Genome-wide association studies (gwas) are invaluable in revealing the common variants predisposing to complex human diseases. Yet, until now, the large volumes of data generated from such analyses have not been explored extensively enough to identify the molecular and functional framework hosting the susceptibility genes.We investigated the relationships among five neurodegenerative and/or autoimmune complex human diseases  by characterising the interactomes linked to their gwas-genes. An initial study on the MS interactome indicated that several genes predisposing to the other autoimmune or neurodegenerative disorders may come into contact with it, suggesting that susceptibility to distinct diseases may converge towards common molecular and biological networks. In order to test this hypothesis, we performed pathway enrichment analyses on each disease interactome independently. Several issues related to immune function and growth factor signalling pathways appeared in all autoimmune diseases, and, surprisingly, in Alzheimer's disease. Furthermore, the paired analyses of disease interactomes revealed significant molecular and functional relatedness among autoimmune diseases, and, unexpectedly, between T1D and Alz.The systems biology approach highlighted several known pathogenic processes, indicating that changes in these functions might be driven or sustained by the framework linked to genetic susceptibility. Moreover, the comparative analyses among the five genetic interactomes revealed unexpected genetic relationships, which await further biological validation. Overall, this study outlines the potential of systems biology to uncover links between genetics and pathogenesis of complex human disorders. The aim of genome-wide association studies (gwas) is to discover the common genetic variants associated with susceptibility to complex diseases. In a typical experiment, hundreds of thousands of markers are tested simultaneously in cases and controls, and the allelic frequencies of each marker in the two groups are compared, so that the contribution of single genes to the disease is quantified. However, complex diseases do not originate from changes in single genes, but from the interactions between several genetic and environmental factors. Therefore, the analysis of isolated genes is not overly informative about the biological processes underlying disease and offers limited rationale for the development of novel therapies. Theoretically, reconstruction of the molecular interaction networks linked to gwas-genes by systems biology approaches may help to elucidate the functional consequences related to each susceptibility allele and the combined effects of more genetic variants. The few reports available to date on this issue demonstrated that this approach can identify previously unseen relationships among human diseases at molecular level In this study, we elaborated the genetic interactomes relative to five complex human diseases. Our lab has strong interest in multiple sclerosis (MS), a chronic disorder of the central nervous system presumably of autoimmune etiology, characterized by inflammation of the white matter, demyelination and neurodegeneration −5  or autoimmune  disorders. We utilized results from 39 published genome-wide association studies on these five human diseases available in the GWAS catalog −5 . There w−5 . Notably−5 . Interes−5 . Notably−5 , and tha−5 , includiNow, the analysis of disease relatedness with MS is more effective when considering the genetic interactome rather than direct interactions at the gwas-gene level. In fact, no interactions existed between gwas-genes predisposing to MS and Alz, for example, but there were several shared interactions within the genetic interactomes linked to (but not including) the respective gwas-genes . TherefoTo perform a global comparative analysis among autoimmune and neurodegenerative genetic interactomes, we derived a list of interacting partners for each gwas-geneset . As expeNext, we examined the relationships among the five diseases by comparing the genetic interactomes in a pairwise fashion. The overlaps between interactomes are given in normalization factor by normalizing each disease interaction ratio to the one in the VisANT database . Consequently, we derived a database . For exanormalization factors relative to the two diseases . The highest number of pathways was reported in T1D followed by MS, RA, Alz and Park respectively . Furthernd Other . The comGrowth factor/Hormone signaling category, Panther indicated EGF, FGF and PDGF signaling pathways as shared by three diseases, Alz and T1D with MS or RA. The other pathways were shared exclusively by autoimmune diseases, with the exception of Angiogenesis that appeared both in T1D and Alz. In contrast to Panther, KEGG evidenced predominant pathway sharing among autoimmune disorders, except for the ErbB signaling present in Alz and in the three autoimmune diseases. CGAP-BioCarta exposed high concordances among Alz, T1D and MS. There were 10 pathways shared exclusively by these three diseases, among which EGF signaling and PDGF signaling were already reported by the Panther database. Many of the pathways common to these trios were related to tyrosine kinase signaling, such as Trka receptor signaling and Sprouty regulation of tyrosine kinases. Another remarkable observation was the sharing of the NGF pathway by MS and Alz. IGF-1 signaling, IL3 signaling, Insulin signaling and Growth hormone signaling pathways were shared exclusively by T1D and Alz. In addition, Park shared only three pathways with the autoimmune diseases, namely Ceramide signaling, Trefoil factors initiate mucosal signaling and Phosphoinositides and their downstream targets. Overall, pathways shared by the five diseases in the Growth factor/Hormone signaling category portrayed an undisputable association within the autoimmune group with the predominant presence of T1D. It also revealed that several biological themes related to tyrosine kinase signaling were shared among Alz, T1D and MS. Most unexpectedly, the analysis exposed numerous growth factor related pathways common to T1D and Alz.In the Innate and adaptive immunity contained 20 pathways derived from the three pathway databases. Although expected for autoimmune diseases, consistent sharing of immunity related pathways was also found in Alz. For example, pathways related to B and T cell activation appeared in all databases as shared by T1D, RA, MS and Alz. Furthermore, in the KEGG database Alz and T1D exclusively shared the Fc epsilon RI signaling pathway and Natural killer cell mediated cytotoxicity pathway, whereas CGAP-BioCarta emphasized predominant pathway sharing between MS and RA.The second category Cell cycle and apoptosis, Panther and KEGG contributed with 2 pathways each, and Park shared the Apoptosis signaling pathway with RA and MS. Among the CGAP-BioCarta results, Alz shared 3 pathways with autoimmune diseases, while 4 pathways were shared exclusively by T1D-RA or RA-MS disease pairs.In the third category, Cancer. It resulted that many genes appearing in autoimmune and/or neurodegenerative interactomes played a role in cancer related pathways.The fourth category contained pathways derived from the KEGG database related to Adhesion), the Focal adhesion pathway (KEGG) was common to Park and the autoimmune group and the Integrin signaling pathway (CGAP-BioCarta) was shared among neurodegenerative disorders and T1D. In addition, the Adherens junction pathway in KEGG was shared among Alz, T1D and MS.In the fifth category  shared among the disease pairs. Consistently, we observed that the number of shared pathways was proportional to the number of shared interactors in most cases . HoweverFinally, we ranked the associations among diseases based on the statistical significance of shared pathways in each database . Scores Altogether, the relationships among diseases identified at the level of molecular interactions were found to be sustained at the level of biological pathways, especially among autoimmune diseases. Surprisingly, the same approaches evidenced strong relatedness between Type 1 diabetes and Alzheimer's disease.The reconstruction of the molecular framework hosting genetic variants associated with susceptibility to a complex human disorder has the potential to reveal the biological mechanisms underlying that disease and to highlight similarities with other diseases. Here we made a cross-disease comparison of autoimmune  and neurodegenerative diseases  starting from the point of view of MS, as it presents both autoimmune and neurodegenerative facets Previous studies have shown that molecular relatedness between distinct diseases may be found when searching for direct relationships among gwas-genes B-cell activation and T-cell activation appeared in all autoimmune diseases. It is well known that both arms of adaptive immunity greatly contribute to autoimmunity. Our data suggest that the processes leading to alterations in immune tolerance may be caused or sustained by the genetic framework, and, hence, support the rationale for therapeutical approaches targeting T and B lymphocytes in autoimmune disorders. Surprisingly, the same pathways appeared in Alzheimer's disease. The role of adaptive immunity in Alz so far remains under-explored, however some studies suggest altered T cell phenotypes and responses in such patients  diabetic patients present deficits in numerous cognitive functions  of the database. The list of genes associated with each disease is summarized in We collected genome wide association data related to five human diseases  from the GWAS catalog (December 2009 version). The GWAS database is an online resource comprising of all published genome-wide association studies or meta-analyses of them, and provides information regarding the gene, SNP variations and their statistical significance in each study. We retrieved all the genes predisposing to each disease with the default critical p-value . This ratio was used for normalization, as follows:To derive the list of interacting partners of the gwas-genes, we used the VisANT tool (December 2009 version) Disease interaction ratio  =  No. of interactions/No. of gwas-genes.Normalization factor  =  Disease interaction ratio/VisANT interaction ratio.Paired normalization factor  =   x .Observed  =  No. of interactors shared within a disease pair.Expected  =  (No. of interactors in disease1/12888) x (No. of interactors in disease2).Interactor score  =  (Observed/Expected) x .We chose the ToppGene suite for determination of pathways enriched in the genetic interactomes. At the time of analysis, this tool maintained a meta-database of pathway information derived from KEGG (update: June 2009), Panther (update information not available), CGAP-BioCarta (update: August 2009). The analysis was performed with the stringent criteria of Bonferroni corrected p-value cut-off 0.05 in all cases. The combined p-values were calculated for the shared pathways among diseases using Fisher's method Table S1Detailed records about the studies in the GWAS catalog used in our analyses.(XLS)Click here for additional data file.Table S2Overlap of MS genetic interactome with genes predisposing to other neurodegenerative or autoimmune diseases.(XLS)Click here for additional data file.Table S3List of the gwas genes and their interactors.(XLS)Click here for additional data file.Table S4Shared interactors in disease pairs.(XLS)Click here for additional data file.Table S5Statistical significances for interactor sharing and interactor scores.(XLS)Click here for additional data file.Table S6Statistical significances for pathway sharing and ranking of disease associations.(XLS)Click here for additional data file.Table S7List of shared pathways in disease pairs.(XLS)Click here for additional data file.


