Motivation. Predominant pathway analysis approaches treat pathways as collections of individual genes and consider all pathway members as equally informative. As a result, at times spurious and misleading pathways are inappropriately identified as statistically significant, solely due to components that they share with the more relevant pathways.Results. We introduce the concept of Pathway Gene-Pair Signatures (Pathway-GPS) as pairs of genes that, as a combination, are specific to a single pathway. We devised and implemented a novel approach to pathway analysis, Signature Over-representation Analysis (SIGORA), which focuses on the statistically significant enrichment of Pathway-GPS in a user-specified gene list of interest. In a comparative evaluation of several published datasets, SIGORA outperformed traditional methods by delivering biologically more plausible and relevant results.Availability. An efficient implementation of SIGORA, as an R package with precompiled GPS data for several human and mouse pathway repositories is available for download from http://sigora.googlecode.com/svn/. Pathway analysis identifies biological pathways that are statistically enriched in a given dataset and plays a crucial role in the interpretation of high-throughput experimental datasets including gene or protein expression profiles  and genoCurrently, two types of pathway analysis methods are widely used: Over-representation Analysis (ORA) methods . We describe this approach and demonstrate how SIGORA significantly reduces the identification of spurious pathways in analyses of simulated and real biological datasets.Here, we report a novel approach to address this problem, which involves the identification of statistically over-represented Pathway Gene-Pair Signatures (Pathway-GPS) . The use of such gene pairs is also motivated by the data in Pathway-GPS). Subsequently, in an online phase, the method identifies the statistical over-representation of such Signatures in a user-specified gene list using an adapted version of the hypergeometric test.As illustrated in re-used for pathway analysis of any user-specified gene lists.Given a pathway repository , for each gene-pair in a pathway, SIGORA investigates the co-appearance of the two genes in other pathways of the repository. A gene-pair that uniquely occurs in a single pathway is considered a Signature of that pathway and is assigned a weight. The weight of a Signature  quantifies the average commitment of the components of the GPS towards the common pathway, i.e., the weight scores the reliability of the Signature as evidence for the associated pathway. For hierarchically organized repositories (like REACTOME ), this pWhen presented with a gene list of interest , SIGORA determines which of the pairs from its (pre-compiled) Signature repository can be reconstructed from the genes in the list. A Signature is considered “present” only if both of its constituent genes are found in the user-specified query list. This inherently leads to the selection of the more relevant roles of a gene in the experimental context, as SIGORA relies on the status of the other genes in the pathway for the reconstruction of the Signatures. For each pathway, the weights of present Signatures are summed up and hypergeometric probabilities are used to assess the statistical significance of the observed Signature sets.B =  with two distinct sets of nodes (Vg: gene nodes and Vp: pathway nodes) where the edges in E connect the genes to the pathways and signify the annotation of a gene in a particular pathway (Pathway Unique Genes (PUGs)’).A pathway database/repository contains (at least) two types of entities: pathways and genes. This can be represented by a bipartite graph (or bipartite network)  pathway . In thisB can be manipulated and transformed. A weighted one-mode projection  with only one type of nodes (only gene nodes Vg), where two genes are connected by an edge in E′ if (and only if) they are co-annotated in one or more pathways, and each edge is associated with a weight (in W) which signifies the number of pathways that are shared between the two incident genes . Two genes connected by an edge of weight one in Proj are, as a combination, unique to a single pathway. We call such pairs Pathway Gene-Pair Signatures (Pathway-GPS) of that pathway. The process of identifying PUGs and Pathway-GPS for all pathways in a given repository is termed the ‘Signature Transformation’ of the repository.Using the igraph  package ojection  of B intn-tuples (with n > 2) of genes that co-occur in a single pathway P as signatures of P. The possible benefits of such extensions, however, do not currently seem to justify the associated complications to the computational and methodological framework. This point is further discussed in the Hypothetically, one could go beyond gene-pairs and also consider As, by definition, each Pathway-GPS is uniquely associated with a single pathway, identifying such Signatures in a gene list of interest  can serve as an indicator of the activation/perturbation of the associated pathway. Each pathway can  have multiple possible Pathway-GPS, and (as discussed below) the method does not rely on the observation of an individual GPS but rather on the statistical over-representation of multiple GPS in comparison to the expected proportion.considered individually – can be a member of several distinct pathways.Yet before the over-representation of GPS can be used for the identification of relevant pathways, we need to emphasize that different GPS vary in their reliability as indicators of a particular pathway. This is due to the fact that, while the two genes comprising a Pathway-GPS can only be co-annotated in a single pathway, each of the two genes – g1, g2) for a pathway P, where g1 is annotated in i pathways and g2 is in j pathways. Intuitively, a GPS that consists of two PUGs  is a more appealing signature than a GPS that consists of two ‘multifunctional’ genes , where the simultaneous observation of the two genes might be due to other factors .Consider GPS of the form  be a GPS associated with a pathway P and let i and j be the number of individual pathways annotations of g1 and g2, respectively. The weight of the GPS is To address this issue, a weight is assigned to each GPS to quantify its reliability as an indicator of its associated pathway. Let  genes, and m the length of the query list . In contrast to these traditional approaches, however, the parameters of the hypergeometric function in SIGORA are calculated as sums of GPS weights rather than frequency statistics of individual gene annotations. The Signature ORA parameters for p-value calculations are summarized in Analogous to traditional  ORA (IG-ORA) methods, the distribution function of hypergeometric probabilities is used to calculate strictly speaking – hypergeometric probabilities are only defined on natural numbers while the sums of GPS weights are positive floating point values, which is why the table refers to rounded values of the sums (to the closest integer). Theoretically, this rounding could lead to a ‘blurring of the weights for pathways with few GPS’, however, in practice this issue hardly materializes, as such pathways are very rare . In tests with simulated and real biological data sets, different choices of the rounding strategy  did not substantially affect the significance or the rank order of the identified pathways. As an aside, in the widely popular statistical framework R, the phyper function (which computes the distribution function of the hypergeometric distribution) does accept non-integer (floating point) parameters and handles such input by applying the following rounding strategy: the number of successes is rounded down, all remaining parameters are rounded to the closest integer value.Note that – N), and the success states (n) by providing a list of assayed genes (background). Furthermore, all PUGs are by default considered to represent a GPS of weight 1 (as a combination of the PUG with itself), but the user has the option to restrict the analysis to pairs of genuinely distinct genes.In our implementation, the user has the option to restrict the GPS-sets for the universe . It is, however, relatively easy to change the MTC procedure, if a user prefers to explore other adjustment methods (see the implementation section).Undertaking pathway analysis generally involves a large numbers of significance tests. As testing a multitude of hypotheses will inevitably lead to p-values become by several orders of magnitude smaller than those observed in a typical IG-ORA analysis.The choice of a reasonable cut-off threshold for statistical significance of the hypergeometric test results is an open methodological question in IG-ORA. In practice, values smaller than 0.1 or 0.05 after correction for multiple testing are commonly considered significant. Shifting the perspective from individual genes to the weighted Gene-Pair Signatures brings an additional challenge: as the size of the universe for the hypergeometric test dramatically increases (we move from a few thousand genes to up to a few hundred thousand weighted gene-pairs), the calculated p-value up to this value. The user can also export the entire results table  and the evidence .Based on our experience with simulated and biological datasets, we recommend a significance threshold of 0.001 after MTC (by Bonferroni). In the implementation, the default output of the analysis is the ranked list of pathways that achieve a corrected biological perspective. However, in some cases, there are additional semantic reasons for sharing of components among pathways. In particular, some repositories  are orgREACTOME .Signature. This would have the undesirable effect that all child pathways on the lower levels of the hierarchy would be left without any Signatures at all in the offline stage and hence be undetectable by SIGORA in the online stage.The hierarchical nature of such repositories poses a special challenge for our method. As any gene-pairs from a child category also co-occur in the parent pathway, they would thus be excluded from being identified as a possible To address this issue, we deploy the following iterative top-down strategy in the offline stage (Signature Transformation):1.Set level = 1. Compile the repository Signatures as described for the non-hierarchical case. Assign the compiled signatures to level 1.2.Remove all pathways from the top level of the hierarchy, increase the level and recompile Signatures for the remaining pathways. Assign the GPS to the current level. Iterate this step until no further hierarchical levels can be removed.online stage (the identification of significantly over-represented Signatures in a user-specified gene list) the user can specify how many levels of the hierarchy should be considered in the analysis. Any GPS (and any pathways) that are deeper down the hierarchy  are left out of the universe . If the user (for instance) asks for analysis up to the second level, then only GPS from levels one and two are considered.Elim  is similar to a combination of the Elim , and TreeeHugger  algorithV‖: number of nodes in the network (number of genes + number of pathways), d the average degree of nodes, ‖E‖ the number of edges in the bipartite network.The complexity of the transformation step (in an implementation based on the outlined bipartite network interpretation of gene-pathway membership) is dominated by the complexity of the bipartite projection, which is given as: For most inputs, the online phase  completes within 15 s on a standard laptop .http://sigora.googlecode.com/svn/. The following highlights the most important steps in a typical work-follow in R (requires R version ≥2.10):SIGORA is implemented as an R package. The package and a detailed manual are available for download from ## install and load the downloaded package >install.packages >library(‘sigora’) ##  ## import the query list from a file (assuming the list is given as Ensembl gene IDs): >myquerylist <-ens_converter) ## Alternatively, if the file consists of Entrez gene IDs >myquerylist <-entrez_converter(scan(‘myfile.txt’)) ## perform signature over-representation analysis, using KEGG GPS >sigs ## multiple testing correction is done by Bonferroni and FDRs are also provided. In order to add Hommel’s method: >cbind) ## export the results into a file >export_results ## help  >help(sigs) ## the following demo is also available >demo(sigora)DAVID  method  and theed, GSEA , GSEA_AF(GSEA_AF ) are GSE) method .DAVID, gProfileR, GSEA_PRERANKED for the simulation study, as these tools (like SIGORA) can be run on any pre-selected gene list and