we reported functional differences and similarities in pathways implicated in the development of HCV- and/or HBV-positive HCC ,37. By cet al. [et al. [We further validated our pathway-based classification of HCC patients with those published employing prognostic genes for HCC. Survival-associated genes identified by Lee et al.  and Hosh [et al. , as wellOur pathway-based approach using optimal PCA was more advantageous than the gene-based approach in several aspects, and revealed common prognostic pathways between HCC datasets of different patient cohorts, despite the rarity of common recurrence-associated genes. Identification of these shared pathways allowed the evaluation of functional similarities and differences between HBV- and HCV-positive HCCs. Further studies are essential to validate the specific functions of these pathways in addition with HCC recurrence.The HBV-positive HCC dataset (HBV-HCC) was composed of 142 HCC tissues from patients subjected to surgical resection at Korea Cancer Center Hospital and Seoul National University Hospital at Korea from March 1992 to July 2004. The tissue specimens were immediately collected after resection, and stored in liquid nitrogen until RNA extraction for microarray experiment. The present retrospective study was approved by the Institutional Review Boards of Seoul National University Hospital and Korea Cancer Center Hospital. Written informed consent was obtained from the patients of Seoul National University Hospital or waived by the Institutional Review Board of Korea Cancer Center Hospital. Prior to analysis, patient information was anonymized and de-identified. Clinicopathologic data were obtained retrospectively, including platelet, bilirubin, alanine transaminase, aspartate aminotransferase, α-fetoprotein, Child-Pugh score, tumor size, tumor number, tumor grade, vein invasion, lobular activity, capsule formation, resection margin, and cirrhosis.Total RNA from HCC tissues was extracted using the Qiagen RNeasy Mini Kit , in accordance with the manufacturer’s instructions. The quantity and quality of purified RNA were measured using an Agilent 2100 Bioanalyzer , and only samples with RNA integrity number (RIN) greater than 7.0 were included in the microarray analysis ,39. TotaTwo publicly available microarray datasets were utilized for comparison. The first dataset (Public HBV-HCC), archived in the Gene Expression Omnibus with the accession number GSE14520, is composed of 236 HBV-positive HCC specimens . After ahttp://linus.nci.nih.gov/BRB-ArrayTools.html) in which expression values are correlated with recurrence time using the Cox Proportional Hazards Model. The log-rank p-values for individual genes were tested one at a time for the hypothesis that recurrence time is independent of the expression level for that gene. The p-values were applied to construct gene lists using multivariate permutation tests for controlling the number or proportion of false discoveries with 10,000 iterations.Genes associated with recurrence were selected using BRB ArrayTools  and test sets (40%). PCA analysis was performed in the training set with genes belonging to each pathway. First, in each pathway, we selected a single optimal principal component most significantly associated with recurrence in Cox proportional hazards regression. The optimal principal component was subsequently applied to the test set to obtain sample scores. In weighted PCA, the principal components responsible for over 80% variance in the training set were selected and linearly added to generate the sample score using regression coefficients of individual components as weight. This procedure was repeated 1,000 times to facilitate maximum extraction of information from all samples. Next, the median value of statistics over the entire permutation process was computed on each pathway. The statistical significance of the test validation outcomes was computed by comparing with results from randomly selected pathways with 1,000 iteration times. All procedures were performed in R (v2.14.0). The R source code for the program is available on request.p-values on Cox proportional hazards regression. We applied permutation-based  pathway analysis, as described above, starting from genes with the lowest univariate p-value and gradually increasing the number of genes, and measured the average number of pathways below the log-rank p-value of 0.01 in the test set. The threshold was taken as the p-value with the maximum number of significant pathways in the test set.Genes were ranked and ordered according to log-rank http://www.broadinstitute.org/gsea) [http://www.biocarta.com/genes/allPathways.asp) and 430 from Reactome [Manually curated pathways were obtained from the Molecular Signatures Database   includinrg/gsea) , 217 froFor pathway-based clustering of samples, we constructed a matrix composed of optimal principal component scores for each sample and pathway. The matrix was hierarchically clustered based on similarities of the principal components to classify the samples . For robp-values. KS statistics allow calculation of the maximum difference between i/N and ith smallest p-value. The significance of LS and KS was evaluated by computing the distribution of these statistics in a random sampling of 1,000 times [For a pathway composed of N genes, LS statistics is used to measure the average value of negative natural logarithms of single gene 00 times . ROC-AUChttp://www.ncbi.nlm.nih.gov/geo) under the ID number GSE55039.Our HBV-HCC microarray dataset is available online at the Gene Expression Omnibus (