Signaling pathways belong to a complex system of communication that governs cellular processes. They represent signal transduction from an extracellular stimulus via a receptor to intracellular mediators, as well as intracellular interactions. Perturbations in signaling cascade often lead to detrimental changes in cell function and cause many diseases, including cancer. Identification of deregulated pathways may advance the understanding of complex diseases and lead to improvement of therapeutic strategies. We propose Analysis of Consistent Signal Transduction (ACST), a novel method for analysis of signaling pathways. Our method incorporates information regarding pathway topology, as well as data on the position of every gene in each pathway. To preserve gene-gene interactions we use a subject-sampling permutation model to assess the significance of pathway perturbations. We applied our approach to nine independent datasets of global gene expression profiling. The results of ACST, as well as three other methods used to analyze signaling pathways, are presented in the context of biological significance and repeatability among similar, yet independent, datasets. We demonstrate the usefulness of using information of pathway structure as well as genes’ functions in the analysis of signaling pathways. We also show that ACST leads to biologically meaningful results and high repeatability. Though gene expression microarray-based experiments are popular in life science research, microarray data analysis and interpretation of its results are still challenging. Typically, these analyses lead to lists of genes with the most differential expression between the compared groups. An important limitation of such approach is that genes with moderate but meaningful expression changes may not meet the strict cutoff and alternation of molecular processes may be missed These limitations could be overcome by signaling pathway analysis. Signaling pathways are maps of processes occurring in cells and may represent signal transduction from an extracellular stimulus via a receptor to intracellular mediators, as well as intracellular interactions. Various studies have demonstrated the potential of using gene expression profiles for the analysis of oncogenic pathways Signaling pathways contain information not only about the presence of a given element (gene or protein), but also about interactions between their elements e.g. co-regulation. All elements in a particular pathway have their specific functions and positions in a given pathway, and both of them depend on the pathway. Furthermore, a given interaction may represent protein-protein relationship (e.g. phosphorylation) or gene-gene relationship (e.g. indicating relation of transcription factor and target gene product) Basically, there are two different types of methods for signaling pathway analysis. One of them One of the methods for analysis of signaling pathways is Signaling Pathway Impact Analysis (SPIA) proposed by Tarca et al. Another method, Bayesian Pathway Analysis (BPA) presented by Isci et al. In this paper we propose Analysis of Consistent Signal Transduction (ACST), a novel method for the analysis of signaling pathways. ACST incorporates three types of features: pathway topology information, the position of every gene within a given pathway and the magnitude of changes of all analyzed genes. ACST requires neither arbitrary cut-offs nor arbitrary parameters. In contrast to previously published methods, we propose a method without an oversimplifying assumption that gene expression changes unambiguously reflect protein action (e.g. phosphorylation). In our method each pathway is examined for a consistent relationship between transcription factor and its target genes-genes that are directly regulated by this transcription factor. Expression changes of the latter components of the examined pathway reflect changes in physiological events. Our results are evaluated in forms of nominal p-values and false discovery rate correcting for multiple hypothesis.While testing ACST not only did we use a number of independent experimental datasets, but we also tested repeatability of results examining biologically similar, yet independent, datasets. We compared ACST with results achieved with the methods mentioned above (SPIA and BPA) and with a popular method designed to gene set analysis: Gene Set Enrichment Analysis In order to apply the Analysis of Consistent Signal Transduction we need (i) gene expression measurements and (ii) definitions of signaling pathways. (i) can be obtained with gene expression microarrays. (ii) may be defined using KEGG In this study a particular signaling pathway is represented as a directed graph For the purposes of this study we need to define a consistent relation and a consistent graph. Assuming that two experimental conditions (e.g. disease vs. control) are compared, A consistent graph is a graph in which all edges represent consistent relations. Moreover, a consistent subgraph 1. 2. In general, ACST shares the scheme presented by To compute local statistic we use two-sided t statistic, To compute global statistic each graph The occurrence of a consistent subgraph stands for relative (with reference to control) change of gene-gene co-regulations in tested biological conditions. With regard to biological knowledge we scored only maximal consistent subgraphs. Using only maximal consistent subgraph does not necessarily lead to the highest score, but the greater range of transduction is found, the more likely it is that a functional change is observed. Additionally, since we do not expect that the majority of all analyzed genes are differentially expressed, negative The http://jmieczkowski.nencki.gov.pljmieczkowski.nencki.gov.pl and in Additional file S1.To assess the global statistic we used class label permutations. Class label permutations allowed us to maintain gene-gene relations in each sample and draw conclusions about biological replications. Let Considering the methodological challenges raised in Pathway definitions contain information not only about gene expression interactions, but also on protein-protein interactions (e.g. phosphorylation). However, gene expression data only provide information regarding mRNA levels in analyzed samples. Therefore, in our work we used only information regarding gene expression interactions (marked GErel in KEGG). This type of interaction describes the relationship between particular transcription factor and the protein whose expression is directly regulated by this transcription factor. The proposed algorithm does not include other type of protein action, especially enzymatic reaction like phosphorylation as they are not reflected by microarray data. Due to limitation to genegene relations in this study we analyzed only 47 signaling pathways out of 209 from KEGG database . We realize that such limitation of applicability might be seen as a drawback, but we believe that it is a drawback due to type of data, not of the method itself. Thus, in contrast to other signaling pathway methods we deliberately do not make assumptions about protein-protein interactions based on transcriptional expression data. For instance, we believe that it may be misleading to conclude about activation by phosphorylation based solely on transcriptional expression profiles.Another innovative aspect of our method is looking for consistent relations. We agree that it is rather restrictive and on some occasions our method may miss some pathways. However, occurrence of consistently related genes is of great importance. Indicating signaling pathway in which such consistent relation occurs might be very useful for understanding the biological process as well as for identifying targets for molecular therapy. We think that the methods which lead to a lower number of false positives are desired, even if some true positives might be missed. Nevertheless, it is important to note that ACST may simply be used also with protein expression data (e.g. data from protein expression arrays). Since the t-test does not depend on scale, some gene expression data may simply be merged or replaced with proteomic expression data and more types of interactions may be used.Assessing performance of any new method for pathway analysis is difficult because of the absence of gold standard In this study we used nine publicly available datasets. All the raw data were downloaded from GEO database affyQCReport Bioconductor package All data manipulations were performed using R statistical environment SPIA and limma . For all presented methods, pathways with adjusted p-value below 0.25 were considered significant.In order to compute Signaling Pathway Impact Analysis we used In the present study we analyzed nine publicly available datasets (described below), for which considerable background information is available. All of them have been used to address biological questions, namely to compare gene expression in disease versus normal tissues. Using real datasets allows us to preserve the gene-gene correlations and dependencies. All the methods have been validated by testing: (i) whether the obtained results confirm biological knowledge concerning gene expression deregulation under studied conditions, and (ii) whether the obtained results are repeatable on similar datasets. In order to verify (i) we used datasets for which the original results of analyzes were further confirmed using biochemical methods. This type of validation is considered the most proper assessment of quality of a novel approach and it was used in previous studies Additionally, ACTS was compared with several existing methods including the most widely used method for gene sets analysis-GSEA