in ‘batch mode’. GSEA and Appearance frequency modulated GSEA (AF) are limited to particular experimental designs and have specific input data format requirements, and are used here only in the analysis of three biological dataset for which data in the required input format was available. InnateDB is used as a reference point in evaluation of the biological datasets, because SIGORA’s GPS are based on pathway annotation data as present in InnateDB. The rationale for selecting GSEA is its popularity; while AF was chosen because it attempts to address similar issues as SIGORA.Among the methods listed above, we use A short summary of the relevant characteristics of each of these methods is given below.InnateDB’s pathway analysis interface provides traditional IG-ORA using the standard hypergeometric test. Its recommended MTC method is Benjamini–Hochberg. The pathway GPS in SIGORA’s current implementation are calculated using the pathway annotation as present in the latest release of InnateDB. In other words, any observed differences in analysis results between SIGORA and InnateDB are solely due to the differences between Signature-over-representation and individual-gene over-representation, and there are no additional confounding issues regarding the gene identifier mapping or different update status of the repositories across tools. We compare InnateDB to SIGORA using three biological datasets.p-values are corrected by default using a unique multiple testing correction method (MTC), called the Set Counts and Sizes (SCS) procedure, which is analytically derived from extensive simulation experiments and purports to account for “the actual structure behind functional annotations”. In other words, issues relating to the overlapping structures within annotation repositories are believed to be addressed indirectly and implicitly, as a special case of MTC. We use gProfileR in the simulation experiment. For completeness, we also list gProfileR’s results on the three biological datasets evaluated here; however, some of the pathways listed by gProfileR are very recent additions to the KEGG repository that are as yet not available in other tools, including our current implementation of SIGORA.gProfileR is the R package associated with the web-server of same name. Like InnateDB, it deploys a traditional individual gene over-representation based method using the standard hypergeometric test. In gProfileR, p-value that is designed to raise the bar for statistical significance of smaller pathways. This avoids situations in which observation of only a few genes from a small pathway would make it equally (or even more) significant than observing dozens of genes from a larger pathways. The EASE Score is generally more conservative than the Fisher Exact p-values.DAVID provides individual-gene pathway analysis over-representation analysis using the EASE Score, a modified Fisher Exact functional annotation charts implement a traditional individual gene over-representation based method that treats all genes equally. DAVID has also introduced the concept of functional annotations clusters that are motivated by the idea that rather than focusing on significance of individual pathways, the true nature of a phenotype should be examined by considering the overall emerging picture of interrelated pathways. In some situations, clusters of interrelated pathways can be considered collectively significant while some (or most) of the individual pathways in those clusters might fall slightly beyond the significance threshold. Although this is undoubtedly a sensible statement, the measure of interrelatedness used in DAVID’s functional clusters is in diametrical contrast to the reasoning behind SIGORA: DAVID’s authors postulate that similar pathways tend to contain similar gene members. In DAVID’s functional annotation charts, the more common genes annotations share, the higher chance they will be grouped together as interrelated pathways, and the better the chances of the emerging cluster to become (collectively) significant.Apart from this modification, DAVID’s For the simulation experiment, we will focus on DAVID’s functional charts. In our evaluation of analysis results on biological datasets, we will also briefly exemplify the pitfalls of DAVID’s Functional clusters.p-value of differential expression). The p-values are in essence derived from a contingency table.The three methods discussed above  are over-representation based methods that (like SIGORA) operate on a pre-filtered list of genes of interest (query list). The list of genes of interest is often determined using a (combination of) threshold(s)  between the two conditions. This ranked gene list is then used to assign a normalized enrichment score (NES) – defined as the maximum deviation of a running sum statistic from zero, adjusted for the number of genes in the pathway – to each pathway. The statistical significance of the NES is determined by sample permutation .We compare GSEA and SIGORA in the analysis of three biological datasets. Some of the observed differences in the results of GSEA analysis to SIGORA are inevitably due to the fundamental differences between ORA and GSEA methods. In particular, regardless of their biological relevance to the examined dataset (or lack thereof), the significance of some of pathways observed by GSEA is due to ‘subtle but coordinated changes’ in expression levels of genes that are not in the list of differentially expressed genes.This issue equally applies to the two remaining methods, GSEA-PRERANKED and AF, which are described below.http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html). Hence, the recommended threshold for statistical significance is different (0.05 instead of 0.25). We compare GSEA-PRERANKED to SIGORA in analysis of simulation datasets. Like standard GSEA, GSEA-PRERANKED does not provide a mechanism for dealing with shared components of pathways.As the name suggests, this is a variant of GSEA where the input format is not an expression matrix, but a pre-ranked list of genes. Accordingly, as there is no sample information available, the statistical significance is derived from gene set permutation instead of sample permutations. Technically, applying GSEA and GSEA-PRERANKED to the same dataset can lead to identical NES, but very different FDRs , AF is the most similar method to SIGORA among all methods compared here. AF assigns weights to individual genes based on number of associated pathways and performs a GSEA analysis. Methodologically, AF inherits most of GSEA’s characteristics and is quite distinct from SIGORA. We compare AF and SIGORA in the analysis of three biological datasets.n pathways are chosen at random and a fraction  of genes in each selected pathway are marked as differentially expressed (DE). The restriction to 175 common pathways is intended to reduce the effects of diverging update-status across analysis tools. The list of DE genes from five selected pathways is used as a query list for SIGORA, gProfiler and DAVID. To create an input list for GSEA_preranked, a score of 2 is assigned to the selected DE genes and a score of 1 to all remaining human genes. This procedure is repeated 1,000 times at fixed values for alpha and n.As a preliminary measure to quantify the effect of shared components on the number of spurious pathways, we conducted a simulation experiment over 1,000s of simulated gene lists that are created by applying the following procedure: From a set of 175 human KEGG human pathways that are in the repository of all four compared tools , GSE25001, GSE11199, GSE11199.We further compare each of the different pathway analysis tools by examining their results when applied to three different gene expression datasets. These datasets incorporate the results of microarray studies investigating the host response to a parasite infection , a viral infection (Dengue Fever), and a bacterial infection (Tuberculosis) . We map a priori which are the significant pathways. The simulated input data was created by randomly choosing five KEGG pathways and selecting a fraction  of the genes in each of these pathways as being “differentially expressed”. Each of the methods was then applied to the selected gene list to determine the statistically significant pathways  applied to simulated data, where we know If the sharing of genes between different pathways was not a factor, we would expect that each method should identify only the five preselected pathways as significant. As can be seen in The Recall and Precision metrics in the third and fourth columns of Finally, note that although this analysis is based on recommended significance thresholds in each method, Precision, Recall and F1 score are all dependent on the – ultimately arbitrary – choices of significance thresholds. The last column in p-values and highlighted pathways obtained by each method can be found in the In addition to the simulated data, we also compared SIGORA to five different methods  applied to real biological data, in this case three different gene expression datasets. Full details of the input genes, GSE11199) which measured the host transcriptional response in human macrophages infected with Mycobacterium tuberculosis  applied to a gene expression dataset  contained 16 pathways, 12 of which are different cancer subtypes  in analysis of this dataset.berghei ANKA (NCBI GEO: GSE7814)  dataset, comparing the whole-brain transcriptional responses of genetically susceptible (C57BL/6) and resistant (BALB/c) inbred mouse strains 6 days after infection with Plasmodium GSE7814) . We fistGSE7814) . We use PPAR signaling pathway (discussed further below).Similar to the previous example, the number of KEGG pathways that different methods identified as statistically significantly enriched in this dataset varied widely: AF identified 59 pathways (out of 185 in its repository), while DAVID highlighted just two pathways. SIGORA identified 14 pathways as significant, 13 of which were also reported by at least two other methods . The remAside from this big-picture view, the strong changes in the rank orders of the following individual pathways are also worth mentioning:This pathway is the 6th ranked pathway in SIGORA’s results. Three additional tools  also identify this pathway as significant, but only at considerably lower ranks . Complement and coagulation pathways have been shown to be critically involved in the development of ECM .This pathway is the 7th ranked pathway in SIGORA’s results, the 23rd ranked pathway in GSEA and the 44th ranked pathway in AF. The remaining methods do not identify this pathway as statistically significant. Polymorphonuclear leukocyte recruitment has been shown to be responsible for increased permeability of the blood–brain-barrier, and is strongly associated with fatality rates in ECM .AF, at position 47. Targeting of PPAR is currently being explored as a novel adjunctive therapy for cerebral malaria  is the most significant pathway in InnateDB’s results, and the second highest ranked pathway in gProfileR’s results, but is not significant in SIGORA’s results. Similarly, GSEA and AF both identify “Prion diseases” as significant  and again, this pathway is not significant in SIGORA’s results. Other examples include: “Small cell lung cancer” , “Viral myocarditis” , “Type I diabetes mellitus”   .