Biological pathways are descriptive diagrams of biological processes widely used for functional analysis of differentially expressed genes or proteins. Primary data analysis, such as quality control, normalisation, and statistical analysis, is often performed in scripting languages like R, Perl, and Python. Subsequent pathway analysis is usually performed using dedicated external applications. Workflows involving manual use of multiple environments are time consuming and error prone. Therefore, tools are needed that enable pathway analysis directly within the same scripting languages used for primary data analyses. Existing tools have limited capability in terms of available pathway content, pathway editing and visualisation options, and export file formats. Consequently, making the full-fledged pathway analysis tool PathVisio available from various scripting languages will benefit researchers.We developed PathVisioRPC, an XMLRPC interface for the pathway analysis software PathVisio. PathVisioRPC enables creating and editing biological pathways, visualising data on pathways, performing pathway statistics, and exporting results in several image formats in multiple programming environments.We demonstrate PathVisioRPC functionalities using examples in Python. Subsequently, we analyse a publicly available NCBI GEO gene expression dataset studying tumour bearing mice treated with cyclophosphamide in R. The R scripts demonstrate how calls to existing R packages for data processing and calls to PathVisioRPC can directly work together. To further support R users, we have created RPathVisio simplifying the use of PathVisioRPC in this environment. We have also created a pathway module for the microarray data analysis portal ArrayAnalysis.org that calls the PathVisioRPC interface to perform pathway analysis. This module allows users to use PathVisio functionality online without having to download and install the software and exemplifies how the PathVisioRPC interface can be used by data analysis pipelines for functional analysis of processed genomics data.http://projects.bigcat.unimaas.nl/pathvisiorpc.PathVisioRPC enables data visualisation and pathway analysis directly from within various analytical environments used for preliminary analyses. It supports the use of existing pathways from WikiPathways or pathways created using the RPC itself. It also enables automation of tasks performed using PathVisio, making it useful to PathVisio users performing repeated visualisation and analysis tasks. PathVisioRPC is freely available for academic and commercial use at The online version of this article (doi:10.1186/s12859-015-0708-8) contains supplementary material, which is available to authorized users. Biological pathways are descriptive diagrams used to depict complex cellular processes such as metabolism, gene regulation, and signal transduction. Disturbances in such processes can cause disease. Pathways thus can help understand the functions of individual genes and proteins in terms of the systems and processes that contribute to normal physiology and to disease. Pathway analysis integrates and visualises global high throughput measurements and is a widely applied method among researchers to gain functional understanding from biological quantifications, such as gene expression and (relative) metabolite and protein abundances .Pathways can be obtained from pathway databases. The Pathguide website , 3, at tPathVisio is an open source tool for pathway editing and analysis that has seen rapid adoption by the scientific community since its release in 2008, counting 171 citations. A selection of the citing papers demonstrates the visualisation possibilities –20. In aHowever, when researchers perform the same analysis using multiple sets of data and/or pathways using the graphical user interface of PathVisio they have to click the same set of buttons repeatedly for uploading the data, creating visualisations, performing pathway statistics, and exporting results, making the analyses error prone and time consuming on the user side. In this paper we describe the newly developed XMLRPC interface PathVisioRPC, which enables direct programmatic access to PathVisio from scripting languages. It enables automation of these steps by calling the defined functions, e.g. from inside a loop. An additional advantage is that a script written to automate PathVisio usage can be stored to enable later reference for validation and sharing purposes.RPC stands for Remote Procedure Call. An RPC is initiated by a client that sends a request to a local or remote server to execute a task with supplied parameters. The server sends a response back to the client. XMLRPC uses XML to encode its calls and uses HTTP as a transport mechanism . The PatThe PathVisioRPC interface Fig.  allows uPathVisioRPC is a client–server application that handles client requests to execute tasks after the server is launched. Running the PathVisioRPC.jar file launches the server on a port that is passed as an argument or on the default port 7777 of the local computer. The PathVisioRPC plugin for PathVisio adds a new “XMLRPC server setup” sub-menu item to the PathVisio “Plugins” menu. This is used to define the port for the server and to start or stop the server. A detailed API documentation along with code snippets from different languages for calling the implemented functions is available from the online documentation .The following three examples use PathVisioRPC in Python to illustrate some of the functionalities implemented in PathVisioRPC. The scripts and the data for the examples are provided in Additional file example1.py script loops over three files with gene expression data to create a pathway with the genes in each one of them and visualise the measurements. The script opens and reads each file, creates a new empty pathway, adds the genes in the file as nodes to the pathway, and visualises the gene expression data on the nodes [The first example demonstrates how the same functionalities can be applied to multiple files. The he nodes . In the example2.py script reads a file with a list of genes, creates a new empty pathway, and then adds the genes as nodes to the newly created pathway. Subsequently, the script visualises the data from the second file on the pathway. The gene expression data is also visualised on the Statin pathway [The second example demonstrates how files of different types can be combined/processed together. A pathway is created with a list of genes in a file and the data from a second file is displayed on the pathway created. The same data is then also visualised on a pathway from WikiPathways. The  pathway  from Wik pathway  to a dedThe third example shows how multiple data files can be processed with different settings based on a configuration file. The script reads a settings file that contains the names of data files, associated species (human or mouse), and numbers of the data columns to be visualised. For each data file name present, the script loads the file, uses the correct species identifier mapping database, and visualises the data in the desired column(s) using a gradient on a WikiPathways pathway (cholesterol biosynthesis) from the correct species.As an illustration of the use of PathVisioRPC functionality to analyse a real genomics dataset we performed data visualisation and pathway analysis with gene expression data from a study by Moschella et al. . They peMus musculus) pathways was downloaded from WikiPathways using the WikiPathways web service [The gene expression datasets , for each cell type: PBMCs, bone marrow cells, and splenocytes. The lists of statistical results for each of the nine comparisons were collated into a single file, which was used for further analysis  at each time point . The Z score for over-representation analysis is calculated as a score for each pathway in the pathway collection, by subtracting the expected number of genes in a pathway meeting the criterion from the observed number of genes and dividing by the standard deviation of the observed number of genes . The criP-value ≤ 0.05 represented by green and white otherwise) in each tissue for each time point. Genes in the pathways absent in the dataset were coloured light grey.The dataset was then visualised on the pathways. Log2 fold-changes of the genes were visualised using colour gradients  and significant genes were visualised using colour rules  enrichment analysis was performed using the topGO Bioconductor package  separateOverall, the Z score indicates whether the number of genes meeting the criterion is higher or lower than what is expected based on the complete dataset. A positive score indicates that in that pathway more genes are changed than expected; a negative score means fewer genes are changed than expected. Thus, it can be inferred that the highest-ranking pathways are potentially the most interesting ones for the given conditions and need to be evaluated further, since they were detected to contain an overabundance of differentially expressed genes. Subsequently, the involvement of the pathways in cellular functions has to be determined for a biological interpretation of the results.P-value for that time point . In PBMCs, the transcripts for Fos and Nqo1 are significantly upregulated for day 1 and/or day 2. Moreover, the transcript for Sp1 is down regulated for day 1 and day 2 , where server_address refers to the address at which the PathVisioRPC server has been launched. However, when using RPathVisio the call is simplified to createPathway(name = “Glycolysis”).Several other packages are available in R to access pathways as gene sets and perform pathway analyses. Notable among them are PathView , KEGGGraAs an example of workflow integration, we have developed a module for pathway analysis for ArrayAnalysis.org, a web-based platform for microarray data analysis. It has a modular setup with quality control and normalisation modules for several types of microarray platforms. The workflow has been extended with modules for statistical analysis and pathway analysis that accept data either from the built-in data normalisation modules or from user uploads. PathVisioRPC powers the pathway module of ArrayAnalysis.org. This workflow is available at the ArrayAnalysis.org website .The online pathway module Fig.  in ArrayIn the next form, the user can specify the colour gradients and/or colour rules to visualise the data on the pathways. To choose a colour gradient for a variable, the user selects the variable and then two or three colours and corresponding values to create a gradient. In order to choose a colour rule, the user enters a criterion and a colour to be used if the criterion is met. For performing pathway statistics, a criterion for calculating the Z score needs to be set. After setting the options, on clicking Run, the pathway module is launched. The form inputs are converted into R commands which in turn call the PathVisioRPC server for executing the tasks. After completion of the run, results display on screen.PathVisioRPC enables automated calls to PathVisio to support users in performing repeated analyses and building workflows. We have demonstrated the advantages of using PathVisioRPC with scripts written in Python and R, commonly used scripting languages for bioinformatic analysis. PathVisioRPC simplifies the task of creating pathways and visualising data using multiple files as demonstrated by our example use cases in Python. It also allows users to perform multiple pathway statistics comparisons as shown by the biological analysis in R that compares the transcriptomics profile of three different tissues in three different time-points. We have also shown that the functionality provided by the interface can be easily combined with existing analysis packages from Bioconductor, as existing R packages can be used for statistical analysis and GO analysis and then RPathVisio can be used for pathway building, pathway analysis, data visualisation on pathways, and export. The same approach could be followed in other programming languages, example code snippets are available in R, Perl, and Python from the project website . The patWriting scripts to create pathways, visualise data, perform pathway statistics comparisons, and export data visualised pathway images makes these tasks less error- prone and time- consuming for the user as compared to doing this manually. Using a script also allows the user to retain method provenance. This ensures reproducibility, reference at a later time, and sharing the method used with others. Therefore, PathVisioRPC simplifies workflows involving the visualisation of experimental data on multiple pathway sets from WikiPathways and allows repeated over-representation analyses in various programming languages and for different gene classification approaches such as GO. This allows researchers to integrate PathVisio as a visualisation and pathway analysis tool in workflows, facilitating automated downstream analysis of (multiple) datasets. These pathways can be exported with or without included data visualisation in various image formats for publication, uploaded to WikiPathways for community curation, or used in Cytoscape  for netwFurthermore, the pathway module of ArrayAnalysis.org illustrates how the data visualisation and over-representation analysis of PathVisio can be incorporated into an existing data analysis pipeline using PathVisioRPC. This online module can also be particularly useful for researchers who only need pathway analysis once for their current dataset, allowing them to use the core functionalities of PathVisio over the internet without having to download and install the software itself.Reactome pathways can be used in analysis already as they have been converted to GPML. Similarly other pathway sets (e.g. KEGG and BioPAX) can be used in analysis when these are converted into GPML. The functionality can also be easily extended by registering functionalities of other PathVisio plugins like the GSEA plugin  in the PProject name: PathVisioRPCProject home page:http://projects.bigcat.unimaas.nl/pathvisiorpcOperating system(s): Platform-independentProgramming language: JavaOther requirements: NoneLicense: Apache 2 LicenseAny restrictions to use by non-academics: None other than those defined by the license.