and two methods for pathway analysis-SPIA and BPA. Signaling pathways with adj.p-valIn order to test the repeatability of signaling pathway analyses, we reanalyzed data from three recent studies of colorectal cancers. The first dataset consists of gene expression profiling of samples from young patients . These two pathways characterize kidney functions and perturbations in those pathways are typical for kidney failure. Vasopressin is a critical regulator of water homeostasis by controlling the water movement from lumen to the interstitium for water reabsorption and adjusting the urinary water excretion. Regulation of calcium homeostasis by reabsorption is one of the main kidney functions and elevated calcium level is one the first symptoms of renal carcinoma. The results are consistent with biological knowledge concerning the pathological state.In order to further demonstrate the utility of our method, we applied ACST to five additional datasets. The first two datasets originated from the comparison of gene expression changes in renal cell carcinoma with normal kidney tissue samples Endocrine and other factor regulated calcium reabsorption is composed of four important protein VDR, ESR1, CALB1 and ATP2B1. VDR and ESR1 as transcription factors regulate the expression of protein which play a critical role in intracellular calcium homeostasis like CALB1 and ATP2B1. An independent study showed that inadequate calcium intake is an important risk factor for various types of cancer. VDR and all vitamin D metabolizing enzymes are expressed in normal kidney but during the malignant transformation, expression of VDR and the metabolizing enzymes are lost, though the implications of this loss are unknown Vasopressin regulated water reabsorption is composed of CREB3L2 and AQP2. CREB3L2 is a transcriptional activator that regulates the expression of AQP2-a water channel protein located in the kidney collecting tubule. AQP2 is associated with the pathophysiology of several sodium and water balance disorders As previously mentioned the main advantage of the proposed method is a possibility to identify the consistent subgraph within the identified pathway. The consistent subgraph is composed of transcription factors as well as proteins that are directly regulated by these transcription factors provided that observed changes have a consistent direction. The subgraph within pathway Insulin signaling pathway and Aldosterone regulated calcium reabsorption were indicated for RCC1 and RCC2, respectively. Aldosterone regulated calcium reabsorption similarly to the two mentioned pathways describing kidney functions. Multiple lines of evidence implicate the Insulin signaling in the development and progression of cancer. In the context of developing new treatment for renal cell carcinoma, IGF1 signaling has been shown to regulate HIF1-Additionally, TLR signaling pathway  Legionellosis  Wnt signaling pathway  Focal adhesion . The indicated pathway reflect the pathological condition. Toll-like receptors (TLR), key receptors in innate immunity, play a role in cancer progression and development, including breast cancer. Signaling pathways like Wnt signaling pathway and Focal adhesion, are misexpressed in breast cancer and correlate with poor clinical outcomes. It was recently showed that mammary epithelial-specific disruption of focal adhesion kinase retards tumor formation and metastasis in a mouse model of human breast cancer Legionellosis in this comparison is due to the presence of transcription factor Myd88. Myd88 regulates activation of numerous proinflammatory genes during infection as well as tumor development The next two datasets Cell cycle and Melanogenesis pathways as the most affected pathways  that are directly responsible for up-regulation of proliferation. The second identified pathway, Melanogenesis, has not been associated so far with VIN. However, MITF-one of the genes being part of a consistent subgraph-has been shown recently to act coordinately with transcription factor FoxO to regulate expression of proapoptotic and cell cycle control genes by phosphatidylinositol 3-kinase/Akt/glycogen synthase kinase 3 signaling The last dataset, VIN, was generated to compare gene expression profiles between vulvar intraepithelial neoplasia (n = 10) and control samples (n = 9) The five mentioned datasets were also analyzed with SPIA, BPA and GSEA. The results of these analyses are presented in Additional File S2. Although SPIA, BPA and ACST were designed to analyze signaling pathways all those methods are based on different assumptions. SPIA measures overrepresentation of differentially expressed genes and propagation of expression changes, BPA uses Bayesian network models while ACST is designed to capture genes with consistent expression changes. On the other hand, GSEA, which is designed for analysis of gene sets, does not take into account relations among genes and measures enrichment of genes from a tested gene set in the list of differentially expressed genes. Moreover, SPIA and BPA use information not only about gene expression interactions, but also about protein-protein interactions. The different approaches cause that not all KEGG pathways could be analyzed with those methods. BPA may be used to analyze 206 out of 209 pathways included in KEGG database. SPIA analyzes 113 pathways. Since ACST is focused on gene expression interaction it may analyze 47 KEGG pathways.Colorectal cancer pathway as significantly perturbed in three colorectal cancer datasets. None of the other methods indicated this pathway as significantly changed, even when computations of adj.p-values were limited to pathways analyzed with ACST. It is worth noticing that regardless of the number of tested pathways, the pathways which seem to be fundamental for colorectal carcinogenesis  were not indicated by BPA, SPIA or GSEA. Most of the pathways indicated by GSEA and BPA in colorectal cancer datasets as well as in RCC, BC and VIN characterize cell metabolism . It is generally accepted that cancer cells display dramatically altered metabolic circuitry Extracellular matrix-receptor interaction, Focal adhesion and Cell cycle, indicated for at least two colorectal cancer datasets known to be relevant to colorectal cancer.Importantly, only ACST identified To assess repeatability of a given method, we compared an each pair of the results with two factors (i) the number of signaling pathways called significant for both datasets and (ii) Spearman’s rank correlation coefficient between lists of nominal p-values. (i) allowed us to compare agreement of the top of the lists, which is the most important but depends on the cutoff, while with (ii) we measured agreement of the whole rankings. We show values computed for all possible signaling pathways (in brackets) as well as only for the pathways used by ACST .For colorectal cancer datasets the highest repeatability was obtained with BPA analyzing all pathways from KEGG. ACST also led to high Spearman’s correlations between colorectal cancer datasets. Although Spearman correlation for CC1 with other datasets is much lower (19 with CCms and 19 with CCade), two pathways significantly perturbed in both sets were indicated. The low correlation could be explained by the fact that in CC1 morphologically normal-appearing mucosa from patients with cancer were compared with that of healthy controls. Our data suggest that the mucosa of patients were actually not normal but were already “primed” for carcinogenesis although it is not cancer tissue. Moreover, only tumors classified as microsatellite stable were included in CC1, while CCms contains only tumors classified as microsatellite unstable. It was shown that patients exhibiting high microsatellite instability have different expression profiles and cancer etiology In this study we propose Analysis of Consistent Signal Transduction (ACST), a novel method for detection of deregulated signaling pathways using microarray gene expression data. ACST incorporates information about pathway topology, as well as about the position of every gene in each pathway. Using nine independent biological datasets, we have compared the performance of ACST with three methods commonly used to analyze signaling pathways in terms of biological significance (relevance of the results), as well as repeatability of the results among similar, yet independent, datasets. The obtained results led us to three conclusions. First, we demonstrate usefulness of predefined information of a pathway structure as well as genes functions in an analyzis of signaling pathways. Second, although not only ACST led to biologically meaningful results, we show that ACST is characterized by the highest repeatability among similar, yet independent, datasets. Third, we present that ACST is characterized by high specificity. In some cases it may result in loss of sensitivity, however, when false positives could not be easily excluded (e.g. verification of a novel function of a particular protein when nothing is known about the expected outcome) such characteristic is highly preferred.File S1An R code used to perform ACST.(R)Click here for additional data file.File S2This document shows (i) list of analyzed pathways; (ii) results obtained with SPIA, BPA and GSEA; (iii) lists of eliminated.CEL files; (iv) correlations  between results obtained for different datasets.(XLS)Click here for additional data file.File S3Fragments of pathways containing all consistent subgraphs of signifcantly deregulated pathways in both RCC datasets as well as in both BC datasets. Additionally, this file contains also table which contain a summary of results obtained with each of tested methods.(PDF)Click here for additional data file.


