Proteomics Signature Profiling (PSP) is a novel hit-rate based method that proved useful in resolving consistency and coverage issues in proteomics. As a follow-up study, several points need to be addressed: 1/ PSP’s generalisability to pathways, 2/ understanding the biological interplay between significant complexes and pathway subnets co-located on the same pathways on our liver cancer dataset, 3/ understanding PSP’s false positive rate and 4/ demonstrating that PSP works on other suitable proteomics datasets as well as expanding PSP’s analytical resolution via the use of specialised ontologies.1/ PSP performs well with Pathway-Derived Subnets (PDSs). Comparing the performance of PDSs derived from various pathway databases, we find that an integrative approach is best for optimising analytical resolution. Feature selection also confirms that significant PDSs are closely connected to the cancer phenotype.2/ In liver cancer, correlation studies of significant PSP complexes and PDSs co-localised on the same pathways revealed an interesting relationship between the purine metabolism pathway and two other complexes involved in DNA repair. Our work suggests progression to poor stage requires additional mutations that disrupt DNA repair enzymes.3/ False positive analysis reveals that PSP, applied on both complexes and PDSs, is powerful and precise.4/ Via an expert-curated lipid ontology, we uncovered several interesting lipid-associated complexes that could be associated with cancer progression. Of particular interest is the HMGB1-HMGB2-HSC70-ERP60-GAPDH complex which is also involved in DNA repair. We also demonstrated generalisability of PSP using a non-small-cell lung carcinoma data set.PSP is a powerful and precise technique, capable of identifying biologically coherent features. It works with biological complexes, network-predicted clusters as well as PDSs. Here, an instance of the interplay between significant PDSs and complexes, possibly significantly involved in liver cancer progression but not well understood as yet, is demonstrated. Also demonstrated is the enhancement of PSP’s analytical resolution using specialised ontologies. Proteomics Signature Profiling (PSP) is a novel method for overcoming small sample size, consistency and coverage issues in proteomics . The undThis idea was tested in a published study of Hepatocellular Carcinoma (HCC) patients (5 in moderate and 7 in poor stage) . UtilisiIt is remarkable this is achievable in spite of little inter-patient agreement. To highlight this, in mod-stage patients, only 25 out of over 800 proteins are common to all 5 patients. Of these 25, all are also reported in poor-stage patients with relatively high counts (≥ 4 out of 7 patients) with the exception of HSP90AA2 and TRAP1 (≤ 2 out of 7 patients). In poor stage, only 3 out of over 1000 proteins are common to all 7. Of these, 2 (CLU and CSTB) are reported in mod-stage patients. LYZ or lysozyme, is the only detected protein common to all 7 poor patients and not found in mod-stage patients. The large disparities between reported proteins meant that it was difficult to select potential biomarkers, and also not possible to generate meaningful hierarchical clustering based on detected proteins. Additional file As a follow-up study, we identified four essential points to address: Firstly, we show that pathways can also be used with the PSP approach. While complexes provided a biologically-rich feature set, it is limited. Pathways are also suitable networks on which PSP could be utilised but has several caveats. 1/ Size and topology: Individual pathways vary considerably in size. Suppose that only a small portion of a large pathway is involved, standard statistical tests would fail. 2/ Coverage and consistency issues: Current pathway databases have abysmal agreement even on the most well-studied pathways. We recently developed an integrated pathway database, PathwayAPI , which uSecondly, as pathways are large biological entities with specialised processes, significant PSP-derived complexes and PDSs that co-locate on the same biological pathways, might engage in novel interplays that may account for cancer progression. This is worth examining further.Thirdly, the actual false positive rate needs to be better understood, as it was not studied in the original paper. As PSP uses all detected proteins, the possibility of high false positive rates could be a concern. While we demonstrated theoretically in the PSP paper why false positives are negated, we did not show in actual terms its severity or negligibility.The fourth aim of this study is to demonstrate PSP’s generalisability