“Antigen processing and presentation”, “Viral myocarditis”, “Allograft rejection”, “Graft-versus-host disease”, “Type I diabetes mellitus” and “Autoimmune thyroid disease” together  groups together . All of GSE25001) (n = 72).As a third evaluation set, we re-examined a list of 483 up-regulated genes in the whole blood transcriptome of patients infected with dengue virus (NCBI GEO: SE25001) . More spStaphylococcus aureus infection” (the second ranked pathway in InnateDB results) and the “Prion Disease” pathway (identified by both InnateDB and AF). These two pathways share components with the complement pathway  is Bayesian Pathway Analysis, BPA . BPA traAn early example of the former approach  is impact-analysis . Impact BRCA1 in More recently, it has been proposed to add an appearance frequency based parameter to the statistical framework of GSEA. This additional parameter is intended to weaken the contribution of genes with multiple pathway memberships to the statistical significance of all of their associated pathways . While tG to the measured success (parameter k in G form a GPS).Here we introduced the concept of Pathway-GPS, as genes or gene-pairs that (as a combination) are specific to a single pathway, and we described Signature Over-representation Analysis (SIGORA) as a novel (and comparably efficient) approach to pathway analysis. SIGORA uses Pathway-GPS to bridge the gap between the context sensitive and collaborative nature of biological processes on one side and the universal and discrete statistical framework of over-representation analysis on the other side. Although each Signature’s weight is fixed in advance, the net contribution of an individual gene In contrast to the GSEA-based solutions, SIGORA inherits the versatility of the ORA statistical framework and is applicable to lists of genes of interest obtained in any type of high throughput experimental set-up  without the need for adaptation of the computational method. This is especially notable in situations where ranking of the entire dataset by a single biological parameter (as required by GSEA) is not feasible (see Signatures’) of pathways. To our knowledge, this is the first over-representation based method to do so.This paper highlights the level of component sharing between pathways and demonstrates how this can lead to misleading/spurious results in current pathway analysis approaches that treat all pathway members as equally informative. Here we introduce a novel approach that accounts for the overlapping structure of pathway annotation by focusing on unique features . In the first iteration, only signatures for the outer-most level of the hierarchy(P5) are determined. The GPS for the higher levels are the ones associated with the less general pathways, and are only visible after removal of the more general terms. For example, the GPS of P1, P2 and P3 are only visible at level 3, after removal of P5 and P4.Click here for additional data file.10.7717/peerj.229/supp-3Table S1The worksheet contains 9 tabs: the list of the input genes, the full results of each of the six compared analysis tools for this data-set, a summarizing 6way-comparison sheet of the ranks of pathways that were identified as significant by at least one method, and an additional tab for DAVID’s functional clusters.Click here for additional data file.10.7717/peerj.229/supp-4Table S2The worksheet contains 9 tabs: the list of the input genes (for the ORA-based methods), the full results of each of the six compared analysis tools for this data-set, a summarizing 6way-comparison sheet of the ranks of pathways that were identified as significant by at least one method, and an additional tab for DAVID’s functional clusters.Click here for additional data file.10.7717/peerj.229/supp-5Table S3The worksheet contains 8 tabs: the list of the input genes, the full results of each of the six compared analysis tools for this data-set, a summarizing 6 way-comparison sheet of the ranks of pathways that were identified as significant by at least one method.Click here for additional data file.


Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from  A major challenge in systems biology is the integration of different types of knowledge about biological processes in order to gain insight into the functioning of the organism as a whole system. Experimental data can include detailed molecular quantification experiments and records of phenotypic traits. Much of the time, knowledge such as pathway information is incomplete and is described at varying levels of detail in published records. Experimental data are usually specific to the question they were designed to address, limiting their power to shed light on more general properties of the system or to address different questions. In many cases the choice of the biological system, limits the type of experiments that can be performed, therefore relying on computational methods to make additional inferences. As we accumulate more knowledge about biological processes, it is important to review past experiments in the light of new knowledge, in order to extract further understanding of the system. An additional challenge is to bridge the gap between knowledge of biological pathways, genetic perturbations with their effects on gene expression and the resulting phenotypes, in order to gain a more thorough understanding of biological mechanisms. In this study, we developed a method for integrating pathway with gene expression and phenotypic data sets, making inferences from them and checking their consistency. These include information on changes in gene expression resulting from different gene mutants, biological signalling pathway information at the protein level in the form of activation and inhibition between proteins, and results of phenotypic analyses of the mutant animals.Caenorhabditis elegans, the fruit fly Drosophila melanogaster and the mouse Mus musculus. The insulin/insulin-like growth factor signalling (IIS) pathway and the target of rapamycin (TOR) pathway have been found to be major determinants of lifespan in these organisms. Importantly, as well as extending lifespan through the forkhead transcription factor foxo, reduction of IIS activity increases health during ageing. Initial experimental work showed that worm mutants for daf-2, the single worm IIS receptor, live twice as long as controls Ageing is the process of decline in organismal fitness over time. This process differs significantly across species, as well as between individuals of the same species, leading to a variety of different age-related phenotypes and lifespans. The molecular basis of lifespan has been under intense investigation in recent years, using laboratory model organisms such as yeast, the nematode worm et alet alProtein abundance is predominantly controlled at the level of translation in vivo experiments on ageing of the fruit fly Drosophila melanogaster, where direct experimental measurements of signal transduction by means of ligand stimulations are difficult to perform. Previous work on the inference of signalling pathway components from gene expression data sets has focused on inferring modulators of specific transcription factors by correlating mRNA expression profiles across large sets of microarray experiments Here we develop a qualitative method that enables us to infer the impact of differential gene expression on signalling pathways and attempts to distinguish the effects of the primary experimental intervention from those caused by feedback. We achieve this by developing a comprehensive model of the pathways involved and a rule-based logical theory that integrates results from high-throughput gene expression assays with previous knowledge of the pathway. In this study we have used such data sets from The modulation of small scale signalling pathways has previously been modelled by Ordinary Differential Equations (ODEs) that can represent chemical reactions. Networks of these equations can model biochemical processes in detail, provided there are appropriate experimental data sets to estimate the parameters of the model. This approach requires proteomic and phosphorylation experiments assessing the abundance of the different forms of the molecules so that the reaction parameters can be quantified. Although these models are very useful in describing specific reactions and small pathways et alet alArabidopsis thaliana using action languages on ASP et al, et al, Our approach uses Answer Set Programming (ASP), a methodology for declarative programming that supports logic-based inference, Drosophila melanogaster that manipulated the genes chico, Lnk, foxo and InR to produce a significant difference in lifespan between mutant and wild type strains. We analyse one experiment at a time and we also provide new methods for the comparisons of our inferences across experiments. The methods are implemented in a web-service, NetEffects, http://www.ebi.ac.uk/thornton-srv/software/NetEffects, allowing users to analyse their expression and longevity data using this approach, or to predict probable effects on longevity for mutations to any components of the pathway.In this study we constructed a model of the IIS signalling pathway including its interactions with the TOR pathway and developed a novel method for inferring the modulation of the pathways from large-scale gene expression data sets using data from different mutants affecting longevity. Our resulting rule-based theory enables the inference of effects in a large and complex pathway, implicitly checking experimental consistency, by using a single expression data set at a time. It is also possible to make a custom query of the pathway components the user wishes to perturb and predict the effects on signalling and the phenotype. By using qualitative data sets and an ASP solver, we avoid the computational limitations faced by other methods, and we are hence able to work with larger complex pathways that include feedback loops and make the methods available as a web-service. By using prior knowledge of the pathway connections, we overcome the requirement for a large number of experimental data sets for our inferences. Our analyses focus on experimental data sets from Drosophila. INR activation leads to the recruitment of the single Drosophila insulin receptor substrate CHICO and of the Drosophila homologue of human SH2B, called LNK. These proteins act in parallel and activate the phosphotidylinositol-3-kinase (PI3K) complex formed by proteins P60 and P110 The IIS signalling pathway and its interactions with the TOR pathway are shown in Drosophila MYC/DM, as well as signalling proteins, such as S6K.The insulin pathway interacts with other signalling pathways, such as the JNK kinase, SOS signalling and Wnt pathways. It is also highly interconnected with the target-of-rapamycin (TOR) pathway, mainly mediated through CHICO, PI3K and AKT1 in the IIS and the TSC-complex, L (LOBE), TOR-C2 and S6K in the TOR pathway. The core TOR pathway is triggered by low intracellular AMP levels, which activate the AMP kinase SNF1A, which in turn activates the TSC-complex. The complex inhibits RHEB, which then no longer activates the TOR-C1 complex. The TOR-C1 complex is a major signalling complex integrating various signals and activating and inactivating numerous proteins amongst which are transcription factors such as the Drosophila melanogaster. While we have tried to cover the two pathways and their interactions as completely as possible, the diagram is undoubtedly incomplete because of the limits of biological knowledge.The complex network of these interactions has been manually compiled from pathway diagrams of the KEGG database http://www.yworks.com). This is an XML based format, which allows a formal interpretation, while at the same time defining a graphical layout. It thus enables a graphical representation, which is immediately understandable to biological readers and at the same time allows computational use of the same file. The diagram shows activation and inhibition relationships of the protein components of the signalling network, thus representing the activity wiring. Metabolic, transcriptional or translational influences are not considered. For tractability, we do not include any kinetic data or location-specific information in the pathway. These compromises in complexity are essential to allow the examination of such a large and complex signalling network. However, our approach is far more detailed than pure protein-protein-interaction studies, which lack directionality and connection types (i.e. induces/inhibits).The diagram has been created in GraphML using the program yEd , Lnk . All paths are available in At this stage we used the phenotype recorded by the survival analyses that accompanied each experiment, in order to classify the inferred paths into ‘primary’ or ‘secondary’ effects. We call primary effects those paths that support the observed longevity phenotype and involve the primary experimental mutation. Secondary effects refer to paths that are triggered by differential expression of genes, other than the primary mutant. These are further classified into negative and positive feedback, where effects of negative feedback contradict the observed longevity phenotype, whereas positive feedback effects support the observed phenotype.By comparing the different paths produced within and across the different experiments, we observed that many non-identical signals contained similar “segments” of paths that could potentially reveal important recurrent routes of activity in the signalling pathway. For example, each one of the long-lived mutants up-regulates different insulin-like peptides (ILPs) that in turn can lead to changes in longevity as mediated by FOXO . When cowww.cpan.org). Every path is compared to each other and the longest common sub-string is found for each comparison. The occurrences of each sub-string (sub-path) are counted and returned. The algorithm takes into account