Recent experimental results on the effect of miRNA on the decay of its target mRNA have been analyzed against a previously hypothesized single molecule degradation pathway. According to that hypothesis, the silencing complex (miRISC) first interacts with its target mRNA and then recruits the protein complexes associated with NOT1 and PAN3 to trigger deadenylation (and subsequent degradation) of the target mRNA. Our analysis of the experimental decay patterns allowed us to refine the structure of the degradation pathways at the single molecule level. Surprisingly, we found that if the previously hypothesized network was correct, only about 7% of the target mRNA would be regulated by the miRNA mechanism, which is inconsistent with the available knowledge. Based on systematic data analysis, we propose the alternative hypothesis that NOT1 interacts with miRISC before binding to the target mRNA. Moreover, we show that when miRISC binds alone to the target mRNA, the mRNA is degraded more slowly, probably through a deadenylation-independent pathway. The new biochemical pathway proposed here both fits the data and paves the way for new experimental work to identify new interactions. In living cells, the level of protein expression is thoroughly regulated. Many crucial processes for this regulation occur at the post-transcriptional level. In this context, control mechanisms acting on messenger RNAs (mRNAs) play a pivotal role. A number of biochemical pathways converging on cytosolic mRNAs serve to enhance or repress gene expression. These pathways are known to operate by enhancement of translation ,2, repredecay patterns and degradation pathways is non-trivial [Despite extensive study, the topology and dynamics of miRNA-mediated mRNA degradation pathway are still unclear. One of the main challenges stems from the fact that intermediate states of the pathway are unknown or difficult to quantify; experimentally, it is only feasible to measure the decay patterns of the target mRNAs. Bridging the gap between observed -trivial , since tet al. [D. melanogaster S2 cells containing constructs for the miRNA miR-9b, its target mRNAs F-Luc-Nerfin mRNAs, and the factors NOT1 and PAN3, which are known to trigger mRNA deadenylation. In each experiment, a subset of NOT1, PAN3 and/or miR-9b were selectively knocked down, yielding cell lines expressing different combinations of those factors: a control line without the miR-9b, a cell line with miR-9b only, a cell line with NOT1+miR-9b (but not PAN3), a cell line with PAN3+miR-9b (but not NOT1), and finally a cell line with all three factors NOT1+PAN3+miR9-b. A graphical representation of the network of biochemical interactions hypothesized in [To clarify the interplay of the various factors in a degradation pathway involving miRNA, the protein complexes NOT1, and the protein complex PAN3, Braun et al.  performesized in  is presesized in ; we extrThe conclusion of this detailed experimental study is that NOT1 is a more relevant factor than PAN3 in destabilizing the mRNA . When onbefore recruiting the target mRNA and that there is a strong enhancement of mRNA recruitment when PAN3 is also present.An important conceptual consideration is that Figure degradation pathways . This one-state scenario is also not consistent with biological reality. Indeed, even the most stable cellular macromolecule is eventually degraded. In fact, there are many biochemical pathways devoted to mRNA degradation [Based on the hierarchical strategy above, we start with the "Control (-)" curve " curve of Figure At the next level of our hierarchical approach we consider the decay pattern that results when the miRNA is expressed but NOT1 and PAN3 are knocked-down , the target mRNA is stabilized, probably because it is protected from the action of an alternative miRNA-independent pathway. We postulate that the binding between miRISC and mRNA is irreversible and leads to deadenylation independent decay of the target message in agreement with recent experimental studies. However, this assumption is not obligatory. Indeed, one could have hypothesized that binding to miRISC is reversible, and that the presence of miRNA alone just slows down the action of the alternative pathway. With the present data it is not possible to distinguish between these two alternatives.before interacting with the mRNA. We assume that this discovery is not limited to the special miRNA-mRNA pair studied in [Finally, our analysis indicates that the miRISC complex and NOT1 interact with each other udied in  and is tudied in  that PANExperimentally, one should be able to detect the presence of miRISC+NOT1 complexes in the absence of target mRNA in order to verify our findings. Moreover, steady state relative amounts of mRNA in the different biochemical states can provide further validation data for our networks and additional information to unveil further details of miRNA-mediated mRNA degradation.The authors declare that they have no competing interests.Conceived the project: AV, CS, DC; Data collection: CS; Model development: AV, CS, DC; Wrote the manuscript: AV, CS, DC.This file includes several explicit calculations, the numerical values of the data, and a summary of the theory used in this paper. It includes also a table summarizing the values of the rates obtained upon fitting plus two additional fitting attempts with the originally proposed network to show that the fit does not perform very well.Click here for file


The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules.We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example.PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data. The analysis of genome-wide transcriptomics data has changed in the last decade from a gene-centric vision, which evaluated thousands of gene expression changes in parallel, to a systems biology orientation where coordination among gene activities is pivotal. In light of this, data is analyzed from the perspective that genes do not act as independent entities, but as groups of cooperating molecules that define the cellular state ,2. FunctEA methods implicitly work under two assumptions. On the one hand, they consider that all genes in a gene set or a pathway equally contribute to the activity of that pathway; hence, the pathway is activated when a "sufficient" number of gene members is activated. This consideration does not take into account the differential regulatory factors that modulate each gene's participation in the pathway, such as different translation rates, enzymatic and complex-association kinetics or the quite versatile regulatory capacity of genes. An example of this last type is the heme biosynthesis pathway. This pathway involves eight enzymatic steps to transform succynil-coA and glycine into heme, the first being the synthesis of aminolevunilic acid by ALAS (aminolevunilic acid synthase), which is the committed step of the heme synthesis pathway and is usually rate-limiting for the overall pathway . Hence, The second assumption of EA methods is that pathways are generally considered as isolated boxes, and the interactions between them are normally not explored. However, pathways should be understood as a formalization of our understanding of cell biology and hence their boundaries are arbitrary or, actually, non-existing . In factet al [et al [et al [There are some recent examples in the literature of methodologies that analyze pathway interactions to understand the cross-talk between the functional blocks of a cellular system. Tools like ClueGO  and Enriet al  obtainedl [et al  used thel [et al  construcprofile, that globally represents the main transcriptional regulation patterns within the pathway. Once pathway profiles have been defined, these are used to find connections between pathways. In this view, pathway links do not either depend on previous knowledge about protein-protein interactions or focus on identifying the genes shared between pathways, as it is the case with current pathway interaction approaches, but depend solely on pathway expression profiles.In general, these methodologies rely on the selection of differentially expressed genes, enriched pathways and protein-protein interactions. Therefore, when two pathways have few described protein interaction links, but still functionally influence each other, their connection might be missed by these methods . In thisIn previous work, we used dimension reduction techniques to obtain pathway expression profiles, which are associated with a physiological outcome . This realpha. The second step consists of obtaining a set of association rules that establish pair-wise connections between PPs. Direct and opposite rules are extracted, representing positive and negative correlation, respectively, between PPs. The quality value of the association rule is determined by its accuracy, which is basically a measure of the predictive performance of a rule in terms of the sensitivity and specificity metrics, commonly used in machine learning [Our PAthway Network Analysis approach (PANA) consists of two basic steps Figure . First, learning .The performance of PANA approach was assessed both in terms of the formal characteristics of the inferred networks and in terms of functional consistency by comparing results obtained for the yeast cell cycle data against a database of yeast functional data.s used for the definition of the sigma parameter (the covariance matrix of this distribution) represents the level of noise in the generated data. The value of s was fixed in 0.01 for this experiment. We evaluated the performance of the PANA algorithm as a function its control parameters alpha, which modulates the extraction of PPs, and accuracy, that controls the identification of pathway links, and of the noise in the dataset defined by s  would affect the network results. The simulated dataset contained 24,990 genes and 36 samples. Pathways were defined as blocks of genes of different size. Each pathway was assigned a different simulated expression profile (SEP) out of seven possibilities  value and varying levels of alpha and accuracy. Figure s >= 0.1, and also a constant percentage of direct rules involving pathways with the same SEP (right axis). Similar observations were obtained when considering opposite rules  that uses a Bayesian method to integrate functional evidences; the AS obtained exclusively from microarray data, denoted here as the microarray AS (mAS). From these AS metrics, and given any pair of pathways i and j, the functional association strength among these pathways (bASpij or mASpij) was calculated in terms of the bAS and mAS of the pathway genes. A more detailed explanation about the YeastNet2 data and the method used for computing the bASp and mASp are provided in the Methods section.Similarly to the results with the synthetic data, the number of pathway links inferred by PANA decreased with more restrictive alpha values and higher accuracy thresholds  and 252 associations. Therefore from this point onwards, any mention of the yeast cell cycle PANA refers to this particular network.Taken together, these results indicate that the two main control parameters of the PANA algorithm  work consistently and are efficient in deriving pathway networks that are coherent with available