and to enhance its analytical resolution using specialised ontologies. On the latter, many functional studies are reliant on, but also limited by, the analytical resolution of Gene Ontology (GO). GO was developed for general purposes and, on its own, might not yield specialised/advanced insights especially when a specific biological context---e.g., cancer---is being considered. Here, we use a lipid ontology based on GO for uncovering novel lipid-associated complexes implicated in liver cancer progression. On generalisability, we demonstrate that PSP also works in a non-small-cell lung carcinoma (NSCLC) dataset comparing two subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SCC).et al.[A PDS is a pathway-derived and biologically coherent feature determined by the proteomics data. It is a connected subnet within a biological pathway, which is more likely to function together based on the expression data (See methods for details). There are a number of prior works on extracting subnetworks from (large) interaction databases, e.g., Ideker et al. and Rajaet al.. While iFrom PathwayAPI, 87 PDSs are derived from the non-merged pathways  suggests that merging the pathways (based on the LCS approach) did not make any major change to the overall results. But since slightly less PDSs are produced for merged pathways, they must have been merged into the other PDSs. Suppose PDSx (among the 5 absorbed PDSs) got merged with PDSy (among the remaining PDSs) in some merged pathway P. This must mean that PDSx and PDSy hit two different parts of P that were not contiguous in the un-merged source pathways Px and Py of P. But Px and Py got connected in P after merging.Clustering of the 87 PDSs from non-merged PathwayAPI did reveal they are also capable of segregating the patients into their respective subclasses with high confidence (AU score = 100). The red and green numbers refer to the AU (Approximately Unbiased) and BP (Bootstrap Probability) p-values (between 0 and 100) respectively. Higher values denote higher confidence. Red squares indicate largest possible clusters where the AU p-value is above 95. Additionally, mod patient 203, which was previously found to be anomalous, was once again found in the poor-stage cluster . To illustrate this point, a network was built based on taking a naïve union of all existing pathways in PathwayAPI. There were no isolated components in this highly inter-connected system making it difficult to disambiguate the various constituent pathways. More importantly, attempting to extract PDSs from this results in one super component of size 350, and 3 components of sizes 4, 4 and 6. This lack of informative network features confounds the PSP profiling approach.PathwayAPI (both merged based on the LCS approach and non-merged) outperforms its constituent databases, demonstrating the value of integration  are of lower confidence than KEGG’s, as well as those based on PathwayAPI and real/predicted complexes. The reason for this is due to the number of features (PDSs and significant PDSs) contributed by each database. Altogether, 58 out of 87 PDSs reached significance at p ≤ 5%, indicating they are strongly discriminative between mod and poor stages; see Additional file On the individual pathway databases, KEGG contributes 43 significant PDSs out of 65 PDSs derived from 172 pathways in KEGG, Wiki 6 significant PDSs out of 11 (82 pathways), and IPA 9 significant PDSs out of 11 (45 pathways) respectively  in PathwayAPI. Due to the smaller number of resolving features (PDSs), it is not surprising that Wiki and IPA perform poorer in their ability to resolve the patient samples properly.Given this result, we cannot over generalise on the respective quality of the individual databases. But the key implication here is that one should never defer to the results from one database solely. Obviously, coverage discrepancies between different databases can result in variations of analytical outcomes. It is thus best to refer to an integrated data resource.We use the term “involved” to suggest that a pathway is implicated in HCC if it gave rise to a signficant PDS.There are a total of 159 significant PSP complexes . The significant PDSs originate from 58 different “involved” pathways. The number of non-involved pathways is therefore 241 (299–58). On the other hand, 20% (12 / 58) of the involved pathways overlap with a significant complex while 11% (27 / 241) of the non-involved pathways overlap with a significant complex. Hence, an involved pathway is ~ 2x (20/11) more likely than a non-involved pathway to overlap a significant complex; Additional file Also, none of