High-throughput methods are generating biological data on a vast scale. In many instances, genomic, transcriptomic, and proteomic data must be interpreted in the context of signaling and metabolic pathways to yield testable hypotheses. Since humans can interpret visual information rapidly, a means for interactive visual exploration that lets biologists interpret such data in a comprehensive and exploratory manner would be invaluable. However, humans have limited memory capacity. Current visualization tools have limited viewing and manipulation capabilities to address complex data analysis problems, and visual exploratory tools are needed to reduce the high mental workload imposed on biologists.We present PathRings, a new interactive web-based, scalable biological pathway visualization tool for biologists to explore and interpret biological pathways. PathRings integrates metabolic and signaling pathways from Reactome in a single compound graph visualization, and uses color to highlight genes and pathways affected by input data. Pathways are available for multiple species and analysis of user-defined species or input is also possible. PathRings permits an overview of the impact of gene expression data on all pathways to facilitate visual pattern finding. Detailed pathways information can be opened in new visualizations while maintaining the overview, that form a visual exploration provenance. A dynamic multi-view bubbles interface is designed to support biologists’ analytical tasks by letting users construct incremental views that further reflect biologists’ analytical process. This approach decomposes complex tasks into simpler ones and automates multi-view management.http://raven.anr.udel.edu/~sunliang/PathRings and the source code is hosted on Github: https://github.com/ivcl/PathRings.PathRings has been designed to accommodate interactive visual analysis of experimental data in the context of pathways defined by Reactome. Our new approach to interface design can effectively support comparative tasks over substantially larger collection than existing tools. The dynamic interaction among multi-view dataset visualization improves the data exploration. PathRings is available free at The online version of this article (doi:10.1186/s12859-015-0585-1) contains supplementary material, which is available to authorized users. Biology has entered an era when our ability to collect data has outstripped our ability to turn that data into knowledge. In particular, high-throughput sequencing is providing enormous amounts of information about gene-expression patterns in a large number of species. In many cases, the experimental objective is to compare two or more distinct biological states, such as disease and control, in order to understand the ramifications of changes in gene expression. Typically these high-throughput data are interpreted in the context of signaling and metabolic pathways, and several resources are available that warehouse and provide visualization tools for pathway analysis e.g., -3). Visu. Visu3])Some existing tools either list overlaps between datasets, such as Lists2Networks  and KerfOther tools provide an overview for pathway relationships by overlaying information onto pathways, e.g., iPath gives an overview of regulatory pathways . HoweverMost existing tools are restricted to presenting visualization results in a single view at a time, limiting data comparison  and forcBubbles views: applying the ideas of VisBubbles [Virtual working space: PathRings applies the idea of “virtual screen space extension” [Sunburst compound graph visualization: PathRings displays a hierarchical view of all human pathways from Reactome [Here we present PathRings, a web-based framework for interactive exploration of biological pathway networks Figure . Its feasBubbles , PathRintension”  by proviReactome  in a sinPathRings attempts to solve several challenges in pathway visualization that vary depending upon users’ exploration goals. At one level, comparing pathways between species allows insight into evolution. Such comparison requires identification of orthologous sequences between a reference and a target genome. PathRings uses Human Reactome as the reference to predict pathways in other species. Interspecies comparisons are then displayed that depict pathway differences between the two species. A second usage scenario is to evaluate gene or protein expression data from a single species in the context pathways. One complexity that current visualizations rarely handle is that individual gene products can affect multiple pathways. Changes in the expression of such cross-talking gene