Pathway data are important for understanding the relationship between genes, proteins and many other molecules in living organisms. Pathway gene relationships are crucial information for guidance, prediction, reference and assessment in biochemistry, computational biology, and medicine. Many well-established databases--e.g., KEGG, WikiPathways, and BioCyc--are dedicated to collecting pathway data for public access. However, the effectiveness of these databases is hindered by issues such as incompatible data formats, inconsistent molecular representations, inconsistent molecular relationship representations, inconsistent referrals to pathway names, and incomprehensive data from different databases.Integrated Pathway gene relationship database for model organisms and important pathogens). Four organisms--S. cerevisiae, M. tuberculosis H37Rv, H. Sapiens and M. musculus--are included in this version (V2.0) of IntPath. IntPath uses the "full unification" approach to ensure no deletion and no introduced noise in this process. Therefore, IntPath contains much richer pathway-gene and pathway-gene pair relationships and much larger number of non-redundant genes and gene pairs than any of the single-source databases. The gene relationships of each gene (measured by average node degree) per pathway are significantly richer. The gene relationships in each pathway (measured by average number of gene pairs per pathway) are also considerably richer in the integrated pathways. Moderate manual curation are involved to get rid of errors and noises from source data . We turn complicated and incompatible xml data formats and inconsistent gene and gene relationship representations from different source databases into normalized and unified pathway-gene and pathway-gene pair relationships neatly recorded in simple tab-delimited text format and MySQL tables, which facilitates convenient automatic computation and large-scale referencing in many related studies. IntPath data can be downloaded in text format or MySQL dump. IntPath data can also be retrieved and analyzed conveniently through web service by local programs or through web interface by mouse clicks. Several useful analysis tools are also provided in IntPath.In this paper, we overcome these issues through extraction, normalization and integration of pathway data from several major public databases . We build a database that not only hosts our integrated pathway gene relationship data for public access but also maintains the necessary updates in the long run. This public repository is named IntPath (http://compbio.ddns.comp.nus.edu.sg:8080/IntPath.We have overcome in IntPath the issues of compatibility, consistency, and comprehensiveness that often hamper effective use of pathway databases. We have included four organisms in the current release of IntPath. Our methodology and programs described in this work can be easily applied to other organisms; and we will include more model organisms and important pathogens in future releases of IntPath. IntPath maintains regular updates and is freely available at  The proliferation of pathway databases--e.g., KEGG , WikiPatn source databases each contains information on a particular pathway, that pathway is presented by the aggregator as n separate pathways. On the other hand, GenMapp [There are many approaches to integrate pathways. For example, Pathway Commons and PathCase  can be c GenMapp , Cytosca GenMapp , and Pat GenMapp  can be c GenMapp  can be cVery recently, Stobbe et al.  comparedBesides the databases that focus specifically on pathway data integration, some protein functional interaction databases have also extended their collection to pathway data. For example, ConsensusPathDB  integratIntPath, the Integrated Pathway gene relationship database for model organisms and important pathogens. This approach was also taken earlier by Soh et al. [In this paper, we take this full unification approach in building h et al.  when theThe incompatible data formats of different databases seriously inhibit effective and compatible information retrieval. In KEGG, pathways are represented in KGML format and SOAP . In WikiPathways, pathways are represented in GPML format; recently it begins to support web service , allowinHomo Sapiens is HGNC Symbol, Mus musculus is MGI Symbol, Saccharomyces cerevisiae is Systematic name, and Mycobacterium tuberculosis H37Rv is TuberList Rv number. The unified IntPath gene relationship representations are listed in Table Inconsistent molecular representations significantly lower the effectiveness of pathway information retrieval. Different databases maintain different naming conventions on the nodes of their pathways. In KEGG, the names of the nodes (genes and proteins) in the pathways can be KEGG Entry name, KEGG ORTHOLOG (KO) ID, etc. The graphic names on KEGG pathway map can be Gene Symbol (or synonym), Enzyme Commission (EC) number, etc. In WikiPathways, the nodes' "TextLabel" are given gene symbol (or synonym), gene name, protein name, EC number, etc. In most cases the nodes can also be given Entrez ID, NCBI Accession, Ensembl Gene ID, Ensembl protein ID, UniProt Assession, etc. And some times nodes in WikiPathways are only given "TextLabel" without any database reference ID. MouseCyc  mainly uInconsistent referrals to pathway names are another source of confusion that substantially reduces the effectiveness of retrieving information on the same pathway from different databases. For instance, KEGG may refer to a pathway as "Glycolysis/"Gluconeogenesis", and WikiPathways may name it as "Glycolysis and Gluconeogenesis". For another example, WikiPathways contains a pathway with the name "Cholesterol Biosynthesis", while BioCyc has many corresponding pathways such as "cholesterol biosynthesis III (via desmosterol)", "cholesterol biosynthesis II ", "cholesterol biosynthesis I", and "superpathway of cholesterol biosynthesis". Therefore, a unified pathway naming system may reduce the confusion when referring to the same or similar pathway information from different databases.Furthermore, the comprehensiveness of data from different databases is another limitation of these pathway databases. By the term "incomprehensiveness", we mean that each single biological database is not a comprehensive representation of biological knowledge that is considered by experts to be accurate . We reveHomo sapiens, Mus musculus, Saccharomyces cerevisiae and Mycobacterium tuberculosis H37Rv. For each organism included in IntPath, the pathway data are collected from three representative databases: 1. KEGG; 2. WikiPathways; 3. One of the following four databases--YeastCyc, HumanCyc [We choose several representative data sources--KEGG , WikiPatHumanCyc , and MTBHumanCyc ,5; and MHumanCyc . The fouMouseCyc is curated by the Jackson Laboratory; it is a new, manually curated database of both known and predicted metabolic pathways for the laboratory mouse . YeastCyThe first step of extracting information from pathway databases is downloading the XML files. To automatically download the hundreds of KGML files of each organism on the KEGG ftp site, we use a simple spider program written in Perl. For BioCyc and MouseCyc, the BioPAX files--and for WikiPathways, the GPML files--are compressed into a single package which can easily be downloaded manually.Extracting the pathway-gene and pathway-gene pair relationships from KGML is accomplished using an in-house Java program, which extensively uses regular expressions to retrieve specific information from the KGML files. A KGML file consists of entries like "</entry>", "</relation>" and "</reaction>"; in each entry there is either entry information of the nodes , groups (complexes of gene products like protein complexes and so on) or relationships (relationship between the nodes in the pathway map). Using regular expressions we can specifically obtain the genes of each pathway and the relationships between each gene, and then link these genes according to the relationships. For genes belonging to complexes (groups), the binary gene pairs are generated based on the matrix model.An alternative way of retrieving KEGG pathway genes and gene pairs is by calling the KEGG API, which enables users to easily use their programs to get access to the KEGG database. However, the API is not well updated . The KEGExtracting pathway-gene and pathway-gene pair relationships from a GPML file is also accomplished using a strategy similar to mining KGML files. Mining a GPML file is much more difficult due to its wikistyle; and there are slight variations among individual GPML files even in the same organism, like some key tags may be in upper case in one file but lower case in another, random insertions of whitespace character, etc. Due to these variations, the regular expressions used for performing the extraction must be very robust. In GPML files, the information of genes and proteins are stored in a "</DataNode>" entry where, if the node is a gene or protein, the Type of the entry is set to "GeneProduct". The information of relationships (like activation and inhibition) are stored in a "</Line>" entry. The linkage of "</DataNode>" entry and "</Line>" entry is mainly accomplished by their "graphID". A "</Line>" entry usually records which two "</DataNode>" entries it links to through the records of two corresponding "graphID" of the "</DataNode>" entries. Using this information we can retrieve the relationship of two genes linked by a "</Line>" entry. This relationship--like inhibition, activation, etc.--can be regarded as equivalent to the "PPrel" relationship in KGML.If the genes belong to certain complexes (groups), their "GroupRef" ID are recorded in some "</DataNode>" entries; and genes with same "GroupRef" ID are in the same group (molecular complexes). All possible pair-wise relationships among members of a group are generated based on the matrix model. These relationships, derived from such a group, are mainly binary relationships among members in a protein complex or enzyme complex. They can thus be regarded as equivalent to the "group" relationship in KGML. This strategy works well for most GPML files; but, for some individual files, there is simply no "graphID" in the file and, only positional information of each entry is recorded. This causes difficulty in retrieving the corresponding gene relationships. Attempts have been made to retrieve the pair-wise relationships based purely on the positional information; but these attempts also introduce a substantial amount of noise. Therefore we do not use this noisy information.Retrieving gene relationships from BioPAX files is mainly by using the Paxtools Java programming library  in combiConverting the relationship of genes in complexes (groups) into binary gene pair relationships may not be the most ideal format for some users, who wish to refer to the original protein complexes information in pathways. For KEGG and WikiPathways we also maintain a "group-gene list" which specifically retains the original format of genes in the groups. The groups in this "group-gene list" are not integrated, as we have done for the pathway-gene and pathway-gene pair relationships, since maintaining this "group-gene list" is mainly to prevent information loss and to give users more precise original information that may not be easily reconstructed from the integrated pathway-gene pair relationships. We normalize the gene IDs in the "group-gene list" to IntPath