the 364 (523–159) non-significant complexes overlap with any of the 75 involved pathways, while 10 non-significant complexes overlap some of the 241 non-involved pathways. The propensity for significant complexes to co-localise onto the same pathway with a significant PDS is therefore notably high.To show that the co-localisation effect is not due to the effect of the same proteins being found on both the PSP complexes and PDSs, we looked at the distribution of overlaps between PDS and PSP complexes co-located on the same pathway. In 70% of cases, the Jaccard Score  is much smaller than 0.2, indicating that this was not due to the effect of the shared proteins.The co-locating PDSs and PSP complexes (onto the same pathway) should function in a coordinate manner ; and generally, this is true. The cluster score correlations between co-located PDS and PSP complexes were examined for both mod and poor stage; Figure We examined these two outliers, and found that they corresponded to two different non-overlapping complexes that co-locate on the same pathway (“Purine metabolism”). In the mod stage, the scores for these two complexes are low while the PDS is high. In the poor stage, this disparity is negated: It is high in both PDS and complex. It is possible that this is a switch mechanism that may be involved in the progression from mod to poor stage.et al. [The major site of purine synthesis is in the liver. Interestingly, it is known early on that enzymes involved in purine metabolism play a role in cancer. In fact, an enzymic imbalance, specific only to liver cancer, was known as early as 1983 . HoweverWe decide to investigate further by analysing the functions of the co-localised complexes: “DNA synthesome complex” and “TNF-alpha/NF-kappa B signaling complex 5”. The former is involved in fat signaling , cellular immortality , DNA repair  and cellular proliferation . The latter is involved in stress/immune responses , DNA repair , cell death  and cellular proliferation . It is clear that both complexes are involved in cancer-associated functions, in particular, DNA repair.The associated PDS appears to work primarily on metabolic and purine-related functions, including GO:0009161 ribonucleoside monophosphate metabolic process (P = 0.0001) and GO:0006144 purine base metabolic process (P = 7.9e-09). However, it is also related to differentiation  although myeloid differentiation is typically associated with leukemias, not with liver cancer.Since the expression level of the PDS is both high in mod and poor stage, it is possible that this is an early requirement for oncogenic transformation. Indeed, upsetting nitrogenous base balances can lead to increased chance of error in DNA replications. Our identified PDS suggests that this is the region of the purine metabolism pathway most differentially affected. But in order for oncogenic progression to occur or speed up (for transition from mod- to poor-stage liver cancer), it is logical, and indeed as we find, to target protein complexes that are involved in rectifying mistakes in DNA replication or effecting DNA repair. To further implicate these two out of several other candidate DNA repair complexes, they are closely associated with the pathway as well. In light of these results, the biological relationship between the purine metabolism PDS and the associated PSP complexes should be further explored experimentally.There are 523 biological complexes found in CORUM (size ≥4) used in PSP. At p ≤ 5%, approximately 523 * 5% ≈ 27 false positives are expected. Similarly, there are 87 PDSs, and thus at p ≤ 5%, approximately 87 * 5% ≈ 5 false positives are expected. In order to check the empirical false positive rates against these theoretical estimates, we used the 7 poor-stage patients, randomly assigned them into two groups, and applied PSP analysis. Since both groups were actually poor-stage patients, all significant complexes / PDSs resulting from this analysis should be considered false positives.Figure While the false positive analysis may be more ideally executed by using technical repeats from say, a cell line. We nonetheless show here that these hit-rate based methods utilising biological pathways and complexes are powerful and precise.The liver is a major metabolic center, and a primary regulation site of lipids. Current evidence suggest that lipids are not mere energy-providing metabolites but can effect profound changes via various signaling pathways. This is not limited to cholesterol-based signaling molecules, which are more well known. There are over 2,000 species of lipids, most of which have poor characterisation and not easily