products may affect multiple pathways and have significantly more impact on biology than gene products that function in a single pathway. In addition, few current visualization schemes indicate rate-limiting gene products in the context of pathways. PathRings addresses these issues by identifying both cross-talking and rate-limiting gene products in the context of pathways.PathRings is written in JavaScript Language and HTML5. The main bubble-based interface framework was developed in HTML5 two-dimensional (2D) Canvas API, while the Sunburst visualization is implemented in D3 . It usesWe have extended the Reactome human pathways  to suppoPathRings’ sunburst visualization can depict the impact of the expression data on all Reactome pathways. The user can select interesting pathways for further analysis by clicking a certain arc of the sunburst to create a new sunburst bubble visualization to examine sub-pathways or to create a gene table bubble visualization listing all the affected genes Figure . The useComparison of multiple pathways can be made by grouping concurrent views and editing one view will affect the other view. No analysis will need to be deleted and moving the current canvas location on the panning bar to another view , while keeping the previous exploratory analysis in context Figure .PathRings supports pathway exploration for the four types of relationships between pathway members: hierarchical relationships, cross-talking relationships, orthologous relationships, and gene expression relationships.Reactome provides a hierarchical depiction of pathways by dividing large pathways into multiple sub-pathways in a tree structure, depicted in a Sunburst view Figure . SunbursB, where A is the digit  and B is the exponent (an integer). The integer exponent B uses a wider bar and the real digit A is shown using a narrow bar. Both A and B are on a linear scale from 0 to 10. For example, an expression level of 99 will be re-written as 9.9 × 101, and the wider bar showing 1 whilst the narrow bar showing 9.9. In this way, both large and small expression values can be precisely perceived.Here the visualized gene expression level is represented using the order of magnitude markers approach (OOMM) to fit the large-dynamic range expression data . An exprIn biological pathways, biological crosstalk refers to instances in which one or more components of one pathway affects another; and such crosstalk is often described using cross-talking gene products, as an interconnection relationship between two or more pathways . PathRinComplete: All genes in a pathway can be identified in a target species based on orthology to the reference.Partial: Not all genes in a pathway are present in the target species.Empty: No orthology is present in the target species.Pathway prediction based on orthology between a reference genome (human for Reactome) and a target (such as chicken) will yield three types of orthologous pathway relationships between the reference and target species: complete, partial, and empty defined as the following Figure .Figure 4In PathRings, all three relationships of a pathway are encoded by color overlaying the arc of the sunburst . The default view of the sunburst shows the orthologous relationships between human and chicken. Other species of mouse, alligator, and turtle can be loaded for further analysis.We provide an overview of orthologous genes (shared proteins) of a pathway between two species by embedding bar charts on each arc of sunburst. The height of the bar corresponds to the number of orthologous gene, so that the user can easily find the interesting pathways. Orthologous genes can be queried and listed in a table that includes gene symbol, the number of gene products in the pathway, the number of cross-talking pathways influenced by this gene and the gene product’s rate-limiting status all shown in bar charts in different columns Figure . The usePathRings supports the display of gene expression data from experiments or in controlled conditions Figure . Gene exOur current implementation only supports a few species. Our long-term vision is to integrate this exploratory analysis to iPlant infrastructure  thereby Automated methods play a central role in biological pathway analysis. For example, we could potentially use Gene Set Enrichment Analysis  for intePathRings lets biologists explore pathway datasets in a dynamic fashion that pathway hierarchies, ortholog, crosstalk, and NCBI are integrated to answer biological questions. It provides an overview of all the pathways for analyzing experimental data and supports uploading experimental data. Novel visual interface design supports rapid visual retrieval and comparative exploration for efficient data inspection.Project Name: ABI Development: PathBubbles for Dynamic Visualization and Integration of Biological InformationProject home page:https://sites.google.com/a/umbc.edu/pathbubbles/Operating system(s): Platform independentProgramming language: HTML, Javascript, PHPOther requirements: Web browserLicense: BSD licenseAny restrictions to use by non-academics: no restriction