unified gene IDs and store the list in a simple text file. Users can download this list along with other pathway-gene and pathway-gene pair relationships in the form of compressed text files from IntPath.Normalization of gene names is done using gene ID mapping files downloaded from a variety of databases including NCBI , KEGG 11, UniProAfter we have obtained the pathway-gene and pathway-gene pair relationships from different pathway databases, agreement among the databases can be analyzed. These agreement analyses are crucial for the downstream applications of IntPath. We examine the agreement among the different pathway databases in three aspects: (i) agreement of genes and gene pairs in different databases, (ii) agreement of the pathways in different databases, and (iii) agreement of genes and gene pairs of the same pathway in different databases.After normalization the statistics about pathway number, gene number and gene pair number in each of the source databases can be found in Table To analyze the agreement of the pathways in different databases, we only look at the pathway names in different databases, and calculate how many pathways two databases have in common. To find similar pathway names, we implement a "Longest Common Substring" algorithm. Our program can detect similar pathway names very accurately; detailed techniques will be explained in the following section. In this analysis we only search the related pathway between databases rather than within databases. The results are presented in Figure The two experiments above are analyses at the database level. Next, to analyze--at the pathway level--the agreement of genes and gene pairs of the same pathway in different databases, We calculate the overlap of the genes and gene pairs in the chosen pathway in different databases. The results are summarized in Table From the analyses above, we realize the lack of comprehensiveness and consistency of different pathway databases at both the database level and the pathway level. Hence, we should use the integrated information from all the databases rather than rely on any single source. The inconsistent referrals to pathway names further strengthen the necessity of integrating the pathway-gene and pathway-gene pair relationships from different databases into one unified and comprehensive information source.To find all related pathways both among and within databases which have inconsistent referrals to pathway names (both name variations and different levels of emphases), we implement a refinement of the Longest Common Substring (LCS) algorithm to identify related pathway names. LCS was shown by  to be sux and y), we iterate each name in the list x and search against all the names in the list y; for each name in x, we report the best hit in y. "Best hit" means that, for each name from x, when searched against all the names in y, the one that gets the highest alignment ratio is reported as the best hit for this round. We do not use the alignment score to report best hits because the alignment ratio proves to perform better. This is because some related pathway names do not have very high alignment scores due to the short length of two strings, but the similarities of the two strings can be revealed accurately by the high alignment ratio when compared to other not-so-similar long strings in a single round of search. For example, suppose the name Xa is very short and is searched against the name list y. Suppose there is a very similar short name Y a which aligns all characters in Xa except one character. Suppose there is also a very different but long name Y b which aligns all characters in Xa. It is obvious that the alignment score Xa - Ya is lower than the alignment score Xa - Yb, while the alignment ratio of Xa - Ya is the higher of the two. Thus using the alignment score to report the best hit is not as good as using the alignment ratio.The common LCS algorithm based on dynamic programming works like this: when comparing two strings, the more similar they are, the higher alignment score they have. In our program, the alignment score is the number of aligned characters. We also compute the alignment ratio, which is two times the alignment score divided by the sum of the length of the two strings. To identify the related pathway names in two databases' pathway name lists  conditions to be satisfied:From many background experiments, we realize that relying only on best hits can result in some noise, since many pathway names in the list >the length of shorter string - 1 & alignment ratio >= 0.5, or1. Alignment score >0.912. Alignment ratio Combined with this additional requirement, our program achieves high precision and recall in identifying related pathway names. Nevertheless, a small number of pathways which do not describe the same pathway, but have very similar names, are still incorrectly identified by the methods described above as related pathways. "VEGF signaling pathway" and "EGFR1 Signaling Pathway", "T Cell Receptor Signaling Pathway" and "B Cell Receptor Signaling Pathway", etc. are examples of this kind of mismatches. Our approach to solve this problem is by using a "error-prone words pair list" to filter potential mismatches. For example, if in a candidate related pathway pair, one pathway name has one partner of an "error-prone words pair"(EGFR1) and the other pathway name contains the other partner in the "error-prone words pair"(VEGF), this pair of candidate related pathways is discarded by our program. This approach successfully gets rid of mismatched pathways without compromising the identification of related pathways. Although a little manual curation is needed for initializing the "error-prone words pair list", the curation work load is much less after the first time, since only a few changes or supplementations of "error-prone words pair list" are needed when processing different groups pathway names. Moreover, it is suitable for many different pathways in different organisms.We run our program to compare pathway names within each database and between the databases. After obtaining all the related pathways, our program uses a disjoint set data structure to store all the identified related pathways and then groups together all the related pathways under a general pathway name. The general pathway name is chosen as the shortest pathway names from among the identified related pathways. The shortest pathway name is usually suitable to be the name of the integrated pathway. However, in some cases, the shortest name contains "suffix" or "prefix"--like "I", "II"--that causes the integrated pathway name to give the wrong idea of describing only a specific aspect of the integrated pathway. So our program removes such suffixes and prefixes when generating integrated pathway names. In addition, there are also a small number of cases where several similar pathways are included in one pathway name--an example is shown in the last row of Table IntPath is developed using JSP and MySQL. The web service is created and published using AXIS2.In order to overcome the limitation of incompatible data formats, we directly extract from the XML files  of each pathway database and obtain the gene relationships. To deal with inconsistent molecular representations, we normalize the gene representations into a unified gene ID. The IntPath unified gene ID (which adopts a set of the most commonly used gene names) is compatible with the gene names used in most public repositories. A summary of the number of pathways, genes and gene pairs from different databases after normalization is given in Table After obtaining the normalized pathway-gene and pathway-gene pair relationships, we are able to analyze the comprehensiveness and agreement among the different pathway databases on different aspects.The results from analyzing the overlap of genes and gene pairs in different databases are presented in pie charts in Figures From the results on the overlap of the pathways in different databases we can see there is also a strikingly low overlap of pathways among the different databases; see Figures Zooming in from the database level to the individual pathway level, we analyze the agreement of genes and gene pairs of the same pathway in different databases. The results are listed in Table The comparative analyses from the above three aspects clearly exhibit the incomprehensiveness and inconsistency among the pathway databases. This suggests that the integration of the extracted and normalized information from different databases into a unified and comprehensive resource is very necessary.The results above demonstrate that relying only on a single source of pathway information from any of the databases is not reasonable. Moreover, we have also discovered the problem of inconsistent referrals to pathway names. Table In the integrated pathways, all the related pathways with inconsistent names should be merged. (i) The inconsistent referrals to pathway names are partially caused by the different levels of emphases on the same pathway in different databases. One database (BioCyc) may emphasize on some very specific aspects of a certain large pathway; so this large pathway is broken up in this database into different pathways with similar/related names, yet all describing the detailed aspects of the original large pathway; see Table For the reasons listed above, we should merge all the related pathways under the same general name into one comprehensive pathway (among and within databases). And after merging, we should use the general pathway name which is usually the shortest name among all the comparing pathway names.The results of identified related pathways both within and among databases are summarized in Table We accomplished in IntPath the integration of pathway-gene and pathway-gene pair relationships, achieving compatible data formats, consistent molecular representations, consistent relationship representations, consistent referrals to pathway names and comprehensive data.The web interface of IntPath comprises the following parts: Home, Gene List Analysis Tools , API Toolkit, Statistics, Tutorial, and Download. In order to facilitate convenient access of IntPath data through local programs, the API functions are also supported by IntPath web service. An overview of the IntPath system is shown in Figure Home: It is to introduce the objective of IntPath, what the major contribution of this database is and what the specific problems that we wish to solve through this database are. We also indicate the analysis tools supported in this database, the publications related to these analysis tools, and which species are currently included in our database. This Home page of IntPath is a summary of the general information of the database.Identify Pathways: The function of "Identify Pathways" uses the hyper-geometric test to find the most significant pathways given an input gene list. Through this tool, users can have a clear insight of which pathway is most related to the input gene list. For each result returned, details like p-value are also given.Analyze Distances: The function of "Analyze Distances" is to tell the similarities between the two input gene lists from a pathway perspective. To perform the distance analysis, first the hyper-geometric test is used to find the most significant pathways of the two input gene lists, then the Floyd-Warshall algorithm is used to calculate the "distances" between the two pathways. STRING