the sequence of nodes (pathway components), as well as their status (activated or inactivated). The longest common sub-path finder program is available for download from http://code.google.com/p/longest-common-sub-path.In order to be able to identify the most common sub-paths within or across experiments, we developed a “longest common sub-path finder”. A sub-path is defined as a sequence of ‘activates’/‘inactivates’ relations that is part of a path that has a differentially expressed or perturbed component as a starting point and longevity as an end point. The longest common sub-path finder takes as input all the different paths produced by the analysis of the results from ASP and outputs a list of common sub-paths, together with their frequency in the data set. The algorithm uses an existing solution of the ‘longest common string’ program, implemented in the Perl package  and various players of the TOR pathway .Looking at the signalling sub-paths we inferred for each experiment, we also observed that the TOR-C2 inhibition by the down-regulation of Overall, these effects show a strong negative feedback that is consistent amongst long-lived mutants and opposite between short- and long-lived mutants. This type of negative feedback leads to homeostasis, in an attempt to reverse the effects of the primary mutations.Lnk mutant, we identified a novel hypothesis that involves a possible feedback (secondary effect) route from the up-regulation of Atg1 to lifespan reduction via S6K inactivation and CHICO activation, uncovering a possible cross-talk between IIS and TOR signalling . A possible hypothesis is that they do not play a primary role in lifespan and there is another process regulating them that is currently not included in our pathway model. Another example of missing information is gene L, also known as lobe, which is involved in a cross-talk between AKT1 in the IIS pathway and the TOR pathway, TOR-C1 complex. In our examples, we observed L to be up-regulated in the InR mutant, where it is suggested to be involved in a secondary effect by providing feedback to AKT1 through the TOR complex. According to its functional description in Flybase (ID: FBgn0001332), it also appears to be implicated in the regulation of the Wnt signalling and of the JNK cascade, although there is no evidence of the underlying mechanism.We observed that InR experiment we also observed up-regulation of Tak1, a component of the JNK pathway. This pathway is ubiquitous, in being involved in many different biological processes. It has been implicated in lifespan through FOXO activation Pk61C up-regulation and the hpo and Pten down-regulations in the foxo knock-out experiment. All three of them lead to an inactivation of FOXO according to our current pathway knowledge, indicating possible points for further research.In the p110, the kinase subunit of the PI3K complex, but we noticed that it is over-expressed in two different lifespan-extending experiments. We believe that this highlights the need for further investigations on this kinase and its role in ageing via insulin pathway perturbations.We could not identify any direct paths to FOXO and longevity from in vivo experiments, although it is important in order to be able to uncover the complexities of ageing. A strength of our approach is that it enables us to investigate data sets from many experiments in a fast, simple and rigorous way, uncover detailed paths per experiment, as well as consistent biological trends across experiments, such as the homeostatic behaviour observed across the different mutants. Such insights cannot be produced by standard functional analysis methods for gene expression data sets. Moreover, the application of a computationally efficient and scalable method such as ASP enables the development of web-services, making such complex analyses easier to perform and accessible to experimental scientists.Our method successfully combines knowledge of the IIS and TOR pathways with gene expression data sets and longevity assays, to make inferences about the modulation of the signals in different mutants of the IIS pathway and explain the observed phenotypes. Such insight in the modulation of signal transduction cannot routinely be gained experimentally from such foxo mutant. Although the majority of our results are consistent with previous knowledge and we are still able to generate interesting hypotheses for further investigations, we can not assign roles or causes for the differential expression of genes that trigger positive feedback with respect to FOXO-mediated longevity.An assumption that our pathway model makes is that longevity is solely a result of FOXO activation via the insulin pathway, which is primarily a nutrient sensing pathway coordinating appropriate responses to nutrient availability. Although extensive, our pathway model ignores further complexities of the interactions with other pathways or other determinants of lifespan. By including components of the TOR pathway in our model, especially where this cross-talks with the IIS pathway, we were able to identify how TOR could provide feedback into the IIS pathway under the conditions of certain IIS perturbations. However, the TOR pathway has also been implicated in lifespan possibly through different routes, as demonstrated in other organisms Lnk mutant, can be investigated by further experiments. For example, western-blot analysis can be performed in time-series experiments after pathway induction by ligand stimulation and their impact on longevity can then be studied by dynamic simulations. However, such experiments, validating our hypotheses, would benefit from the use of different biological systems, such as cell line models, instead of in vivo whole-organism models that have been used for the gene expression and lifespan assays.By formulating our problem as a logic-based theory, we are able to use the efficient search facilities of Answer Set Programming and can therefore explore larger pathways. Secondly, we take advantage of the rich and expressive language of formal logic to combine different types of data sets and reason about them in a biologically meaningful way, by formalising in a rule-based representation the implicit reasoning of biologists, when considering such data sets. This largely qualitative method is very useful for combining different, large data sets, testing their consistency with known phenotypes, predicting phenotypes according to the pathway model and generating hypotheses that could be investigated further by more detailed experimental and quantitative computational approaches. Moreover, the ASP framework and the available solvers generate all possible solutions (answer sets) to a problem, given the input data at hand. Specific signals involving smaller segments of the pathway, such as the feedback mediated by ATG1, S6K and CHICO in the foxo knock out, the only short-lived mutant, we generally observe opposite effects on differential expression of the insulin pathway components. For example, in all long-lived mutants we observe at least one Ilp per experiment being up-regulated, in order to stimulate the pathway and lead to FOXO inactivation by facilitating its retention in the nucleus. Conversely, in the short-lived experiment we observe down-regulation of Ilps. Similarly, the inhibitor of ILP2 and ILP5, IMPL2, is down-regulated in the Lnk mutant and up-regulated in the foxo mutant.In the majority of cases, primary effects reflect the impact of the perturbation on lifespan, whereas secondary effects seem to be parts of feedback mechanisms that lead to homeostasis, attempting to “correct” the effect of the experimental perturbation. When comparing the long-lived mutants against the et alMoreover, studying the predicted signals in the different mutants associated with differences in longevity, we are able to generate hypotheses on how lifespan could be modulated within the pathway framework, not only per mutant, but also across mutants. Interesting questions for further investigations are (1) whether the primary and secondary effects, as presented here, represent opposing contributions to the overall longevity phenotype we observe in the survival studies and (2) whether it would be possible to quantify these effects and identify a relationship between each signal and difference in lifespan. Moreover, our current pathway model ignores the contribution of other pathways and processes, e.g. growth/autophagy, to lifespan by solely examining lifespan as a result of FOXO activation. It could be possible, in the future, to integrate methods that employ dynamic simulations, such as the one used by Dalle Pezze Our application is limited by the lack of kinetic information on the pathways and also by the experimental data sets, which contain mRNA levels, making the quantitative analyses of signalling pathways impossible. Moreover, we integrate pathway connections derived by different experimental methodologies, while the accuracies of these connections are ignored by the analyses, because they are sometimes unavailable or unsuitable for simulations. However, since we have been strict in only selecting connections that have been experimentally verified, our method gains in strength by the use of larger but qualitative pathway diagrams thus making their analysis possible in a qualitative fashion, as opposed to the computational difficulties faced by quantitative methods and discrete logic-based methods, to simulate larger pathways.et alIt would be an interesting exercise to test on a smaller example the performance of tools implementing Markov Logic Networks, e.g. Alchemy by Domingos The rule-based model itself is general and can be used to reason about different types of pathways, other than the IIS and TOR pathway and relate these to phenotypes other than longevity. In the future, we will enable the import of user-defined pathways to the web-service, so that users could relate their molecular data sets to their pathways of interest. It is important that for the inclusion of different types of pathway models we develop further the web-service, so that it is compatible with standard pathway formats, such as BioPAX and SBML.Our current pathway model aims to represent the topology of the interactions without including constraints on the conditions or tissues, where these interactions take place. Such information, once available in sufficient detail to have an impact on the analyses, could potentially be collected and included in the rule-based logic program. We anticipate that the modular nature of our model will enable it to expand and combine pathway processes at different levels of detail (e.g. undirected Protein-Protein Interaction Networks), as new information on tissues, granularity of pathway interactions becomes available. Currently, the longevity phenotype is considered in a discrete fashion as “long-lived”, “unchanged”, or “short-lived”. In the future, we aim to integrate the methods implemented in the web-service SurvCurv (manuscript in preparation) with the methods in NetEffects in order to achieve a more quantitative and full representation of longevity phenotypes.C.elegans and M.musculus, with the aim of performing cross-species data analyses to investigate how the predicted signals in equivalent mutants differ across species.As it stands, the pathway model and the ASP methods can be used to investigate the signalling effects on the IIS pathway by longevity experiments that involve exposure to environmental stress conditions or perturbations of other genes, not included in the IIS pathway. A requirement is that some of the components in the pathway model must be differentially expressed in these experiments, therefore implicating the IIS pathway in the response to the experimental condition. For example, these could include dietary restriction experiments or perturbation of other transcription factors that have also been shown to alter ageing. In the future we also aim to generalise the web-service to include the longevity-related pathways of other model organisms used in ageing research, such as Supplementary File S1All paths inferred per experiment.(XLS)Click here for additional data file.Supplementary File S2The developed logical theory (code).(TXT)Click here for additional data file.Supplementary File S3The longest common sub-paths per experiment.(XLS)Click here for additional data file.Supplementary File S4Analysed microarray data sets (LIMMA output).(XLS)Click here for additional data file.Supplementary File S5Supplementary Methods. Description of Answer Set Programming and the logical theory we developed.(PDF)Click here for additional data file.