biological information. From the obtained results, we selected the yeast cell cycle pathway network obtained with an alpha value of 0.001 and an accuracy value of 0.90 for further analysis. This network has been chosen because it has the highest bASp and mASp values, the number of genes of the linked pathways, driving genes and other relevant information is included in the website of the PANA project at http://pathwaynetworkanalysis.org (see YCCPN website section). Several tests were designed to determine the relevance of the pathways associations inferred by the method. First, we asked whether the pathway links within the YCCPN provided greater functional evidence than expected by chance. For this purpose, the universal set of all possible yeast pathways associations and their bASp values were computed from YeastNet2, resulting in 2,541 possible associations with a bASp higher than zero. In Table bASp values in the universal set and the YCCPN are presented in terms of twelve different percentile values. From this table, it can be concluded that approximately 70% of the rules included in the YCCPN correspond to the first quartile of the universal set of pathway associations. Moreover, 40% of the YCCPN rules correspond to the 90% percentile of the universal set. Similar results were obtained when the network was compared to the bASp values obtained from randomly generated gene blocks of the yeast genome  is presented in Figure mAS values; i.e., the universal set of pathway associations integrated exclusively of the links supported by microarray data, resulting in 1,350 potential associations among pathways. When the mASp distribution was compared to the YCCPN mASp, we observed that approximately 30% of the links with the highest mASp values included in the network corresponded to the 80% percentile of the universal set of pathway associations. Moreover, we have evaluated the number of rules with gene commonalities in the YCCPN and found that 76.19% of the associations corresponded to pathways that had no genes in common, indicating that the presence of shared genes between pathways is not the underlying mechanism of the inferred networks.Subsequently, the reference ASp value distribution analysis was repeated using the bAS -collecting multiple sources of functional evidence- is considered instead of mAS (only microarray data). This claim is also consistent with the fact that the identified associations go beyond the presence of shared genes between pathways.The conclusions of these analyses are two-fold. On the one hand, we demonstrate that the YCCPN contains a significant enrichment of pathway associations of strong functional links according to available biological knowledge. On the other hand, our approach, even when using only gene expression data, is able to capture relationships between pathways that are evidenced by other types of functional information and cannot be found by univariate co-expression analyses. This is supported by the fact that this enrichment is higher when driving genes. Driving genes are those genes that contribute the most to the definition of the pathway signature can be understood as fundamental pieces in their regulation (see Methods). We hypothesized that these genes can reveal the functional relationships between pathways and aid in the interpretation of the pathway network links. To help with this discussion, we refer readers to the PANA site where a fully hyperlinked YCCPN can be browsed. For notation purposes, driving gene names have been underlined in this section.We have demonstrated that the PANA approach unravels a network of connections between pathways, and that it is backed up by functional data. The next question is how these links can be interpreted in terms of their biological meaning. Our approach to this is the detailed analysis of the molecular function of the pathway bASp: 304.97) and conform a cluster to other three DNA repair pathways. The connections between these processes are well documented by the literature and represent a suitable example to demonstrate the molecular fundaments of the pathway links. They share six genes corresponding to the MCM complex. However, none of those were selected as driving genes. Instead CLB6, CDC45, MCD1  (ssc1) and RAD53 included in Cell Cycle, and POL30  and RFA1  annotated to the DNA Replication pathway, were identified by minAS as major contributors to the connected pathway profiles. Note that RAD53  is annotated as DNA replication and DNA repair by other databases such as Saccharomyces Genome Database.These two pathways are strongly associated , a protein that acts as a processivity factor for DNA polymerase δ in eukaryotic cells. In response to DNA damage, this protein is ubiquitinated and involved in the RAD6-dependent DNA repair pathway.The regulation in eukaryotic cell cycle occurs during the transitions from the G1 to the S phase and from the G2 to the M phase . These rlication . MCD1  icohesion . MCD1  icohesion . Finallyrocesses . POL30  POL30  and MCD1 [MCD1, POL30, CLB2, and SUR7), whose periodic expression during the yeast cell-division cycle is well established. From these experiments, the POL30-MCD1  pair achieved a higher level of correlation for synchronized (0.86) and unsynchronized (0.75) samples, proving that the simultaneous expression of both genes is an intrinsic feature of yeast growth. Therefore, there is clear evidence for strong temporal pattern matching between these driving genes. RFA1  is a subunit of Replication Protein A Complex (RPA). There is evidence for the regulatory action of RAD53  on RPA during the early S phase [Recent studies have identified strong correlations between genes and MCD1 . In part S phase , and alsbASp value (81.47) and an accuracy of 100%. No genes are shared by the two pathways.This association is an example of a negative relationship between two pathways; i.e., basically, their pathway profiles are negatively correlated. However, the link has a high COX12, COX13, COX17, CYT1, VMA4 and QCR6, reveals the important roles of the genes associated with the cytochrome c-oxidase complex and ATP production in the conformation of the temporal profile of this pathway, indicating that COX activity and ATP synthesis are essential for the interpretation of this signature.Eukaryotic cells produce energy in the form of ATP molecules by two different pathways: via glycolysis and by oxidation of glucose to ethanol or lactic acid. In particular, Cytochrome c-oxidase (COX), the terminal enzyme of the mitochondrial respiratory chain (MRC), plays a key role by regulating the rate-limiting step of respiration. This regulation mechanism facilitates aerobic ATP production. An analysis of the driving genes of the oxidative phosphorylation pathway signature, HXK2 and ENO1 drastically reduce with an increasing flux [HXK2 and ENO1 and the identification of several glucose-repressed proteins as driving genes, such as ACS1, GAL10 and ALD3, suggest that this signature represents a situation where the glycolysis pathway is active, but reaching ATP saturation. This ATP concentration is promoted, in part, by the activity of the Oxidative Phosphorylation pathway, as indicated for the driving genes associated with COX and ATP syntheses. In other words, the negative link between Glycolysis and Oxidative Phosphorylation reflects the opposite involvement of ATP concentration in both pathways: while ATP production reflects the activity of the Oxidative Phospohorylation pathway through the action of its driving genes, high ATP levels down-regulate Glycolysis and modulate the expression of glucose-related genes. Note that this link is not explained by the presence of common genes between the pathways or protein-protein interactions, but by the action of a metabolic regulatory element.On the other hand, it is well known that the glycolytic flux is conditionally correlated with the ATP concentration in yeast. In particular, there is a strong negative correlation between glycolytic flux and intracellular ATP content; i.e., the higher the ATP content, the lower the glycolysis rate. Moreover, glycolytic enzymes ing flux . When cobASp of 96.77 and an accuracy of 100%. Moreover in this case, no genes are shared by the two pathways.The previous two examples represent connections between pathways that are well established by the scientific literature. Here, we discuss a pathway association that might not be so evident. The link between ubiquitin-mediated proteolysis and the purine metabolism has a SKP1 and CDC34 from the Ubiquitin-mediated Proteolysis, and on AAH1, POL32, RPA34 and RPC19 from the purine metabolism pathway. SKP1 is an evolutionarily conserved kinetochore protein that forms part of multiple protein complexes, including the SCF ubiquitin ligase complex. CDC34 is an ubiquitin-conjugating enzyme (E2) and a catalytic subunit of the SCF ubiquitin-protein ligase complex , which regulates cell cycle progression by targeting key substrates for degradation. AAH1 is the adenine deaminase-encoding gene and plays a central role in the salvage adenine pathway. There is a well-known relationship between the driving genes of the ubiquitin-mediated proteolysis and AAH1, which occurs during cell starvation. In response to nutrient limitation, S.cerevisiae cells enter a non-proliferating state termed quiescence. AAH1 is among the most tightly regulated genes upon entry into quiescence. Escusa et al [AAH1 regulation at this stage is conducted by the gene SAF1, but its regulatory role is dependent on genes SKP1 and CDC34. Figure SAF1, and reveals the negative correlation between SAF1 and AAH1, which is consistent with the negative regulatory role of the former on the latter. We postulate that this regulatory role of the ubiquitin-mediated proteolysis genes on key Purine Metabolism gene AAH1 might not be restricted to the quiescence process, but may operate during cell cycle progression, thus explaining the link between these two pathways.Several driving genes were identified for these linked pathways. Specifically, we focus on the interaction and activation patterns of sa et al  showed tPOL32 codifies a third subunit of DNA polymerase δ, involved in chromosomal DNA replication and required for error-prone DNA synthesis in the presence of DNA damage. An association between POL32 and the ubiquitin system occurs during DNA replication and repair processes, where a small ubiquitin-related modifier (SUMO) and ubiquitin jointly affect a key signal integrator at the replication fork, PCNA [et al [SRS2 in order to inhibit DNA recombination; in particular, Pfander et al [POL32 SUMOylation is essential for the recruitment of this helicase. This result is concordant with other works [POL32 is consistent with a simultaneous activity of the ubiquitin-mediated proteolysis pathway.The other driving genes of the Purine Metabolism are related with the synthesis, repair and degradation of DNA and RNA. In particular, rk, PCNA ,38. PapoA [et al  found ther et al  presenteer works ,42, whichttp://www.ncbi.nlm.nih.gov/geo/ and were previously used by Dutta el al [The yeast cell cycle analysis showed how PANA can describe pathway interconnections along a time course of events. In this section we evaluated how effective the PANA approach would be in studying