PPI datasets (version 9.0) is used in the distance calculation between two pathways in the current version of IntPath (V2.0). The "distances" provide a reference in telling the relationships between two specific pathways, and it can be very useful, e.g., in identifying how "far" it will take for a normal pathway to transform into a diseased pathway. For a detailed explanation of "Analyze Distances" and its application in biomedical research, please refer to methods described in .Statistics: This statistics section gives users an overall insight of IntPath. Users can easily get the following statistics: number of genes, number of gene pairs, number of integrated pathways, number of original KEGG pathways, number of original WikiPathways pathways, number of original BioCyc(MouseCyc) pathways, and number of source databases. The default option is "All statistics" which displays all the statistics listed above.API Toolkit: We provide powerful as well as flexible API functions of our IntPath database. Users can both call the API functions using their local programs through IntPath's web service or using API functions by directly retrieving information through IntPath's web interface. The following API functions are supported, getGeneID, getDBPathways, getPathway, getPathwayGenes, getGenePathways, getPathway-Interactions, getPathwayDifference, getIntPathGenes, getIntPathGenePairs and getIntPathPathways. The explanation and user guide of each API function can be found in the Tutorial page.Download: Some users may have other requirements of data analyses that are not met by IntPath in the current version. Some users may also have different application purposes of IntPath. To cope with a variety of needs, we release all our IntPath data in this "Download" section, where users can obtain all IntPath data in two different formats: (1) text format (*.txt), this compressed package includes three text files, (a) the integrated pathway-gene relationships, (b) the integrated pathway-gene pair relationships and (c) the normalized group-genes list; and (2) sqldump format (*.sql), which is based on the integrated data we have prepared and stored in 6 tables in each sqldump (each organism is a separate sqldump).The "wiki-style" of WikiPathways makes this database more casual than other databases. It is good for the community to freely maintain and share knowledge through WikiPathways. On the other hand, it causes many problems for automatic information retrieval. One of the limitations is the slight inconsistency among the formats of GPML as mentioned before--some key tags can be upper or lower cases. GPML is more different from other XML formats. GPML emphasizes more on pictorial information; therefore, most of the objects on the file are more likely to be recorded for their positional information. Worse, some GPML files even do not have a "graphID" record; and for these GPML files, whole information of certain pathways is given by the positional information on the pathway map. For these GPML files, judging the relationships between two genes is solely dependent on the positional information. It may be easy for the human eye to look at the pictorial format of the pathway map; but it is hard for computer programs to retrieve accurate information automatically. Attempts on spatial clustering have been made. But these attempts also introduce a substantial amount of noise. Therefore we decide to discard this noisy information at the current stage.Recently, WikiPathways begins to support web service and BioPAX. We have tried solving the problem mentioned above using WikiPathways web service and directly extracting from BioPAX format; but no improvement has been achieved.Web service has not solved the problem of those GPML files that do not have "graphID" record. For example, our program fails to extract reliable gene relationships from the pathway "Mm Androgen Receptor Signaling Pathway WP252_35669" by calling the WikiPathways API function "find Interactions". It is supposed to find interactions defined in WikiPathways' pathways. In our experiments, it works in finding interactions in other pathways. Extensive experiments have been made using different ways to call the "find-Interactions" function. Yet nothing related to the WP252 pathway is returned. On the png graph we can see there are lots of interactions in this WP252 pathway. These kinds of experiments have been attempted many times on several pathways. All have failed to find the "interactions" or gene relationships in the specific pathways that lack "graphID" entry.We turn to BioPAX files which have recently been supported on WikiPathways for a solution. We specifically run our program on the pathway BioPAX files whose corresponding GPML files do not have "graphID" records. Our program successfully retrieved gene relationships from BioPAX files in BioCyc and MouseCyc, but not on these specific BioPAX files in WikiPathways . We also try to visualize those specific BioPAX files on Cytoscape; but no relationship can be visualized from these files.The gene ID problems in WikiPathways is also quite serious. There are two places to retrieve gene ID information from the GPML "</DataNode>" entry, one is from "TextLabel" and the other is from "<Xref Database"IDs. Usually gene IDs in "TextLabel" are gene symbol, while gene IDs in "<Xref Database" can be the gene IDs from different public databases, like Entrez, Ensembl, UniProt, and so on. Getting gene ID information from both of these two fields is necessary. It is not uncommon for the WikiPathways database to have errors and problems in both fields. In most cases, erroneous gene IDs from "TextLabel" also do not have any information in "<Xref Database". The erroneous gene IDs can be gene symbols or EC numbers that cannot be found in the target organism to which the pathway map belongs; they can also be common gene names without any information in "<Xref Database", or they can be just upper- or lower-case flaws. In our program, both information from the two fields,"TextLabel" and "<Xref Database" are retrieved. For gene IDs where information from both of these fields are problematic, manual curation is adopted to deal with them, generally by removing them from IntPath.http://compbio.ddns.comp.nus.edu.sg:8080/IntPath. As some studies are already using data in IntPath [IntPath and all its data have been released online at  IntPath , we beliIntPath heavily depends on source pathway data from all the pathway databases and most databases update quite frequently. The important question is: Can we keep our data updated in a timely fashion? The answer is: Yes.S. cerevisiae, M. tuberculosis H37Rv, H. Sapiens and M. musculus--and we will include more in future releases of IntPath. Extending the methodology to include other organisms just needs modifying the regular expressions for extracting GPML and KGML files; preparing the gene ID mapping files; manually correcting some possible errors of the gene IDs introduced by the source databases (like WikiPathways gene ID problems) and, if necessary, updating the "error-prone words pair list"; and reviewing integrated pathway names. Therefore, the whole process of including other organisms in IntPath takes a short time. We will include more model organisms and important pathogens in IntPath in future releases.The "IntPath Data Preparation" program is streamlined and automated in performing the extraction, normalization, integration processes and directly outputing into MySQL databases and text files. For organism already included in IntPath, running the program for each update takes a short time; and we will maintain a regular update of IntPath in the long term. Another key question is whether we can extend our approach to other organisms. Currently, we have already included four organisms--In the near future, more functions and analysis tools will be supported in IntPath--for example, clustering algorithms for microarray studies using the IntPath data as background knowledge, visualization tools of interaction and relationship, more powerful algorithms to identify pathways given user-specified input gene lists, and more API functions. Moreover, in this version of IntPath we only take gene relationships into account; in a future version, IntPath will also consider other important relationships in the pathways--like protein-compound relationships, compound-compound relationships, and so on. Meanwhile, in future releases, more organisms will be included. We wish our continuing effort can make IntPath one of the most useful databases in pathway studies that can benefit a variety of related researches.The five limitations of current pathway databases that hamper effective use of pathway information have been overcome in this work. We solve the problem of incompatible data formats in different databases by extracting the pathway-gene and pathway-gene pair relationships. The limitations of inconsistent molecular representations and inconsistent molecular relationship representations have been overcome by our normalization of the data into common gene name representations and common relationship types which are compatible with other database. The problems of inconsistent referrals to pathway names and incomprehensive data from different databases have been solved by the integration of pathway-gene and pathway-gene pair relationships into a unified and comprehensive data source.H. sapiens, S. cerevisiae, M. musculus and M. tuberculosis H37Rv. IntPath can maintain a regular update in these organisms and, the methodology we describe here can be applied to other organisms straightforwardly.We achieve compatible data formats, consistent molecular representations, consistent relationship representations, consistent referrals to pathway names and comprehensive data in our IntPath database for several organisms--viz., We believe IntPath will not only facilitate convenient access of the integrated pathway gene relationship data for model organisms and important pathogens but also greatly boost data analysis and application to many related studies through the analysis tools and API functions provided in the database.The authors declare that they have no competing interests.This work was jointly conceived, planned, and written up by Limsoon Wong and Hufeng Zhou. The analytical experiments were performed by Hufeng Zhou. The IntPath data were prepared by Hufeng Zhou. The IntPath database web interface was designed and implemented jointly by Hufeng Zhou, Jingjing Jin, Bo Yi, Haojun Zhang as follows. Hufeng Zhou prepared the relational databases of all the included organism's data in MySQL. Jingjing Jin provided the draft codes of Analyze Distances, Identify Pathways and parts of API functions of IntPath web interface supporting PathwayAPI data. Hufeng Zhou improved and rewrote the draft codes provided by Jingjing Jin, except the codes of the Floyd-Warshall algorithm. Hufeng Zhou implemented the rest parts of the IntPath web interface. Hufeng Zhou and Haojun Zhang identified the seralizable-file bug of the draft codes and, together improved the web interface response speed of IntPath API online Toolkit. Hufeng Zhou prepared and collected protein-protein interaction data and the serializeable files for all the included organisms. Hufeng Zhou and Bo Yi developed the IntPath web service. Hufeng Zhou and Michal Wozniak configured the draft codes for Linux and identified missing packages and bugs of the draft codes.