Recently, a pathway-based approach has been developed to evaluate the cumulative contribution of the functionally related genes for genome-wide association studies (GWASs), which may help utilize GWAS data to a greater extent.In this study, we applied this approach for the GWAS of basal cell carcinoma (BCC) of the skin. We first conducted the BCC GWAS among 1,797 BCC cases and 5,197 controls in Caucasians with 740,760 genotyped SNPs. 115,688 SNPs were grouped into gene transcripts within 20 kb in distance and then into 174 Kyoto Encyclopedia of Genes and Genomes pathways, 205 BioCarta pathways, as well as two positive control gene sets (pigmentation gene set and BCC risk gene set). The association of each pathway with BCC risk was evaluated using the weighted Kolmogorov-Smirnov test. One thousand permutations were conducted to assess the significance.Both of the positive control gene sets reached pathway p-values<0.05. Four other pathways were also significantly associated with BCC risk: the heparan sulfate biosynthesis pathway , the mCalpain pathway , the Rho cell motility signaling pathway , and the nitric oxide pathway .We identified four pathways associated with BCC risk, which may offer new insights into the etiology of BCC upon further validation, and this approach may help identify potential biological pathways that might be missed by the standard GWAS approach. The cutoff of 10–200 genes was selected as a good balance for gene sets to reduce the multiple-testing issue and to avoid testing overly narrow or broad functional categories. We also designed two positive control gene sets, the pigmentation gene set and BCC risk gene set, to test this approach. We selected the genes based on information gathered by the previous GWAS studies on the pigmentation traits and BCC risk. The pigmentation gene set included We applied the method of Wang et al. Basic characteristics of the 1,797 BCC cases and 5,197 controls in our study are presented in Of the 740,760 genotyped SNPs, 366,550 SNPs located within 20 kb upstream or downstream of gene transcripts were assigned to the genes. Among them, 115,688 SNPs were ultimately assigned to 4,891 genes within the pre-defined pathways . A total of 174 KEGG pathways and 205 BioCarta pathways that contained between 10 and 200 eligible genes were used for the pathway analysis.Both of the positive control gene sets reached a nominal p-value<0.05 (p  =  0.001 for the pigmentation gene set and p  =  0.03 for the BCC risk gene set). As shown in Besides the two positive control gene sets, 10 out of the 174 KEGG pathways and eight out of the 205 BioCarta pathways reached a significance level of p<0.05, and four also reached a FDR of less than 0.5. Those were the heparan sulfate biosynthesis pathway , the mCalpain pathway , the Rho cell motility signaling pathway , and the nitric oxide pathway <0.05; the same was true for 14 out of 24 genes in the mCalpain pathway, 16 out of 32 genes in the Rho cell motility signaling pathway, and 16 out of 30 genes in the nitric oxide pathway. The representative SNP of each gene in the four pathways that had PBCC<0.01 are listed in Thirteen out of 19 genes in the heparan sulfate biosynthesis pathway had SNPs with p-value for marginal effect on BCC risk  in the Rho cell motility signaling pathway (32 genes); and 6 genes  in the nitric oxide pathway (30 genes). After excluding the 3 genes shared between the mCalpain pathway and the Rho cell motility signaling pathway, the association obviously attenuated for both pathways . The association remained similar after excluding the 6 genes shared between the mCalpain pathway (p  =  0.003 and FDR  =  0.12) and the nitric oxide pathway (p  =  0.04 and FDR  =  0.76). No other overlap was found among the remaining pathways. In addition, based on the concern that the case-control status of T2D and CHD may introduce potential bias to the associations between the SNPs and BCC risk, a sensitivity analysis was conducted by adjusting for the case-control status. As a result, no substantial change was detected for the associations before and after the adjustment.According to the pathway definition in the BioCarta database, there are some overlaps between the mCalpain pathway (24 genes) and the other two pathways: 3 genes  and m-calpain . One of the most carefully studied functions of the calpains is the regulation of integrin-mediated cell migration The mCalpain pathway ranked first among the 205 BioCarta pathways tested in this study (p  =  0.002 and FDR  =  0.18). Calpain is a class of CaThe other two pathways associated with BCC risk in the BioCarta database were the Rho cell motility signaling pathway and the nitric oxide pathway. After excluding the genes shared with the mCalpain pathway, the association for the Rho cell motility signaling pathway became non-significant, suggesting that the association was driven by the genes shared with the mCalpain pathway. There is evidence from previous study that calpain mediates integrin-induced signaling at a point upstream of Rho family members NOS3 polymorphisms and BCC risk in our study (representative p  =  0.01). A characteristic clinical feature of BCCs is a translucent papule or nodule with prominent telangiectasia, presumably an angiogenic response propagated by the growing skin cancer. A NOS-predominant BCC may potentially be able to attract a risk blood supply for continued growth and NOS (via NOS inhibitors) may be considered a therapeutic target.The association of the nitric oxide pathway remained significant after excluding the genes shared with the mCalpain pathway, which suggests an independent effect. Nitric oxide (NO) is a signaling molecule that plays a critical role in vasodilatation, neurotransmission and immunity, and has been shown to be cytostatic or cytotoxic for cancer cells As for the KEGG pathways, the heparan sulfate biosynthesis pathway was significantly associated with BCC risk (p  =  0.007 and FDR  =  0.35). Heparan sulfate (HS) is a glycosaminoglycan covalently attached to serine residues in a proteoglycan core protein. Heparan sulfate proteoglycans (HSPGs) have been known to play vital roles in every step of tumor progression: allowing cancer cells to proliferate, escape from immune response, invade neighboring tissues, and metastasize to distant sites trans by genetic variants that are physically distant from the structural gene There are some limitations to our approach. First, current knowledge on human gene functions is incomplete, and the pre-defined pathways cannot comprehensively represent the functionally related genes in the human genome. We used the KEGG and BioCarta pathways in this study, which represent relatively well-defined known biological pathways. To minimize multiple-testing concerns, we did not include some other pathway databases with more broad functional categories such as the Gene Ontology (GO) In conclusion, we applied a pathway-based approach to further explore the GWAS data of BCC and identified several pathways associated with BCC risk, which was missed in the traditional GWAS. These findings may suggest new insights into the etiology of BCC upon further replication. Functional studies are also warranted to characterize the biological functions of these pathways in BCC development.Table S1Characteristics of BCC cases and controls in this study.(XLS)Click here for additional data file.Table S2The representative SNP information for the two positive control pathways.(XLS)Click here for additional data file.Table S3Association results for the 174 KEGG pathways and 205 BioCarta pathways. (a) Association results for the 174 KEGG pathways; (b) Association results for the 205 BioCarta pathways.(XLS)Click here for additional data file.Table S4The representative SNP information for the four pathways with significant enrichment in the BCC GWAS.(XLS)Click here for additional data file.Methods S1Description of the GWAS on BCC.(DOC)Click here for additional data file.