differential pathway connectivity associated to a disease. For this, we used two microarray datasets generated for the study of Alzheimer Disease (AD). Both datasets were downloaded from the Gene Expression Omnibus (GEO) database ta el al  for the et al [The first dataset (GEO ID: GDS810) , studiedet al  specificWe have applied PANA to the same set of conditions. For the six cases, the pathway network corresponding to the disease and control samples were computed and contrasted, and we asked which pathways most frequently modify their association with the AD pathway when switching from healthy to disease status. The analysis revealed that PANA detects most frequent associations reported by Dutta, but in some cases with a lower frequency Table . Notablyet al [et al [et al [A new rule is related with the Focal Adhesion pathway. Alzheimer's disease is a neurodegenerative disorder that results from a loss of synaptic transmission and ultimately cell death. The presenting pathology of AD includes neuritic plaques composed of beta-amyloid peptide (Aβ) and neurofibrillary tangles composed of hyperphosphorylated tau, with neuronal loss in specific brain regions. In the other hand, focal adhesion proteins assemble into intracellular complexes involved in integrin-mediated communication between the extracellular matrix and the actin cytoskeleton, regulating many cell physiological processes including the cell cycle. Remarkably, recent studies report that integrins bind to Aβ fibrils, mediating Aβ signal transmission from extracellular sites of Aβ deposits into the cell and ultimately to the nucleus. In particular, Caltagarone et al  discuss l [et al  observedl [et al . Rosenstl [et al  also disInterestingly, and as mentioned before, PANA was able to detect associations between pathways that only share few genes or even none (i.e. Peroxisome and AD pathways do not share genes). This contrasts with Dutta's results where the median number of shared genes linked to the AD pathway is 11. For PANA new rules this number drops to 3. This result is a direct consequence of fundamental differences between both algorithms. Dutta's method is oriented towards the topological information of the pathways , whereas our methodology connects pathways based on their shared activity profiles. Still, the literature survey returns evidence of functional connections between AD and these pathways as discussed before.Understanding the complexity of molecular interactions in a cellular system is one of the most challenging aspects of current genomics research. Many analysis approaches have been developed in recent years and have attempted to exploit functional information and multivariate analyses to provide answers about molecular systems functioning. These approaches rely on the systems biology concept; hence they analyze the collective behavior of groups of genes. In this work, we take one step forward by presenting a methodology that not only studies blocks of genes jointly, but also establishes relationships between these blocks. The result is a global, interconnected view of the system's transcriptional status.functional block). while the EA methods typically rely on identifying a significant majority of gene set members associated with the phenotype and consider all the genes equally contributing to the block's functionality, PANA is built upon the analysis of the correlation structure within the group of functionally related genes .There are some substantial differences between the PANA approach and most functional Enrichment Analysis methods. Probably, the most relevant one is the way that PANA extracts information from the gene set  and that this might be concentrated in a subset of pathway genes. This procedure is able to address situations where pathways are roughly defined, include genes that are not necessarily co-expressed or when the regulation of the pathway is concentrated in a low number of switch genes. For example, the KEGG Purine Metabolism pathway (PMP) in yeast, present in our YCCPN results, includes reactions involving purine nucleotides and it branches out to histidine and thiamine metabolisms, sulfur assimilation and allantoin degradation pathways, among others. These other sub-pathways are not particularly seen as being highly regulated in our analysis. Additionally, in yeast . PANA reSAF1 protein. This is an interesting property because it makes the approach amenable for application in situations of insufficient or misplaced pathway database annotation or when common regulatory elements are not proteins.Another differential characteristic of the PANA approach is that the links between pathways do not derive from shared components or protein-protein interaction data, but exclusively from a co-transcriptional analysis. Co-expression has been largely used to infer gene regulatory networks -63, but PHO85, which negatively controls the expression of numerous genes induced under nutrient limitation conditions [UGP1, which catalyzes the reversible formation of UDP-Glc, a source compound in glycogen and trehalose biosynthesis. In our analysis, UGP1 is the driving gene of the Galactose Metabolism pathway and is negatively correlated with PHO85. Moreover, two driving genes in the Glycine, Serine and Threonine Metabolism pathways, GCV2 and CHA1, are related to nitrogen utilization under nutrient-limiting conditions. Hence the fourth profile of the Cell Cycle pathway might witness the coordination of this pathway with the nutritional state of the yeast cell.An unique PANA feature is the possibility of presenting different aspects of pathway behavior when the dimension reduction step results in multiple principal components being selected as pathway profiles. Each one can establish links with different pathways. For example, the Cell Cycle pathway is represented in the YCCPN by three profiles corresponding to principal component one, two and four of the PCA of the Cell Cycle gene expression matrix. Cell cycle_1 collects most of the canonical controllers of cell cycle progression and is linked to several DNA processing pathways, as discussed in the Results section. However, Cell cycle_4 presents a profile of activation at late time points of the yeast experiment. This profile is connected to the Glycine, Serine and Threonine Metabolism pathways, and indirectly to the Galactose Metabolism pathway Figure . One of nditions . One of Another interesting application of PANA is to unravel changes in pathway connectivity that associate to a given phenotype. This is relevant not only to understand the new functional status acquired in a disease situation, but also to explore possible side effects of treatments. Methods for differential molecular wiring have been described at the gene level ,69 and hPANA was developed in the microarray analysis context, but can be extended to other high-throughput methodologies provided that a functional database is available for feature annotation. The adaptative association rule algorithm, used for network construction, recommends evaluating the expression along a sufficient number of samples. This might preclude the utilization of this approach in reduced sample size experiments, but does not restrict the method to time series data. Besides, case control studies and multifactorial designs are potential experimental set-ups for PANA. On the other hand, the dimension reduction technique used in the first algorithm step, PCA, analyzes covariation across the entire data matrix. Other multivariate analysis approaches, such as biclustering or spectral analyses, might extend the possibilities of the method to identify the pathway profiles associated with a restricted number of samples and to fine-tune the network analysis to specific conditions within the experimental design.In summary, we propose a novel method for the interpretational analysis of high-throughput data in systems biology research. This approach not only offers global views of the interconnections among the different functional blocks of the system, but also allows focusing on these links to reach the molecular basis of the network. We believe PANA is a useful tool to improve our understanding of the functional interdependencies that operate within complex biological systems.pathway signatures or pathway profile (PP), which collect most of the gene expression variation within the pathway and represent the pathway activity changes along the experiment. These PPs are used as input data to derive adaptive association rules based on mutual information maximization [The proposed approach relies on the combination of dimensionality reduction methods with machine-learning techniques. Given a gene expression experiment and an annotation scheme for genes in pathways or functional modules, this method creates a gene expression submatrix for each pathway and uses a Principal Component Analysis (PCA) to reduce the dimensionality of the pathway expression data . Each pamization . These rX be the expression data matrix of dimension N × M, where N is the number of genes measured and M is the total number of samples. Let xnm n:1 ... N, m:1 ... M be the expression value of gene n in sample m. Let F be the set of functional annotation (pathways) of the genes in the transcriptomic dataset. Let f Nbe the number of genes associated with each pathway f ∈ F.Given a transcriptomics experiment, let ∈ F, create the expression submatrix of X, fX, with the f Nrows corresponding to the genes associated with pathway f and with the same M columns as X. Obtain fc Xas the transposed, column-mean centered transformation of fX.1. For each ff, obtain a number of pathway signatures fh, by applying a Principal Component Analysis- (PCA) based procedure that uses bootstrapping to obtain pathway signatures with a given confidence alpha according to the following procedure:2. For each pathway X with M columns, sample M columns with replacement to obtain rX. Use r Xto calculate the variance of each gene. Approximate the gene variance distribution by a Gamma distribution as described [alpha percentile value of this distribution.a) Given the original expression matrix escribed  and obtafr Xand select the principal components (PC) with variance  higher than the gamma cutoff. Let PC1, PC2, ..., PCk(r) the selected PCs for matrix frX, where 1 ≤ k(r) ≤ rank(frX).b) Apply PCA to each bootstrap pathway submatrix f be the set of all the selected PCs in the R repetitions: Hf = {1,..., k(1), 1,..., k(2), ..., 1,..., k(R)}. Hence, each i∈Hf has a frequency qfi. Select i∈Hf with frequency qfi higher than a Q threshold  as the number f hof pathway signatures for pathway f.c) Repeat 3 and 4 R times. Let Hf hprincipal components selected using the criterion described above, the PCA decomposition of submatrix fc Xcan be written as: fc X= fPft T+ E, where f Tis the scores matrix for pathway f (with dimensions M × fh), f Pis the loadings matrix for pathway f (with dimensions f N× fh) and E is an error term. The scores matrix f Trepresents the f hpathway signatures for pathway f. These new f hfunctional variables represent the coordinative expression patterns of the genes associated with pathway f. f Pcollects the contribution of each gene to each pathway signature.3. Given the PLM) through the row-wise concatenation