Identification of canonical pathways through enrichment of differentially expressed genes in a given pathway is a widely used method for interpreting gene lists generated from high-throughput experimental studies. However, most algorithms treat pathways as sets of genes, disregarding any inter- and intra-pathway connectivity information, and do not provide insights beyond identifying lists of pathways.We developed an algorithm (PathNet) that utilizes the connectivity information in canonical pathway descriptions to help identify study-relevant pathways and characterize non-obvious dependencies and connections among pathways using gene expression data. PathNet considers both the differential expression of genes and their pathway neighbors to strengthen the evidence that a pathway is implicated in the biological conditions characterizing the experiment. As an adjunct to this analysis, PathNet uses the connectivity of the differentially expressed genes among all pathways to score pathway contextual associations and statistically identify biological relations among pathways. In this study, we used PathNet to identify biologically relevant results in two Alzheimer’s disease microarray datasets, and compared its performance with existing methods. Importantly, PathNet identified de-regulation of the ubiquitin-mediated proteolysis pathway as an important component in Alzheimer’s disease progression, despite the absence of this pathway in the standard enrichment analyses.http://www.bhsai.org/downloads/pathnet/.PathNet is a novel method for identifying enrichment and association between canonical pathways in the context of gene expression data. It takes into account topological information present in pathways to reveal biological information. PathNet is available as an R workspace image from High-throughput technologies enable the study of biological processes at the systems level. However, analyzing the large amount of data generated by high-throughput techniques and translating these data into biological knowledge is currently a critical bottleneck in systems biology. To study a disease at the system level, DNA microarrays are routinely used to provide a comparison of gene expression patterns in control vs. disease conditions. Because this comparison usually reveals a large number of differentially expressed genes, it is difficult, if not impossible, to analyze the effect of each gene individually. In addition, high-throughput data often contain considerable noise, making individual or isolated gene observations less likely to be relevant. Using statistical methods to summarize the data can help reduce noise and increase the reproducibility of the results. HoweverThe most commonly used methods for summarizing gene expression data rely on enrichment analysis of differentially expressed genes to identify and rank Gene Ontology (GO) terms and canonical pathways in order to characterize the underlying biological nature of the data. Comprehensive reviews of these approaches are available-4. WhileWell-studied canonical pathways provide extensive information about how the genes and gene products interact and regulate each other. However, most of the pathway analysis methods, including the hypergeometric test and GSEA, treat pathways as lists of genes and do not take into account the connectivity information embedded within the pathway. More recently, some studies-9 have iPathways based on Network information (PathNet). To incorporate inter-pathway connectivity, we combined KEGG pathways (from http://www.kegg.com) to create a pooled pathway. For enrichment analysis, PathNet first identifies the association of each gene with a disease (referred to as direct evidence) by comparing gene expression data in control patients vs. patients with the disease. Then, PathNet identifies the association of each gene’s neighbors with the disease (referred to as indirect evidence) based on the inter- and intra-pathway connectivity information present in the pooled pathway. Finally, PathNet combines the direct and indirect evidences to obtain the significance of the combined evidence. Based on the statistical significance of the combined evidence for all genes, PathNet uses the hypergeometric test to uncover the pathways associated with the disease.In this study, we have attempted to address these issues by developing an algorithm for examining pathway enrichment that uses differential gene expression (or other molecular profiling data) to analyze et al.[et al.[et al.[pooled pathway. This association of specific pathways, beyond the mere overlap of genes annotated as belonging to more than one pathway, can reveal otherwise hidden pathway dependencies  that are not directly attainable from enrichment analysis alone.As genes in pathways function in a coordinated fashion, association studies between pathways in the context of gene expression data can unravel the underlying complexity of biological processes. Li et al. proposedl.[et al. identifi.[et al. estimateTo illustrate the utility of PathNet, we applied it to two AD microarray datasets and analyzed the results in the context of existing knowledge. In addition, we show how the statistical scores of the associations between pathways through gene expression data facilitated the identification of a biological association between the AD pathway and ubiquitin-meditated proteolysis pathway.pooled pathway, and the R package, named ‘An interface to the BOOST graph library,' from Bioconductor (http://www.bioconductor.org/packages/rel-ease/bioc/html/RBGL.html) was used to convert this information into the adjacency matrix (A). The adjacency matrix is a non-symmetric square matrix, where the number of rows (and columns) represents the number of genes present in the pooled pathway. The diagonal elements of matrix A were set to zero to exclude self-interactions. The non-diagonal element Aij represents the directed KEGG protein interaction between nodes i and j:Pathways from the KEGG database availabli to node j and another from node j to node i. Although the bulk of the genes annotated in KEGG pathways are present on most microarray chips, about 10% of the genes are typically missing. In order to only include information derived from experimental data, we re-constructed the adjacency matrix for each chip-set by deleting rows and columns of genes that were not examined experimentally. In order to be consistent in the analysis presented below, we also redefined the pooled pathway for each chip-set to include only genes for which experimental data exists. PathNet automatically carries out this step from the input files.In the case of a bidirectional interaction, two edges are introduced, one from node i between two experimental conditions (control and disease), while indirect evidence considers the differential expression of the neighbors of gene i in the pooled pathway. The nominal p-values associated with the direct and indirect evidences of each gene were combined to obtain the p-value of the combined evidence, which is subsequently used for the pathway enrichment analysis.PathNet combines two types of evidence for pathway enrichment analysis, referred to as direct evidence and indirect evidence Figure. Direct iD) in order to gauge whether the average expression of gene i was different between the two experimental conditions. The lower the pD-value, the more likely it is that the observed difference in gene expression is significant. Alternative methods, such as SAM[D.We used the t-test to calculate a nominal p-value for the direct evidence , which incorporates the topological information of the pathways. This score captures a weighted level of differential expression of the neighbors of gene i, and is calculated using the following equation:To ascertain the significance of the indirect evidence, we need to test whether the expression of each neighbor of gene pooled pathway, Aij is defined as in Eq. (1), and pjD denotes the nominal p-value of the direct evidence for gene j which is used to assign the weight of the contribution. The nominal p-value associated with the indirect evidence (piI) was inferred by testing if the observed score SIi was greater than the corresponding random values created by shuffling the pjD-values in the pooled pathway. In each of the N shuffles, all pjD-values were scrambled by randomly re-assigning their indices. As the connectivity in the pooled pathway remained fixed, for each gene i in the nth shuffle, we calculated the corresponding random score SIiR(n). Next, for each gene i, we formally re-constructed the probability density distribution function for the random scores piR. Practically, we estimated the piI-values by counting the number of random scores larger than the actual scores, as follows:where G denotes the set of all genes present in the iI-values are integer multiples of 1/N, we cannot accurately estimate piI-values if they are less than 1/N. To address this issue, we assigned 1/N as the minimum piI-value. The lower the piI-value, the more likely it is that the observed weighted gene expression pattern around gene i is not a random pattern.In our calculations, we used N = 2,000 shuffles. As the estimated piC) for each gene i by using Fisher’s method[iD and piI). Previous studies[D- versus pI-values for the datasets we used and there was no obvious dependency of these values on each other. We also verified that the set of pD- and pI-values were linearly independent for all comparisons by calculating a non-significant correlation coefficient in each test set. Accordingly, for gene i, the two probabilities were combined based on Fisher’s method, using the following equation:We obtained the p-value of the combined evidence  denotes the probability density function of the χ2 distribution with 4 degrees of freedom. Note that, even if the pD- and pI-values were correlated, they could still be combined using a modified version of Fisher’s method[where P, and pi/jD denotes the nominal p-value of the direct evidence for gene i/j used to construct the weight for each Aij value. Note that as Aii ≡ 0, the SCαβ does not contain self interactions and only includes gene pairs that have been connected to each other via the pooled pathway. The formulation uses only the pD-values associated with the direct evidence and not the pC-values, which already contain pathway information via the indirect evidence as calculated in Eq. (2). A higher SCαβ indicates a stronger contextual association between the pathways.where gαβ greater than expected by chance alone, we followed the same procedure used to estimate the p-values for the indirect evidence. The p-value associated with the SCαβ (pαβ) was inferred by testing if the observed score SCαβ were greater than the corresponding random values created by shuffling all the pD-values in the pooled pathway N times. With the connectivity in the pooled pathway fixed, for each pathway pair α and β in the nth shuffle, we calculated the corresponding random score SCαβR(n). We then formally re-constructed, for each pathway pair α and β, the probability density distribution function for the random scores PαβR. Finally, we estimated the pαβ-values by counting the number of random scores larger than the actual scores for each pathway pair:To evaluate the probability of finding a SCN = 2,000 shuffles to estimate the pαβ-values. The lower the pαβ-value, the more likely it is that the observed weighted gene expression pattern connecting pathways α and β are not a random pattern.We used β overlap, based on common genes between the pathways. This information is only based on the KEGG database and is not dependent on gene expression data, i.e., we used the full complement of KEGG genes to estimate this overlap. The hypergeometric test was used to estimate if the observed overlap was statistically significant.We also tested the extent to which the genes from pathways α and http://www.ncbi.nlm.nih.gov/geo/) and involved AD-related studies. The first dataset (GEO ID: GDS810)[disease progression dataset, investigated the expression profile of genes from the hippocampal region of the brain as a function of the progression of the disease . We refer to the second dataset[brain regions dataset. This dataset examined the effect of AD