With the growth of biological data in volume and heterogeneity, web search engines become key tools for researchers. However, general-purpose search engines are not specialized for the search of biological data.http://www.integromedb.org.Here, we present an approach at developing a biological web search engine based on the Semantic Web technologies and demonstrate its implementation for retrieving gene- and protein-centered knowledge. The engine is available at The IntegromeDB search engine allows scanning data on gene regulation, gene expression, protein-protein interactions, pathways, metagenomics, mutations, diseases, and other gene- and protein-related data that are automatically retrieved from publicly available databases and web pages using biological ontologies. To perfect the resource design and usability, we welcome and encourage community feedback. Diverse web resources and databases, such as UniProt , GenBankWeb search engines, such as Google, excel at finding a needle in a haystack: a single fact, a single definitive web page. Often, however, the user's objective is not to find a single fact, but to explore all available data concerning a specific genes or proteins. For example, a person diagnosed with diabetes might want to learn all known scientific data about genes associated with this disease. Or, for a researcher who wants to learn 'all about p53 gene/protein', it is much easier to get the information centralized in one place, instead of searching the whole Internet for this information. Even if the researcher knows exactly what to look for, for example, specific post-translational modifications in p53 protein that result in different types of cancer, he/she still might need to browse many different web resources in search of this information.http://www.integromedb.org, has been designed for browsing available data on gene regulation, gene expression, protein-protein interactions, pathways, metagenomics, mutations, diseases, and many other gene- and protein-related data. IntegromeDB web page is linked to BiologicalNetworks http://www.biologicalnetworks.org[http://integromedb.org/api.jsp provides access to the most features available through the web application and can be used by third-party users and resources. The batch search and retrieval options are also provided at the IntegromeDB web site, http://www.integromedb.org.Here, we propose a solution to the problem of searching biological data, specifically gene/protein-centered data, via semantic integration of data from a broad collection of molecular biology resources and presentation of these data on one web page for each searchable gene/protein. Data have been automatically integrated from more than a thousand databases listed in the Nucleic Acid Research database list , as wellworks.org, which iworks.org. Applicahttp://integromedb.org/download_rdf.jsp.The basic anatomy of the IntegromeDB data integration system that is behind the presented search engine is described in detail in . Brieflyhttp://www.ncbi.nlm.nih.gov/gene/?term=') is analyzed and further keywords will be extracted from the resulting pages. The iterative process is continued until either no new candidate keywords can be extracted or no more new web pages can be retrieved for a given resource.The major architectural blocks of the system are a web-crawler, archive server, data integration server, internal database, and ontological mediator Figures  and 1S1.The IntegromeDB content is available for more than 100,000 genes and proteins from 1252 organisms, more than 2 million experiments, 1000 pathways, 900,000 gene and protein records, and thousands of PubChem  records.Search on the IntegromeDB web page can be executed via keyword, wild card, or multiple word queries. Genes/proteins can be queried using either the gene name  ; PO - frequency of a term A in the ontology which the term A belongs to; PP - frequency of a term A in the document X, this value corresponds to the Lucene keyword search score [PT - frequency of the term A in different HTML tag fields of the web page (document) X; PR - PageRank [X. In the process of executing the query containing the term A, ICE retrieves the top one thousand ranked documents.where ch score ; PT - frPageRank  of the d'diabetes', or an abbreviation, like 'Leu', a sequence word 'ATGCTGGG' or 'MEPQSDVL', a number, or a combination of letters and numbers, like 'Hs.654481'. Further assume that for each word i a threshold frequency to be in the document is set at b(i). Now, for each document, we can compute a vector in which the i-th component is set to 1, if the value of relative frequency of the word i in the document is greater than b(i), or 0, otherwise. This binary vector is regarded as a fuzzy digital signature of the document and is built on the descriptive set of words. Vectors with 3 or less words above threshold are removed from further consideration. Low complexity words, such as 'ATGC', 'MEPQSDVL', or '0123456789', are considered as unique sets of symbols and are not considered in the vectors. The descriptive set of N words, which is defined for the whole database once it is updated, consists of the 'quality' words that cover nearly the maximum amount of documents thus that N is nearly minimal, where N is determined empirically. The 'quality' of a word is determined as relative stability of the corresponding component of the vector in similar documents. The threshold b(i) is chosen thus that the most of the documents have non-zero vectors. To estimate the similarity between two documents, the normalized Levenshtein distance [Documents, or web pages, that are found to be relevant to the query gene/protein are further grouped together by data sources Figure  and by sdistance  is used.e.g., Google). Data are grouped by data sources and similarity and sorted in each group by the relevance to the query gene/protein as is shown in Figures e.g., protein structures from PDB [IntegromeDB report page uses a two-dimensional newspaper-like layout Figure  rather tfrom PDB  and Wikifrom PDB ), and reFor example, for the query 'p53', the report page Figures  and 5 de'In this topic'. For example, for the 'p53' query, among the most relevant  resources were OMIM, UniProt, UniGene, GenBank, PubMed, Wikipedia, GeneCards, InterPro, p53.free.fr (mutation database), SYSTERS (protein families database), tp53.org. They were followed by (accessible via clicking the 'Show More Pages' button) EMBL/EBI, MGD, STRING, WikiGenes, Genetic Atlas, CancerIndex, ProteinAtlas, KEGG, UCSC genome Browser, HAGR (Human Ageing Genomic Resource), SwissProt, PharmGKB and others.For each gene/protein, information retrieved from web pages is clustered by data sources Figure . The useIn contrast to table data stored in listed and well-maintained databases, the tables published in the PubMed articles, as well as separate tables distributed across the web, are barely searchable by any search engine. Here, these data are integrated and become searchable using the same approach that is applied to the tables in the databases. Specifically, it concerns tabular data on gene regulatory regions, gene/protein interactions, and gene expression experiments.promoter_sequence. If the sequence is not found, the postprocessor will assign the sequence as a new attribute with the name as provided in the first cell of the second column, and the type of the attribute will be sequence. Figure To integrate the tables, IntegromeDB looks for web pages containing the HTML 'Table' tag and distinguishes relational tables (that are further parsed) from non-relational (that cannot be integrated), using a combination of in-house and statistical classifiers, e.g., calculating the ontology term in each column of the table; the classifiers have been trained on manually selected examples and use the information already integrated in IntegromeDB. If the columns containing significant percentage  of the object IDs from IntegromeDB can be found, the table will be further processed; otherwise, filtered out. For example, a two-column table containing gene names and promoter sequences will be identified as a relational table as the percentage of ontologically defined objects in the first (gene names) column is high. Data from the other column will be considered as gene attributes if types of data can be defined, or named; that is, the column contains a data label in the first row. Thus, if in the example table the first cell of the second column contains the word 'promoter' and the other cells, DNA sequences, the postprocessor will understand that data in this column are promoter sequences. It will therefore scan the upstream region of the respective gene from the first column, and in case of finding the matching sequence, remembers its chromosomal localization in the global genomic coordinates and assigns to the gene the sequence in the corresponding cell as the attribute of the type Experimental tables containing, for example, microarray data, are clustered based on the Z-scores obtained using Fisher r-to-Z transformation of Pearson correlation coefficients calculating co-expression of the query gene with each gene in each experiment (data for each gene are averaged over multiple probes). The resulting co-expression matrix Figure  highlighhttp://integromedb.org/api.jsp ('API XML-RPC' tab). Second, if the external user/resource wants to visualize retrieved object(s)  on his web resource using the BiologicalNetworks integrated research environment [http://integromedb.org/api.jsp 'API BiologicalNetworks' tab. Thus IntegromeDB/BiologicalNetworks maintains mutual cross-referencing with other web resources, that is not limited to simple text-based HTML links, but also enables partner websites to embed visualization of the BiologicalNetworks objects within their own web pages.One of the central aims of the IntegromeDB is to maintain cross-connectivity and integration with other public resources in a user-friendly manner. Therefore, we provide the programmatic access to our SQL database that is accessible via the following routes. First, the integrated content of IntegromeDB is available via the IntegromeDB API, which is implemented in Java. Through an XML-RPC service, API provides functions to access programmatically most of the features available in the IntegromeDB web interface, such as retrieving aliases, promoter sequences, or transcriptional regulators for a set of genes. Example code of using API and access to the XML-RPC service are available at ironment  he/she sMost of the data  