of the f Tscores matrices of all the pathways with at least one selected pathway signature. Hence, all the pathway signatures selected during Step 5 are included in the PLM, which has ∑f∈F f hrows and M columns.4. Create a pathway level matrix . The discretized matrix obtained for the PLM and pathway signature j (jPLM) is denoted as jδPLM. For the computation of j δPLMa set of discretization thresholds (tlj) for each signature pathway l (lPLM), with l ≠ j, is calculated in relation to jPLM. The algorithm for computing tlj considers each score value shown by the pathway signature l PLMas a candidate threshold tc. Therefore, for each possible c tvalue, the sample set l PLMis partitioned into two subsets, namely S-1 and S1. S-1 contains all the samples where l PLMhas a score value that is less than or equal to tc, whereas S1 contains all the samples where l PLMhas a score value that is greater than tc. In other words, S-1 and S1 represent sample sets (columns of the PLM) where l PLMhas low and high activity levels, respectively, on the basis of tc. Next, calculate the partition entropy, which is a statistical indicator of the quality of threshold tc as a discretization value for l PLMin relation to the discretization of jPLM. The partition entropy is computed from the discretized values of rows l PLMand jPLM, where l PLMis discretized using tc, while j PLMis discretized using its average score value. In numerical terms, the value returned by this metric is a real number between 0 and 1. When the partition entropy value associated with a discretization approximates 0, the threshold tc that generates this discretization represents a better solution. Consequently, the tc that minimizes the partition entropy is selected as tlj. Details about the equations for the computation of the entropy and partition entropy metrics can be found in Mitchel [1. For each pathway signature, make an adaptive discretization of tization . The dis Mitchel  and Koha Mitchel , respectj δPLMin order to determine which pathway signatures are linked with pathway signature j (jPLM). This task is carried out by a classifier optimization method [2. Extract the association rules from the discretized matrix by detecting covariation between pairs of pathway signatures. The rules inference procedure is applied to each n method , which iWhere:k = number of pathway signatures in PLM (k = ∑f∈F fh)• P is the space of all the vectors v of dimension k, so that i vrepresents a class of association rule ∀i, i = 1 ... k,• j δPLMis the discretization of the PLM for pathway signature j,• j,• j δPLMis a performance function that evaluates the accuracy of j δPLMdata,• . As PPs are modeled by PCA, loadings represent the contribution of each gene to the definition of the PP. Genes with low loadings are poorly correlated with the pathway profile, while those with high loadings are highly correlated. Frequently, a subset of pathway genes can be identified as being mainly responsible for the definition of the PC. We will refer to these genes as driving genes as they are "pulling" the pathway signatures for having the greatest weights in the PC.In order to better interpret the associations between pathways, we propose to determine which genes contribute most to create the driving genes of each pathway profile.To identify driving genes, PANA uses the minAS method , which iyeast cell cycle PANA (YCCPN) were published by Spellman et al [S. cerevisiae cell cultures, which were synchronized by three different methods: the cdc15, cdc28 and alpha factors. The data-transformation method used by Spellman et al returned background-corrected signal log ratios, with control as an average expression level extracted from asynchronous cultures of the same cells growing exponentially at the same temperature in the same medium. For this work, the cdc15 experiment was selected as the benchmarking dataset for the generation of YCCPN because it contains the largest number of data points (24 samples), thus providing the largest number of instances for the machine-learning method. In this time series, cell cycle progression was blocked at a specific point by conditional factor CDC15 which, if removed, permits cells to recommence progression through the cell cycle in a synchronous fashion.The microarray data used for the inference of an et al . These eTo validate YCCPN, the functional annotation data contained in YeastNet2  were useYeastNet2 contains a total of 102803 links covering 5483 yeast proteins, this which represents 95% of the validated yeast proteome and provides an association score (AS) for each pair-wise gene relationship. AS values are obtained from each kind of experimental evidence separately  and jointly combining, following a Bayesian method, different evidence AS into an unique AS value per gene association.bAS) represents the amount of scientific evidence of a putative pathway association by taking into account all the biological data sources, and the AS value obtained from microarray evidence exclusively, named Microarray AS (mAS). From both AS metrics, and given a pair of pathways i and j, the functional association strength between these two pathways (bASpij or mASpij) was computed as the sum of the functional association scores of all the gene pairs which can be established between these pathways. Moreover, a mean bAS and mAS value for each network were calculated as the average bASp and mASp values, respectively, of its pathway associations. Although ASp is a summing-up value, there was no correlation between the magnitude of ASp and the size of the pathway involved , and the temporal profile that corresponds to this gene. This information is useful for understanding the direction of the principal component which represents the signature pathway. Finally, the annotation of samples at cell cycle phases can be found in the section Additional Information. In particular, the cdc15 dataset contains 24 samples obtained during 300 minutes [The pathway signatures of YCCPN are also available as a pdf file in the section  minutes .The authors declare that they have no competing interests.IP and AC designed the computational methodology, conceived the study and drafted the paper. MJN designed the method for generating simulated data. ST obtained driving genes by adapting the minAS algorithm. SG contributed to the graphical representation of the YCCPN. DM performed the variability analysis. JSD created the project web site. JD helped in drafting the manuscript. All the authors read and approved the manuscript.Figure S1. Performance of control parameters for opposite rules. Average correlation among Pathway Profiles of opposite rules  and percentage of opposite links with same simulated expression profile SEP  in the resulting network, as a function of the accuracy threshold. The upper and lower border of polygons indicate the range of variation at different alpha values. Noise level was set at 0.01. Figure S2. Association score enrichment against random pathways. For each pathway association rule R present in the YCCPN, a total of 100 random pathway associations with the same cardinality pattern as R were generated, the ASp values computed and the percentile position of the ASp of R in its reference distribution was obtained. The cardinality pattern of a rule is defined by three values: the amount of genes contained in each pathway linked by the rule, and the number of shared genes between both pathways. This analysis revealed that most (63% of the links) of the rules obtained by the PANA method are located in the 20% percentile of the 100 random trials of their gene cardinality pattern. In particular, the average bASn of network integrated by the random links is low (28.50) in comparison with the bASn of the YCCPN (123.61). The difference between YCCPN bASn and the random bASn was statistically significant . Figure S3. Independence of the ASp score of the pathway size. Relationship between pathway association scores  and the number of genes in the left (dot) and right (cross) pathways. Lack of correlation is observed in all cases. Table S1. Simulated expression profiles (SEP). Temporal expression patterns defined for the generation of simulated time series for the artificial pathways. Table S2. Network size (number of pathway associations) inferred using different accuracy and alpha values in the Yeast Cell Cycle network obtained by PANA.Click here for file


Database URL:http://pathcards.genecards.org/The study of biological pathways is key to a large number of systems analyses. However, many relevant tools consider a limited number of pathway sources, missing out on many genes and gene-to-gene connections. Simply pooling several pathways sources would result in redundancy and the lack of systematic pathway interrelations. To address this, we exercised a combination of hierarchical clustering and nearest neighbor graph representation, with judiciously selected cutoff values, thereby consolidating 3215 human pathways from 12 sources into a set of 1073 SuperPaths. Our unification algorithm finds a balance between reducing redundancy and optimizing the level of pathway-related informativeness for individual genes. We show a substantial enhancement of the SuperPaths’ capacity to infer gene-to-gene relationships when compared with individual pathway sources, separately or taken together. Further, we demonstrate that the chosen 12 sources entail nearly exhaustive gene coverage. The computed SuperPaths are presented in a new online database, PathCards, showing each SuperPath, its constituent network of pathways, and its contained genes. This provides researchers with a rich, searchable systems analysis resource. The systematic analysis of biological pathways has ever-increasing significance in an age of growing systems analyses and omics data. Mapping genes onto pathways may contribute to a better understanding of biological and biomedical mechanisms. The literature provides a large collection of pathway definition sources . PathwayIndeed, the definition of the boundaries of biological pathways differs among sources, as exemplified by the highly studied processes of fatty acid metabolism  or the TPrevious attempts to unify pathways from several sources include NCBI’s Biosystems , PathwayHere, we describe an approach aimed at generating an integrated view across multiple pathway sources. We applied a combination of nearest neighbor graph and hierarchical clustering, utilizing a gene-content metric, to generate a manageable set of 1073 unified pathways (SuperPaths). These optimally encompass all of the information contained in the individual sources, striving to minimize pathway redundancy while maximizing gene-related pathway informativeness. The resultant SuperPaths are integrated into GeneCards , enablinPathway gene sets were generated based on the GeneCards platform , implemeJ) for all 11 325 pathway pairs. We then selected a sample of 30 pairs containing 28 unique pathways out of a total of 87 pairs with J ≥ 0.3, ensuring maximal representation for larger pathways. Each of the 28 pathways was queried in MetaPathwayHunter against the entire gamut of 151 with default parameters . We found that 29 out of the 30 sample pathway pairs obtained a significant MetaPathwayHunter alignment (P ≤ 0.01). As only 64 of the 4228 comparisons showed such a P-value, the probability of obtaining this result at random is 1.6 × 10−53 (Supplementary Table S1). Thus, Jaccard scores appear as excellent predictors for the results of the more elaborate method. A full account of interpathway pairwise similarity is available upon request.In the analyses performed, we utilized gene content overlap to estimate pathway similarity. This was done based on the Jaccard coefficient, that measures similarity between finite sample sets, and defined as the size of the intersection divided by the size of the union of the sets. To examine the legitimacy of this method, we performed a comparison to an alternative methodology, embodied in MetaPathwayHunter pathway comparison, that incorporates topology in pairwise pathway alignment . For suc2T, the upper cutoff, equivalent to hierarchical clustering. ii) Performing cluster extension by generating new best edges, i.e. joining every pathway to a pathway showing the highest score, as long as it is ≥1T, the lower cutoff, akin to nearest neighbor joining. If two or more target pathways have the same best score, all are joined. Each resultant connected component is defined to be a pathway cluster (SuperPath). Identical pathway sets were joined without considering each other as nearest neighbors . This clustering algorithm is order independent.For the main pathway clustering algorithm, we applied a method described elsewhere , which iNp is the number of pathways that include a certain gene, averaging for each pathway over all genes in the SuperPath (divided by the number of genes Ng). Uniqueness of genes Ng is the number of genes included in the relevant pathway, averaging for each gene over all SuperPaths including a gene. In order to then find the best tradeoff between the two scores, we summed up the average sU and sI for each set of 1T and 2T cutoff parameters. Thus s + IsU was calculated for each set of parameters to find the two parameters for which the tradeoff between pathway and gene uniqueness would be optimal. The best cutoffs by maximizing sU + sI were 1T = 0.3 and 2T ≥ 0.5. Further fine tuning of the upper cutoff was performed by resampling of the data, a technique employed by Levin and Domany  was set to Publication and Protein-Protein Interactions (PPI) data for each gene were obtained from the GeneCards database, including several combined sources. Publications sources of GeneCards include both manually curated publications (e.g. UniProtKB/Swiss-Prot) as well as text mining approaches that report connections between a gene and a list of publications. A shared publication between two genes is an association of both genes to the same publication and does not indicate a direct interaction between the genes. PPI scores between pairs of genes are also based on several interaction sources in GeneCards. Unlike shared publications, PPIs reflect direct interactions between the two gene products.A randomized set of pseudo-SuperPaths was generated, such that the pseudo-SuperPaths are the same size and quantity as the SuperPaths, albeit with genes assigned at random (from the list of genes with any pathway annotation). Gene pairs that belong to at least one SuperPath, but do not belong together in any individual pathway (the test set) were analysed for the number of shared publications and PPI scores for each pair. In comparison, gene pairs that belong to at least one pseudo-SuperPath, but do not belong together in any individual pathway (the control set) were analysed for the same attributes. To compare the two sets which are of different sizes, a random sample of the larger set (the control set) of the same size as the smaller set (the test set) was compared with the smaller set. A one-sided Kolmogorov–Smirnoff test was performed to compare between the test and control sets.P values was computed.Differentially expressed sets of genes were obtained from the GeneCards database  containiSuperPaths have been implemented in GeneCards and are now included in the standard procedure of GeneCards generation. PathCards is an online compendium of human pathways, based on the GeneCards database, presenting SuperPath-related data in each page.http://www.genecards.org/ , we found that the gene count increment is ∼2% of the currently analysed total. This is an indication that the chosen 12 sources provide adequate coverage of human gene-pathway mappings. Switching between the six non-included sources and six included sources of similar size give a very similar graph, with merely 4% increment in gene count .We analysed 12 pathway sources included in GeneCards rds.org/ A. The tords.org/ B, suggesSupplementary Figure S2).Analysing the gene repertoires of the four largest sources A, we fouWe performed global pathway analysis aimed at assigning maximally informative pathway-related annotation to every human gene. For this, we converted the pathway compendium into a set of connected components (SuperPaths), each being a limited-size cluster of pathways. We aimed at controlling the size of the resulting SuperPaths, so as to maintain a high measure of annotation specificity and minimize redundancy.J) metric  Preprocessing of very small pathways: pathways smaller than 20 genes were connected to larger pathways (<200 genes) with a content similarity metric of ≥0.9 relative to the smaller partner. ii) The main pathway clustering algorithm: this was performed using the Jaccard similarity coefficient  metric  . The resultant set of SuperPaths indeed enhances the uniqueness SU as depicted in Our procedure pointed to an optimum at T2 = 0.7 B and 5A.T2 = 0.7 A and 82.Supplementary Figure S3), suggesting a substantial preservation of gene groupings. Notably, applying pure hierarchical clustering (1T = 2T = 0.3) resulted in a single very large cluster with 1046 pathways . This sub-optimal performance of pure hierarchical clustering is general; any of the examined cases of 1T = 2T  in the SuperPath cluster. For simplicity, the option of Supplementary Figure S4). Second, by unifying into SuperPaths, the number of gene pairs is further enhanced, reaching 8.3 million .One of the major implications of the process of SuperPath generation is elucidating new connections among genes. This happens because genes that were not connected via any pre-unification pathway become connected through belonging to the same SuperPath. The unification into SuperPaths is important in two ways: first, it brings, under one roof, pathway information from 12 sources, each individually contributing ∼9000 to ∼5 million instances of gene pairing, for a total of 7.3 million pairs  and in PathCards  http://pathcards.genecards.org/, a GeneCards companion database presenting a web card for each SuperPath. PathCards allows the user a view of the pathway network connectivity within a SuparPath, as well as the gene lists of the SuperPath and of each of its constituent pathways. Links to the original pathways are available from the pathway database symbols, placed to the left of pathway names. PathCards has extensive search capacity including finding any SuperPath that contains a search term within its included pathway names, gene symbols and gene descriptions. Multiple search terms are afforded, allowing fine-tuned results. The search results can be expanded to show exactly where in the SuperPath-related text the terms were found. The list of genes in a PathCard utilizes graded coloring to designate the fraction of included pathways containing this gene, providing an assessment of the importance of a gene in a SuperPath. Other features, including gene list sorting and a search tutorial, are under construction. PathCards is updated regularly, together with GeneCards updates. A new version is released 2–3 times a year.SuperPath information is available both in the GeneCards pathway section . PathCards enables one to then view which genes are more highly represented within the constituent pathways. Our algorithm thus mimics human intervention, and greatly simplifies the task of finding concurrence within and among pathway sources.This study highlights substantial mutual discrepancies among different pathway sources, e.g. with regard to pathway sizes, names and gene contents. The world of human biological pathways consists of many idiosyncratic definitions provided by mostly independent sources that curate publication data and interpret it into sets of genes and their connections. The idiosyncratic view of the different pathway sources is exemplified by the variation in pathway size distribution among sources D, where versus pathways tables as search output. This scheme allows useful comparisons as related to specific search terms, but is not leveraged into global analyses of interpathway relations. A third line of action is exemplified by ConsensusPathDB  indicates that the Jaccard coefficient is a good approximation of the more elaborate pathway alignment procedure , having only one constituent pathway. This is an outcome of the specific cutoff parameters used. However, this provides a useful indication to the user that a singleton pathway is distinct, differing greatly in its constituent genes from any other pathway.Supplementary Figure S2B), and such events are faithfully conveyed to the user.This SuperPath generation process is intended to reduce redundancies and inconsistencies found when analysing the unified pathways. Although SuperPaths increase uniqueness as compared with the original pathway set B, some rA clarifying example is that of the 40 pathways whose names include the string ‘apoptosis’. The final post-unification list has 10 SuperPaths whose name includes ‘apoptosis’. This obviously provides the user with a greatly simplified view of the apoptosis world. Yet, at the same time the outcome is replete with instances of two name-similar pathways being included in different SuperPaths. Employing a more stringent algorithm would result in over-clustering, which would in turn reduce informativeness see C.J = 0.03 and 0.13. The need to view information on pathways with low pairwise similarity is addressed in Supplementary Figure S6, and is available as a text file upon request.In parallel, there are pathways with overlapping functions that are not consolidated into one SuperPath. For example, the pathway ‘integrated breast cancer pathway’ does not unify with the pathways ‘DNA repair’ and ‘DNA damage response pathway’, despite the strong functional relation of breast cancer with DNA damage and repair . This isFinally, when looking at the number of contributing sources per SuperPath B, it is Supplementary Figure S5A).Biological pathway information has traditionally been a central facet of GeneCards, the database of human genes , 42, 43.http://pathcards.genecards.org/, a database that encompasses and displays such information in greater detail. PathCards has a page for every SuperPath, showing the connectivity of its included pathways, as well as gene lists for the SuperPath and its pathways. For every SuperPath, we also show a STRING gene interaction network (GeneCards is gene-centric and inherently does not present (Super) pathway-centric annotations. We therefore developed PathCards  network  for the Supplementary data are available at Database Online.


Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless ‘geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies. This circumstance hampers the delineation of a global picture of cancer biology and prevents from efficient integration of high-throughput data and application of bioinformatics and systems biology approaches.2 The effort towards systematic collection and formalization of the knowledge about molecular interactions in a computer-readable form gave birth to pathway databases.3 Nevertheless, there is no up-to-date pathway database that systematically depicts molecular processes specifically implicated in cancer.Understanding of tumorigenesis has drastically improved recently owing to extensive studies of the implicated molecular mechanisms.4 manifested by co-participation, interaction or co-regulation of molecular entities in several cell signalling processes. Understanding connections between molecular mechanisms is important for determining potential therapeutic intervention points.6Most existing pathway databases provide a view on molecular mechanisms as disconnected processes, splitting their content into ‘canonical pathway' representations. However, cancerogenesis affects simultaneously multiple cellular processes and their crosstalk. Therefore, cancer research can benefit from the reconstruction of cellular signalling in the form of a comprehensive map, representing the complexity of pathway crosstalk7 In addition, handful software tools for charting the large biological maps, such as CellDesigner,8 CellIllustrator9 and others, stimulated the community to create large comprehensive maps of molecular interactions implicated in various biological processes. Therefore, large maps of signalling pathways,16 of complex metabolic processes,17 as well as maps specific for certain cell types19 or non-mammalian organisms21 have appeared. Comprehensive maps representing biological mechanisms that are implicated in particular diseases such as Alzheimer disease or rheumatoid arthritis have also been charted recently.23To enable reconstruction of comprehensive cell signalling maps, the common standard for depicting biological knowledge, the Systems Biology Graphical Notation has been developed.25 Cancer Cell Map database was created and included into the Pathway Commons network biology resource.26 However, those resources are far from being comprehensive representations of cancer-related processes. Therefore, there is the need for a global and detailed representation of molecular mechanisms involved in cancer, depicting the latest discoveries in this rapidly evolving field.Several attempts have been undertaken to connect cancer-driver genes into relatively large networks or to represent them in pathway databases.We have thus constructed the Atlas of Cancer Signalling Network (ACSN), which describes in detail signalling and regulatory molecular processes that occur in a healthy mammalian cell but that are frequently deregulated during cancerogenesis. ACSN is characterized by several features whose combination makes it unique: (i) it is a comprehensive description of cancer-related mechanisms retrieved from the most recent literature; (ii) its content is represented as an interactive global map that can be browsed with Google Maps interface; (iii) it has an online discussion forum, allowing users to provide feedback; and (iv) it has a built-in tool box for visualization and analysis of high-throughput data in the context of the comprehensive signalling network.1 Currently, ACSN covers signalling pathways involved in DNA repair, cell cycle, cell survival, cell death, epithelial-to-mesenchymal transition (EMT) and cell motility. The information included in ACSN is organized in three hierarchical levels: (i) the seamless global map with all molecular interactions, (ii) containing five maps of biological processes, and (iii) in turn comprising 52 functional modules .The biological mechanisms represented in ACSN are briefly described below and their correspondence to the hallmarks of cancer is made explicit. A more detailed description can be found at ACSN website  DNA repair pathways.DNA repair mechanisms and their coordination with cell cycle through checkpoints are frequently deregulated in familial and sporadic cancers.31 Cell survival together with apoptosis (see below) corresponds to the ‘Resisting cell death' hallmark of cancer. Cell survival mechanisms determine ‘Sustaining proliferative signalling' and 'Evading growth suppressors' cancer hallmarks through multiple signalling cascades upstream of the cell cycle. In ACSN, representation of cell survival mechanisms is divided into four major modules. Mechanisms depicted on the survival map are involved in modulation of the antiapoptotic, proliferation, EMT and migration processes. Cell survival description in ACSN is visually organized as a chessboard with squares that makes the connection between individual module maps more straightforward.Signalling mechanisms involved in cell survival are widely exploited by cancer, ensuring the viability of a cell in diverse conditions, such as hypoxia, autophagy, anoikis and lack of nutrients.33 ACSN contains seven modules devoted to mechanisms of apoptosis as intrinsic and extrinsic regulatory mechanisms and to the core of the apoptotic machinery. The interplay between apoptosis and part of glucose metabolism (Warburg effect) is represented,34 which creates a link to another cancer hallmark, ‘Deregulating cellular energetics'. In ACSN, apoptosis description is visually organized in several cell compartments with the structure of mitochondrial membranes represented in more detail.Programmed cell death is one of the fundamental cellular processes that has a crucial role in tumorigenesis. Frequent deregulation in programmed cell death machinery, especially apoptosis, is one of the central hallmarks of cancer, ‘Resisting cell death'.35 that can be activated in wound healing and fibrosis. In cancer, EMT promotes invasion of tumour cells into surrounding stromal tissues. Description of cell motility-related processes corresponds to ‘Activating invasion and metastasis' cancer hallmark. In ACSN, the EMT and cell motility map is composed of five interconnected functional modules with the core EMT regulators placed in the centre of the map. Graphically, the map represents a cell directly interacting with a neighbour cell and with extracellular matrix.EMT and cell motility is a normal process during embryonic development36 empowered by Google Maps engine. Navigation features such as scrolling, zooming, markers, callout windows and zoom bar, are adopted from the Google Maps interface. All map components are ‘clickable', making the map interactive . The user-friendly interface connects together the global Atlas map, the individual maps of biological processes and the functional module maps. The visualization and navigation of maps are supported by the NaviCell web-based environmenteractive . ACSN aleractive .ACSN is a large and detailed map of molecular interactions representing multiple distant crosstalks between molecular processes. This richness is a ‘double-edged sword', because the density of the presented information makes the navigation through the map difficult. This problem is addressed in ACSN in four complementary ways.First, exploration of the content is facilitated by the hierarchical modular structure of ACSN. Shuttling between the global ACSN map to individual maps of biological processes and further to functional module maps is possible. It allows visualization of only a limited number of molecular species and reactions related to a particular molecular mechanism .Second, the semantic zooming feature in ACSN simplifies navigation through the large maps of molecular interactions, showing readable amount of details at each zoom level. Gradual exclusion of details allows exploration of map content from the detailed towards the top-level view . PruningThird, to facilitate exploration of individual entities of the map, the function of selecting and highlighting only species of interest is available in the ACSN user interface (detailed in the user guide at the ACSN website). It is also possible to select and highlight neighbours of a molecular species of interest. This way, the connectivity of the selected entities through the map can be studied. Step-wise enlarging of the neighbourhood coverage can help to follow the propagation of signalling from one molecular entity to another .The fourth way to explore the map is via detailed annotation post of each component of the map that can be accessed through the callout window or from the selection panel . AnnotatThe field of oncology evolves rapidly, therefore ACSN maps require regular updates. The process of maintenance involves map managers that regularly collect the users' comments posted in the blog of ACSN, check the latest scientific literature and update the maps and the annotations accordingly. An automated procedure supports update of ACSN and archives older versions of posts including users' comments, thus providing traceability of all changes on the maps and all discussions in the blog .37 or KEGG PATHWAY.38 However, one can suggest that focussing on fewer cellular processes allows performing a more detailed description of molecular mechanisms resulting in a denser and richer interaction network. We compared ACSN with two pathway databases that have similar structure and level of details: REACTOME37 and National Cancer Institute Pathway Interaction Database (NCI PID).25 BiNoM plugin of Cytoscape was used to process BioPAX 3.0 files,40 storing the content of the three pathway databases compared. We only used protein-related parts