in six different brain regions: the entorhinal cortex, hippocampal field CA1, middle temporal gyrus, posterior cingulate cortex, superior frontal gyrus, and primary visual cortex (GEO ID: GSE5281). Because different regions of the brain are involved in controlling different biological processes, this dataset can provide insights into the tissue-specific activation of pathways. The entorhinal cortex region samples were obtained from patients in the early stages of AD, while the remaining samples were obtained from patients in the later stages of the disease.We evaluated the performance of the PathNet algorithm using two microarray datasets generated by two different research groups. Both datasets were downloaded from the National Center for Biotechnology Information’s Gene Expression Omnibus (GEO) database ( GDS810), which w dataset as the bdisease progression dataset, the expression of each gene in patients with incipient, moderate, and severe disease was compared with control patients using the t-test. In the brain regions dataset, gene expression was compared between diseased and control patients for each brain region. We applied the proposed pathway enrichment method for each of these nine comparisons (three from the disease progression dataset and six from the brain regions dataset).In the http://www.broadinstitute.org/gsea/index.jsp) and Bioconductor (http://www.bioconductor.org) Web sites, respectively. For GSEA, we used the provided Java-version of the program with a pre-ranked gene list. To ensure the comparability of results, we used the same version of the KEGG pathways (downloaded in November 2010) for all comparisons. Finally, to account for multiple comparisons, we corrected the pathway enrichment p-values for family-wise error rate  and used a significance threshold of 0.05 for all comparisons. The results of all nine comparisons using each of the four pathway analysis methods are provided in Additional fileWe used PathNet to identify the enrichment of pathways in each of the nine comparisons described above. We also compared the results of PathNet with three existing algorithms for pathway analysis that are currently in wide use: the hypergeometric test; gene seFWER. Figuredisease progression dataset, none of the methods could identify significant enrichment in the AD pathway during the early (incipient) stages of the disease. As the disease progresses, the significance of the enrichment increased in all four methods. During the late (severe) stages of the disease, three of the four methods could identify significant enrichment in the AD pathway. Notably, at moderate stages of the disease, only PathNet was able to determine that the AD pathway was significantly enriched in AD patients.Our primary aim was to determine if these methods could identify whether the AD pathway (KEGG ID: 5010) is significantly enriched in AD patients vs. control patients. Figurebrain regions dataset, all of the methods could identify significant enrichment of the AD pathway in the middle temporal gyrus region and posterior cingulate cortex regions, however, none identified AD enrichment in the entorhinal cortex or superior frontal gyrus regions  for the AD pathway.In the s FigureB. One pldisease progression dataset, with samples taken from the hippocampus region of the brain, and the results in the brain regions dataset, with samples from the hippocampal field CA1. PathNet correctly identified most of these pathways as significantly enriched while the other three methods failed to do so. The complete set of results is provided in Additional fileTo test the sensitivity of PathNet with respect to the other three pathway analysis methods, we compared the enrichment levels of seven pathways that have been frequently associated with AD in the literature. Tableshows that in six out of the nine comparisons where the AD pathway was enriched, we analyzed pathways co-enriched with the AD pathway. Eight pathways were co-enriched with the AD pathway in five or more of the six cases. Of these eight pathways, six were related either to AD  or to other neurological diseases (Parkinson’s disease and Huntington’s disease). Both the Parkinson’s disease pathway and the Huntington’s disease pathway show significant overlap with the AD pathway, which explains why they were frequently co-enriched. There is evidence in the literature to support the association of each of these co-enriched pathways with AD. This qualitatively implies that most of the significantly enriched pathways identified by PathNet are unlikely to be biological false positives.To test the specificity of PathNet, we investigated the biological relevance of pathways co-enriched with the AD pathway. Table2disease progression dataset were collected from the hippocampal field CA1 region. Similarly, the brain regions dataset provides results of samples for patients with severe disease with samples also collected from the hippocampal field CA1 region. Therefore, the data from these two samples, collected in the hippocampus for severe AD patients, should be comparable and the overlap of their significantly enriched pathways can be considered as a measure of the quality of the pathway analysis methods. FigureThe samples from the 1) identify the AD pathway as significant in cases where the existing methods failed; 2) detect significantly enriched pathways that are known to be biologically relevant to AD; and 3) detect a higher level of significance in overlap of the enriched pathways in two independent datasets that are expected to be comparable.In summary, we compared the results obtained when using PathNet for pathway analysis vs. the results obtained with three existing widely used methods. We found that PathNet was able to: disease progression data, we randomly shuffled the gene names 1,000 times and estimated the pFWER values for 130 pathways from PathNet. The randomization of gene names ensures that the direct evidences and number of differentially expressed genes in the shuffled data is the same as in the original data. The distribution of pFWER values given in Additional fileFWER values were equal to 1. The false positive rate of PathNet at a pFWER cutoff of 0.05  was 0.02. We further investigated if the difference in pathway topology contributes to variations of false positive rates among pathways. We calculated false positive rates for each pathway from 1,000 random shuffles and plotted the distribution of false positive rates for 130 pathways , where PathNet identified the AD pathway as statistically enriched. The results from all comparisons are provided in Additional fileWe used PathNet to identify pathway associations in each of the two AD datasets described above. Because we are interested in analyzing datasets related to AD, we specifically analyzed pathways that have statistically significant contextual association with the AD pathway. We focused on six comparisons  enrichment analysis using PathNet performed better than the three existing pathway analysis methods in identifying biologically relevant pathways, 2) contextual pathway-pathway analysis can reveal biological insights that may not be obtained from enrichment analysis alone, and 3) the enrichment of pathways associated with AD changes with disease progression.In this study, we developed PathNet, a method for pathway analysis based on high-throughput molecular profiling data, using inter- and intra-pathway connectivity information. PathNet calculates both pathway enrichment and contextual associations between pathways. We have shown that PathNet was able to identify the AD pathway and other biologically relevant pathways in multiple scenarios while three other widely used pathway analysis methods  often failed to do so. PathNet also identified pathways contextually associated with the AD pathway. Literature studies support the biological relevance of the results identified using PathNet.The existing methods used for pathway enrichment consider each pathway as a separate entity. In contrast, PathNet considers both inter-pathway and intra-pathway connectivity for pathway enrichment. This connectivity information, in the form of a significance-level weighted gene-gene connection, corroborates and strengthens the direct evidence of differential gene expression readily derived from microarray data when a gene’s neighbors on the pathway are also differentially expressed. The method properly accounts for highly connected genes that are part of multiple pathways via comparison with the appropriate probability density function generated from topology-preserving randomized data. The unbiased nature of this method was confirmed by the estimated low false positive rates. However, if no connectivity information is available for a gene, PathNet still includes the microarray-derived evidence for identifying pathway enrichment. This ensures that we do not penalize genes that have no information available regarding their connectivity.In PathNet, indirect evidence of a gene is calculated based on gene expression levels of its neighbors using Eqs. (1–3). Hence, indirect evidence of the gene cannot be estimated if neighboring gene expression is not measured in the microarray analysis. In such cases, the combined evidence of a gene is replaced with the direct evidence. In the limiting case where none of the genes’ neighbors expression levels are measured, PathNet converges to a standard hypergeometric test.Currently, there is no gold standard for quantitatively testing and comparing the performance of pathway enrichment methods. As an alternative, we have selected a well-studied disease , where considerable amount of knowledge already exists about the deregulation of its biological processes and multiple high-quality microarray datasets are available, to examine important aspects of the disease. This allowed us to assess the performance of PathNet based on an in-depth analysis of the biological relevance of the results, directly compare its performance with other existing pathway enrichment methods, and ascertain each method’s ability to retrieve the relevant biological information.Software name: PathNetDownload site:http://www.bhsai.org/downloads/pathnet/Operating system: Platform independentLicense: GPL version 3Programming language: R version 2.14.1 or laterAβ: Beta amyloid; AD: Alzheimer’s disease; EC: Entorhinal cortex; GEO: Gene expression omnibus; GSEA: Gene set enrichment analysis; GnRH: Gonadotropin releasing hormone; GO: Gene Ontology; HIP: Hippocampal field CA1; KEGG: Kyoto encyclopedia of genes and genomes; MTG: Middle temporal gyrus; NFTs: Neurofibrillary tangles; NTRs: Neurotrophin receptors; PC: Posterior cingulate cortex; PPI: Protein-protein interaction; SFG: Superior frontal gyrus; SPIA: Signaling pathway impact analysis; VCX: Primary visual cortex.The authors declare that they have no competing interests.BD, AW, and JR conceived of the algorithm. BD implemented the algorithm, performed the study, and wrote the first draft of the manuscript. All authors contributed to the manuscript writing and approved the final manuscript.KEGG directionality assignments. This file gives the types of edge directionality used in the KEGG pathway.Click here for fileScatter-plots of direct and indirect evidences. A figure showing the relationship between direct and indirect evidences for the nine different comparisons used in this work.Click here for fileHypergeometric test and PathNet results. An Excel spreadsheet of the results of all nine comparisons using the hypergeometric test and PathNet.Click here for fileGSEA results. An Excel spreadsheet of the results of all nine comparisons using GEAS.Click here for fileSPIA results. An Excel spreadsheet of the results of all nine comparisons using SPIA.Click here for fileRandomized distributions of pFWER. Distribution of pFWER from PathNet derived from the null distribution scenario and obtained from data randomization.Click here for fileEstimated false positive rate. Distribution of estimated false positive rates based on an analysis of all pathways.Click here for fileContextual AD pathway association. An Excel spreadsheet of the pathways identified to have a statistically significant contextual association with the AD pathway.Click here for file