returned by the IntegromeDB web site as a search results can be opened for detailed exploration and analysis in BiologicalNetworks. Figure Partner websites or third-party software programs can choose to embed the entire IntegromeDB website into their own software. Thus, an IntegromeDB 'plugin' can be established at the BioGPS  portal, Centralized and publicly available resources and search engines for integrated biological data are critical in enabling biologists to efficiently analyze genome-scale data. To meet this demand, we developed IntegromeDB, the general purpose unifying resource and search engine that can take the exploration of molecular biology data to the next level. IntegromeDB uses a hybrid approach to the Deep Web that combines elements of the crawl and federated search and data preprocessing approaches. It follows the trend established by other integrative resources, BioGPS  and EntrTo further enhance the ability of researchers to extract and manipulate the data, we will continue to provide the access to IntegromeDB through the API and data integration tools available in BiologicalNetworks making the most often used tools implemented as web services, thus eliminating the necessity to download BiologicalNetworks. We will also work on providing execution of SPARQL queries on the IntegromeDB data and create a SPARQL to SQL adaptor, using specifically designed dictionaries .We plan to continue extending the resource by adding new data sources and more experimental data and further developing the search engine's usability and speed. To improve the resource design and usability, we will rely on external experts (subject of funds availability) and we welcome and encourage community feedback that can be provided using the "Provide Feedback" button at the top right of the search results page. The speed of the presented search engine depends on both software and hardware; and while we will continue optimizing the search algorithms, we also plan to utilize a new hardware, specifically, a supercomputer Gordon that will become available at the San Diego Supercomputer Center in 2012.Project name: IntegromeDB• Project home page: http://www.integromedb.org• Operating system(s): Platform independent. Tested on the Internet Explorer 8, FireFox 8, Google Chrome web browsers on Windows 2000/XP/Vista/7, Linux/Ubuntu/Redhat, and MacOSX OS.• Programming language: Java• Other requirements: Java 1.6 for running BiologicalNetworks program;• License: GNU GPL version 3• Any restrictions to use by non-academics: none• The authors declare that they have no competing interests.SK, YD and MB designed and implemented the system and performed major programming work, JP contributed to the system concept, MB and JP wrote the manuscript. This work was coordinated by MB. All authors read and approved the final manuscript.Components of the IntegromeDB architecture and Search algorithm. Major components of the IntegromeDB search engine.Click here for file


Metagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Technological advances in next-generation sequencing methods are fueling a rapid increase in the number and scope of metagenome projects. While metagenomics provides information on the gene content, metatranscriptomics aims at understanding gene expression patterns in microbial communities. The initial computational analysis of a metagenome or metatranscriptome addresses three questions: (1) Who is out there? (2) What are they doing? and (3) How do different datasets compare? There is a need for new computational tools to answer these questions. In 2007, the program MEGAN  was released, as a standalone interactive tool for analyzing the taxonomic content of a single metagenome dataset. The program has subsequently been extended to support comparative analyses of multiple datasets.The focus of this paper is to report on new features of MEGAN that allow the functional analysis of multiple metagenomes (and metatranscriptomes) based on the SEED hierarchy and KEGG pathways. We have compared our results with the MG-RAST service for different datasets.The MEGAN program now allows the interactive analysis and comparison of the taxonomical and functional content of multiple datasets. As a stand-alone tool, MEGAN provides an alternative to web portals for scientists that have concerns about uploading their unpublished data to a website. Metagenomics seeks to understand microbial communities by DNA sequencing. Deeper sequencing and better reference databases are advancing the potential and success of such analyses. While metagenomics provides information on the gene content of a microbial community, metatranscriptomics promises to reveal the actual metabolic activities of this community at a specific time and place, and how those activities change in response to environmental forces or biotic interactions.A number of different systems and resources for metagenome or similar analysis, which are offered in the form of databases, web portals, web services and basic stand-alone programs -11. ThesFor the pathway analysis, the field of systems biology already possesses a high-quality database, namely the Kyoto Encyclopedia for Genes and Genomes (KEGG) . MEGAN pMEGAN performs a taxonomic analysis of a dataset by mapping reads onto different taxa in the NCBI taxonomy, depending on the phylogenetic footprint of the gene that a read contains . The reNow, as a new feature, MEGAN uses the SEED classification  for funcTo perform a functional analysis, MEGAN assigns each read to the functional role of the highest scoring gene in a BLAST comparison against a protein database. Figure To perform a KEGG analysis, MEGAN attempts to match each read to a KEGG orthology (KO) accession number, using the best hit to a reference sequence for which a KO accession number is known. MEGAN then calculates the number of hits to each KEGG pathway and reports these numbers to the user. The user can request to see the hits to a given pathway and an appropriate image of the pathway is generated by coloring the pathways based on the KEGG mapping. MEGAN allows one to analyze several datasets together, using different colors to show which parts of a pathway are present in which datasets. Because different genes that are present in different organisms in a consortium of microbes will often not operate together in a single pathway, MEGAN allows one to restrict the pathway analysis to a set of one or more taxa in the NCBI taxonomy .MEGAN supports the simultaneous analysis and comparison of the taxonomic content and now also the functional content of multiple datasets in several ways. The functional content of a set of metagenomes can be simulteniously opened and compared using a new SEED-based tree view  obtained from a hydrothermal vent microbial community. Out of a total of 1408 sequences, MG-RAST assigned 831 functions and MEGAN 727; the latter amounts to 88% of the assignments from MG-RAST. Figure 4 obtaineIn addition, we have tested our program on a published dataset ’ and ‘photosynthesis’.The citrate cycle (TCA cycle): The citric acid cycle is of central importance for cells that use oxygen as part of cellular respiration. We compared the results for this cycle produced by both tools (MEGAN and MG-RAST) using pooled DNA samples from the PLM-Bergen datasets. The resulting pathway graphs Figure  look verPhotosynthesis: For all the datasets considered, for the photosynthesis KEGG map, MG-RAST only provides hits associated with ‘F-type ATPase’, ‘Cytochrome b6/f complex (PetC)’ and ‘Photosynthetic electron transport (PetH)’. For example, using the four pooled DNA samples from the PLM-Bergen datasets, the MG-RAST server states that all the ‘F-type ATPase’ enzymes are present  obtained from a hydrothermal vent microbial community. We used eight marine datasets from Plymouth Marine Laboratory, consisting of four metagenomes (DNA) and four metatranscriptomes (cDNA) from a mesocosm experiment performed in Bergen, Norway (see  for detaMEGAN places each read of a given dataset onto one of the taxa (or “nodes”) of the NCBI taxonomy, based on the BLAST matches provided for the read, using the LCA algorithm. For functional assignment in a similar fashion, MEGAN provides a hierarchical representation using the SEED classification. Normally, reads are mapped to the NCBI taxonomy, and the program provides the exact numbers of reads assigned to any given node and the number of hits to any nodes in the subtree rooted at the node. In a similar fashion for the functional analysis, reads are mapped to SEED subsystems using the ‘seed2ncbi.gz’ file from the SEED server.After opening all the samples in MEGAN, we compared the taxonomic content using the ‘Compare’ menu item to obtain the comparison in a new window. Choosing the ‘SEED’ menu from the comparison window allowed us to get a functional comparison of the samples directly in a new window Figure .To compare six PLM-Bergen marine samples, based on their functional content, we chose ‘Networks’ from the ‘Option’ menu of MEGAN’s SEEDviewer to see the network comparison view of multiple datasets that are under consideration Figure . MoreoveMG-RAST is a leading service for functional annotation. To test the functional assignment of MEGAN, we compared MEGAN’s functional assignment with MG-RAST’s assignment in two datasets which are described using two case studies below.We first performed MEGAN-SEED annotations on an unpublished dataset of 1408 sequences (454 FLX-titanium pyrosequencing) of a metatranscriptome obtained from a hydrothermal vent microbial community and compared them with the parallel assignments obtained by MG-RAST. We then performed the same study with a published marine dataset  and compared the results with MG-RAST’s assignment.MEGAN includes a module called ‘KEGGviewer’ for the analysis of metagenomic data in the context of pathways. It is designed to consume a list of RefSeq accession numbers and maps them to KEGG orthologies (KO numbers). The functionality of this program has been compared to MG-RAST, which also provides basic pathway analysis methods for some of the above mentioned PLM-Bergen marine samples by selecting different metabolic pathways.SM and DHH designed the project and wrote the manuscript. PR performed the KEGG analysis and wrote necessary codes. DHH wrote necessary codes for SEED. SM performed all the BLAST, SEED analysis, and multiple comparison. All other authors contributed in MG-RAST analysis and in helpful discussion.The authors declare that they have no competing interests.