of each database and excluded the metabolic reactions involving small molecules only. We have found that each pathway database among the three is characterized by a specific distinguishing feature. REACTOME is the largest among the three pathway databases in terms of the number of proteins and reaction. However, ACSN is almost three times denser in terms of the number of reactions per protein and is comparable to NCI PID in this aspect. NCI PID is the densest among the three databases in terms of the number of protein complex species described per protein . These LCCs were surprisingly similar in size and in diameter but very different in terms of the topological properties if the direction of reactions was taken into account. Interestingly, ACSN reaction graph is almost completely included into one gigantic connected component . REACTOMJournal of Biological Chemistry, while ACSN uses this journal in 13% of annotating references, exploiting other journals more broadly . The median date of an annotated reference in ACSN is 4 years younger compared with REACTOME and NCI PID . ACSN us broadly . In geneet al.41 and 31% (150 out of 488) of the more exploratory Catalogue Of Somatic Mutations In Cancer (COSMIC) census gene list,42 demonstrating that ACSN includes a significant number of cancer-related processes for which a sufficient mechanistic understanding exists. ACSN will be extended in the future with new mechanisms such as the role of immunity in cancer.To further quantify the cancer-related specificity of ACSN and its degree of completeness, the coverage of various census lists of cancer-related genes by ACSN was computed. ACSN covers 60% (87 out of 138) of the cancer-driver genes from Vogelstein 43 can be used to import and visualize expression data , gene copy-number data, mutation data and simple gene lists, such as small interfering RNA (siRNA)-based screening hit list. In a more elaborated situation, a weighted gene list can be visualized such as the distribution of mutation frequencies observed in multiple cancers. The ACSN maps can be used to visualize and interpret the global trends in gene expression data as well as the phosphoproteomic data that are able to provide more relevant information than the gene expression data alone.44 Interestingly, users can also visualize the results of any statistical analyses applied to a data set. For instance, a matrix of P-values resulting from a differential gene expression analysis between two conditions  can be visualized by map staining. In addition, users can perform a functional enrichment of ACSN modules statistical analysis from a gene list  directly in the ACSN environment  functional module of the apoptosis map . The MOM49 making EMT machinery and specifically SMAD450 attractive targets for sensitization to Gemcitabine.In addition to the suggested cell cycle checkpoint and survival pathway-related mechanisms of sensitization to Gemcitabine, unexpectedly high local density of siRNA hits is found in the EMT_REGULATORS functional module. In particular, the siRNA target SMAD4 is found in several molecular complexes together with regulators of EMT . It has 51 SMAD4 has numerous and often opposite effects on cells and the surrounding tissue in a context-dependent manner. For example, with respect to cancer SMAD4 can act as either suppressor or promoter of tumour progression.52 It can exhibit pro-EMT54 or anti-EMT and anti-invasive properties56 depending on cancer or tissue type, stage of tumour development and microenvironment.58However, it is known that SMAD4 is a multifunctional protein participating in a wide range of cellular processes, such as growth, proliferation, differentiation and apoptosis.Although inhibition of SMAD4 indeed sensitizes cells to Gemcitabine in the original study, given the involvement of SMAD4 in multiple cellular processes, undesired off-target effects in patients could be anticipated. To avoid these effects, instead of directly inhibiting SMAD4, targeting interactions between SMAD4 and its specific partners might help to drive SMAD signalling towards a less-toxic Gemcitabine-sensitive outcome. Here we suggest that several components of the EMT-related complexes where SMAD4 is found can represent potential targets for sensitization to Gemcitabine , thus prThese examples demonstrate that network-based visualization of siRNA screens can help in prioritizing targets and in designing optimal combinations of targets for sensitizing cell to conventional drugs.59 Correlating mutation patterns with other tumour characteristics can help to interpret the role of these mutations in cancer progression. To illustrate applicability of ACSN for visualizing mutation data, we mapped the most frequently mutated oncogenes and tumour-suppressor genes (TSGs) in breast and lung subsets from TCGA data41 on ACSN . The territory ‘occupied' by a module on the ACSN map is highlighted using a colour gradient denoting the module ‘activity' . This ‘map staining' data visualization technique can be combined with other types of data visualization.Interpretation of genome-wide data obtained from cohorts of tumour samples using microarrays or massive sequencing technologies requires functional annotation tools.62 Basal-like tumours are highly mitotic, overexpressing DNA repair-related and phosphoinositide-3 kinase/AKT pathways. Consistently with this, Cell cycle, DNA repair modules and Survival and Apoptosis modules are overactivated in ACSN, . The ACSN maps are freely available for downloading from the ACSN web page (https://acsn.curie.fr/downloads.html) in BioPAX and PNG formats. The module composition of ACSN is available as GMT files. The description of visualization and analysis functionality, including case studies, tutorial, guide and online live examples, are available from https://acsn.curie.fr/documentation.html. The Java code used to assemble the ACSN map and convert it into the NaviCell format is a part of BiNoM Cytoscape plugin and available at http://binom.curie.fr.An introduction into the features of ACSN, video tutorial, downloadable user guide and documentation about ACSN database are available on the ACSN web page  as well as to its particular modification  . Each en78 The score reflects an average distance in the PPI graph between all proteins participating in the reaction . Thus, if all proteins participating in a reaction interacts directly  in the PPI network then this reaction obtains the highest score (FUNC=5). If in the reaction where phosphorylation of protein A is catalysed by protein B, and A and B are separated in PPI graph by 2  edges, then the reaction obtains the functional proximity score FUNC=4 . If two reaction participants are not connected in the PPI network, then FUNC=0. The functional proximity is computed using BiNoM Cytoscape plugin.40 Both scores are visualized in the ‘Confidence' section of the ACSN annotations as a coloured ‘five-star' diagram  to 5 (high confidence). The reference score (REF) indicates both the number and the ‘weight' associated with publications found in the annotation of a given reaction, with weight equal to 1 point for an original publication and 3 points for a review article. For example, a reaction annotated by two reviews will obtain the highest reference score REF=5. The functional proximity score (FUNC) is computed based on the external network of protein–protein interactions (PPI), Human Protein Reference Database, which contains both experimental and literature-based curated interaction data. diagram . Same scEach entity annotation is represented as a post in the web-blog providing a possibility of communication between map users and map managers. Any user can submit comments in the form of hyperlinked text together with files of any type, if needed .42 The frequency of a gene mutation is represented as a ratio of samples carrying a mutation over the number of samples tested in each data set.To illustrate the coverage of gene mutation frequencies over the ACSN maps, the information about mutations in breast and lung cancers was obtained from the COSMIC database.79 and then grouped into the four BC subtypes:80 Basal-like (79 samples), Her2-positive (32 samples), Luminal A (385 samples), and Luminal B (118 samples). Each gene was assigned a score using median expression level across samples of the same subgroup, for ACSN map staining. Each functional ACSN module was attributed a score based on the average score of its genes. This score was visualized using BiNoM.40For mRNA expression analysis, normalized RNASeq data for 745 breast tumours and 100 healthy samples were obtained from TCGA database. Raw count data were first normalized using the Upper Quartile method43 About 130 phosphorylated protein forms from the data set were found on the ACSN maps. If ACSN contained only a generic representation of a ‘phosphorylated' protein, then all residue frequencies related to this protein were summed up. Visualization of data was performed using BiNoM.40For phosphoproteomic analysis, the phosphorylation data for a cohort of 234 lung tumour samples were obtained from PhosphoSitePlus database.et al.36). NaviCell includes a panel of tools for visualization and analysis of high-throughput data (43 using Python or R languages.ACSN uses NaviCell environment to represent the content of the ACSN map online (Kuperstein put data . The curP-values of intersections of a given gene list with ACSN modules (see In ACSN, users have the possibility to perform the most frequently used types of functional data analysis, which exploit the content of ACSN pathway database, such as functional enrichment of ACSN modules in a gene list. A hypergeometric test can be performed to compute the ules see .