Comparative analysis is an essential component to biology. When applied to genomics for example, analysis may require comparisons between the predicted presence and absence of genes in a group of genomes under consideration. Frequently, genes can be grouped into small categories based on functional criteria, for example membership of a multimeric complex, participation in a metabolic or signaling pathway or shared sequence features and/or paralogy. These patterns of retention and loss are highly informative for the prediction of function, and hence possible biological context, and can provide great insights into the evolutionary history of cellular functions. However, representation of such information in a standard spreadsheet is a poor visual means from which to extract patterns within a dataset.We devised the Coulson Plot, a new graphical representation that exploits a matrix of pie charts to display comparative genomics data. Each pie is used to describe a complex or process from a separate taxon, and is divided into sectors corresponding to the number of proteins (subunits) in a complex/process. The predicted presence or absence of proteins in each complex are delineated by occupancy of a given sector; this format is visually highly accessible and makes pattern recognition rapid and reliable. A key to the identity of each subunit, plus hierarchical naming of taxa and coloring are included. A java-based application, the Coulson plot generator (CPG) automates graphic production, with a tab or comma-delineated text file as input and generating an editable portable document format or svg file.CPG software may be used to rapidly convert spreadsheet data to a graphical matrix pie chart format. The representation essentially retains all of the information from the spreadsheet but presents a graphically rich format making comparisons and identification of patterns significantly clearer. While the Coulson plot format is highly useful in comparative genomics, its original purpose, the software can be used to visualize any dataset where entity occupancy is compared between different classes.http://sourceforge.net/projects/coulson and http://dl.dropbox.com/u/6701906/Web/Sites/Labsite/CPG.htmlCPG software is available at sourceforge  With a rapidly growing database of completed genomes and consequential improvements to the reconstruction of deep and broad phylogenetic relationships, it has become possible to consider the molecular origins of many complex cellular systems. Such analyses can reveal deep relationships between cellular functions, identify lineage-specific features and uncover evolutionary mechanisms -5, and aWhile it is now comparatively trivial to generate vast datasets containing 100s to 1000s of query results using BLAST, HMMer and other sequence-based algorithms -10 theseon-the-fly plots and possibly wider adoption of the format. Hence, to facilitate generic/automated production and adoption of the plot we developed a platform-independent application, the Coulson plot generator (CPG), to draw Coulson plots from structured data that uses standard spreadsheet file formats as input. CPG should be accessible to the vast majority of workers with only rudimentary computing skills and requires minimal post-plot manipulations to generate publication quality plots of considerable complexity.To address these needs we devised the Coulson plot, a matrix of colorized pie charts and which displays information in a clustered format, together with hierarchical taxonomic labels and a key to individual gene products. This plot we, and others, have used in multiple publications and which we have found to be highly useful and accessible to readers of these reports ,17-24. HWe considered many of the formats commonly used in the published literature for the display of comparative genomic data, and found these frequently too complex or inelegant for the presentation of data in a manner that retains as much biological information as possible. Specifically, simple spreadsheets or dot plots are either difficult to read or lose information concerning complexes, which is especially critical to understanding evolutionary processes. In many instances dot blots also become very large, with moire effects and other issues emerging. Hence we designed a more sophisticated format that retains functional groupings, provides colors as keys to taxonomic relationships and also provides a key to subunit identity Figure . We conshttp://www.eclipse.org) to execute on any machine running a Java Virtual Machine (v1.5.0 or greater). CPG takes as input a comma separated (.csv) text file of binary data recording subunit occupancy in multiple systems  or Adobe Illustrator (http://www.adobe.com/products/illustrator.html?promoid=KAUCB). We selected this option as more efficient than attempting to build sophisticated editing tools into CPG as the precise choices and requirements of users and datasets are difficult to predict. This follows a similar philosophy to FigTree, a popular phylogenetics tree graphics package which also generates editable graphics requiring a small amount of finessing prior to publication (http://tree.bio.ed.ac.uk/software/figtree/). A Coulson plot with more than 200 pies can be produced satisfactorily.The CPG application opens with three tabs Figure A. The fiWe developed the Coulson plot to display and compare data on gene representation grouped by gene product complex or pathway membership and to display this information across multiple taxa Figure . An arraOne of the more flexible aspects of the CGP is that the user can decide quickly how best to group data. For example, complexes or pathways with many components may be difficult to visualize in the individual pie charts, with the result that clarity is lost. However, CPG allows pies to be set up that have only one subunit, for example mimicking the more standard dot blot format, or to subdivide the data into subcomplexes with biological relevance, to improve clarity or increase the amount of data that may be logically compared Figure . A seconWe have found the Coulson plot to be highly valuable for presentations of comparative genomic data, and that the lucid display of patterns within datasets more than offset the time required to manually produce these plots. However, we are aware that the skills required and potentially the effort needed acted as a barrier to adoption of a broadly potentially useful graphing format, and which is not available as part of commercial graphing packages as far as we are aware. We therefore developed a plotting tool that manages the vast majority of the plot functionality, leaving the user a format that can be subjected to final editing as appropriate for individual requirements.on-the-fly, allowing hypothesis-driven composition of datasets, is a distinct advantage, and we hope that the Coulson plot will become a more generally exploited format, and that the use of this plot beyond comparative genomics will be facilitated with the provision of CPG.A great many datasets have been used to test CPG ,17-24. Whttp://sourceforge.net/projects/coulson as a jar file or disc image for Mac OS X. Project home page: http://dl.dropbox.com/u/6701906/Web/Sites/Labsite/CPG.html and http://sourceforge.net/projects/coulson. The software is licensed under GNU Artistic license 2.0.CPG is a Java application and requires Java 1.5.0 or higher for the JVM. CPG source code and binaries are available from sourceforge: The authors declare that they have no competing interests.HIF created and tested the software. MCF identified the need and had input on software design and features. RMRC designed the original Coulson plot graphic format. All authors participated in the writing and approval of the manuscript and β-testing of the software. All authors read and approved the final manuscript.


The World Wide Web plays a critical role in enabling molecular, cell, systems and computational biologists to exchange, search, visualize, integrate, and analyze experimental data. Such efforts can be further enhanced through the development of semantic web concepts. The semantic web idea is to enable machines to understand data through the development of protocol free data exchange formats such as Resource Description Framework (RDF) and the Web Ontology Language (OWL). These standards provide formal descriptors of objects, object properties and their relationships within a specific knowledge domain. However, the overhead of converting datasets typically stored in data tables such as Excel, text or PDF into RDF or OWL formats is not trivial for non-specialists and as such produces a barrier to seamless data exchange between researchers, databases and analysis tools. This problem is particularly of importance in the field of network systems biology where biochemical interactions between genes and their protein products are abstracted to networks.For the purpose of converting biochemical interactions into the BioPAX format, which is the leading standard developed by the computational systems biology community, we developed an open-source command line tool that takes as input tabular data describing different types of molecular biochemical interactions. The tool converts such interactions into the BioPAX level 3 OWL format. We used the tool to convert several existing and new mammalian networks of protein interactions, signalling pathways, and transcriptional regulatory networks into BioPAX. Some of these networks were deposited into PathwayCommons, a repository for consolidating and organizing biochemical networks.The software tool Sig2BioPAX is a resource that enables experimental and computational systems biologists to contribute their identified networks and pathways of molecular interactions for integration and reuse with the rest of the research community. BioPAX is a protocol for the specification and representation of cell signaling pathways, gene-regulatory networks, protein-protein interactions and other types of biomolecular interaction data . There aBioPAX is one of several specification protocols that have been developed in an attempt to formally characterize biochemical regulatory molecular interactions. Some of these other specifications include the Proteomics Standard Initiative Molecular Interactions format (PSI-MI)  and the We created a Java-based tool, Sig2BioPAX, for the purpose of converting cell signaling pathways and interaction network datasets into BioPAX. The software package uses the Jena Library, which irules.txt is included with the program's release. Users may modify the included rules.txt or create their own rules file from scratch.There are a finite number of reaction types that can be processed and converted into the BioPAX format. These reactions are described in Table sig, is described in Table source_target, forgoes describing the source and target types (i.e. kinase) and only describes the reaction type between the source and target (i.e. phosphorylation). The third template type is tf_target, used to describe transcription factor binding to the promoter of target genes as determined by ChIP-seq or ChIP-chip types of experiments [sif, or simple interaction format, which is a format specifying the target and source names and the interaction type. In the implementation, input templates are stored within the Line.java source file, which contains the function ReadLine. This function is composed of IF statements, one for each input template type. In this code file, the testing condition for the IF statement are defined. The input line is parsed through a series of statements. By modifying the order and content of these statements, the user can customize the input templates.One of Sig2BioPAX's accepted input parameter is the input file template type, which is a descriptor of the format of the input file. Four different input formats are accepted by the current version. The default type, eriments . The fou-cmd, accepts input parameters including the input file name, output file name, rules file name, option to overwrite output file, and an input-template type. In the command line version of the program, these options are passed as command line arguments. For example, to use input file foo.txt, output file bar.owl, the rules file rulesfile.txt, and the overwrite option, the command is: 'java -jar sig2biopaxv4.jar - cmd -in:foo.txt -out:bar.owl -r:rulesfile.txt -o'. The sig input template type is the default input template type. In the GUI version of the program, the input, output, and rules files can be selected from a file browser and the other options can be set interactively  versions. The command line version of the program, accessible by using the command line argument We used Sig2BioPAX to convert several network datasets into the BioPAX format, and some of these networks are available on Pathway Commons , an inteAs the bulk and complexity of genome-wide molecular data increases, methods for sharing and exchanging data need to be further developed. Effective standard representation of data enables seamless data exchange across platforms, tools and databases. However, converting existing and new data into such exchange formats is not trivial. The Sig2BioPAX tool will further enable researchers to easily convert their flat file interaction data into the computable BioPAX format so that their data can be reused and interpreted by other researchers worldwide.Project name: Sig2BioPAXProject home page: http://code.google.com/p/sig2biopax/Operating system(s): Platform independentProgramming language: JavaOther requirements: Jena 2.6.2 or higherLicense: GNU GPLAny restrictions to use by non-academics: noneThe authors declare that they have no competing interests.AM initiated and managed the project. RLW implemented and tested the Sig2BioPAX Java tool. AM and RLW wrote the manuscript. All authors read and approved the final manuscript.