analysable . Lipid dMany proteins are annotated to specific functionalities based on GO annotation rules but the lipid associations are not well known or sufficiently comprehensive.To overcome this and to maximise compatibility with current annotation standards, we developed a set of gold-standard lipid-related terms based on GO terms; see Methods. This list consists of 1463 lipid-associated GO terms in the Biological Processing category (BP terms), 924 GO terms in the Cellular Component category (CC terms) and 1736 GO terms in the Molecular Function category (MF terms).For BP, out of a total of 177 PSP predicted/real complexes, 21 reached significance. Of these 21, 5 are predicted clusters. Therefore, about 12% of significant PSP complexes are lipid associated. About half of these are not previously known to be lipid associated . A list of these novel/potentially novel BP lipid-associated real complexes are reported in Table Among the novel lipid-associated complexes, the HMGB1-HMGB2-HSC70-ERP60-GAPDH complex is of particular interest. This complex was isolated from human leukemia cells deficient in components of the mismatch repair system  and functions by detecting changes in DNA structure caused by incorporation of nonnatural nucleosides and is a determinant of cell sensitivity to DNA modifying chemotherapy . DisruptTo understand if the other lipid GO categories also report similar results, a similar analysis was repeated for CC and MF terms, reporting 30 and 20 significant complexes respectively.For the full list of complexes, refer to Additional file Although BP is commonly used for functional annotation and analysis, the limited overlaps between the 3 categories suggest that it is prudent to examine all three GO categories when determining lipid associations.et al.[To demonstrate generalisability, we performed similar analysis using the results from Wei et al.. Here, tHowever, there are variabilities, though limited, in terms of identified proteins for each sample (n = 30). On average, there are about 3000 identified proteins in total, which is 3 times more than the HCC dataset.Figure Figure As mentioned earlier, the NSCLC dataset reported 3 times as many proteins than the HCC dataset. Since PSP is dependent on variations in reported proteins per sample, it may not work if the assay reports lots of proteins. However, we find that the effectiveness of PSP is undiminished.Current proteomics assays also saw the rise of extensive set ups allowing reporting of more than 10,000 proteins in samples ,17. HowePSP is a powerful and precise method, with acceptable false positive rates. This is confirmed in the use of both biological complexes as well as PDSs.The PDSs are informative, being able to properly recover the patient classes as well as selecting significant features whose enriched GO terms are consistent with the liver cancer phenotype.Pathway analysis based on integrated data is evidently superior due to consistency and coverage issues among databases. KEGG performs well with our data but the added PDSs from WikiPathways and IPA improved analytical resolution.By analysing the inter-play between co-localised significant PDSs and complexes, we find very good expression correlation implying coordinate responses and activity. We also uncovered an interesting relationship for two DNA repair complexes with the “purine metabolism” pathway. This co-localisation appears to be involved in mod-to-poor-stage liver cancer transition and warrants further biological exploration. Finally, we also demonstrate how analytical resolution can be enhanced via the use of specialised ontologies. Specifically, we have identified several novel lipid-associated complexes from the set of significant PSP complexes.Briefly, liver tissues were obtained from 12 HCC-diagnosed male patients with chronic Hepatitis B virus (HBV) infection. Tissues collected were grouped according to histology report; 5 had moderately differentiated HCC (mod) and 7 had poorly differentiated HCC (poor). Paired tissues were obtained from each patient, one from the adjacent non-tumor region  and the other from the tumor region of the resected liver. Samples were labelled with iTRAQ-tags and separated using two phase LC-MS.Proteomics analysis was performed using GPS Explorer Ver. 3.6. Peptides and proteins were determined using Paragon6  and MASCOT5  on IPI human database (Ver. 3.31). Quantification of iTRAQ ratios and standard deviation (s.d.) was performed using standard parameters on GPS Explorer or Protein Pilot.et al.[In Goh et al., it was http://www.ingenuity.com). It consists a total of 299 gene pathways, 21,314 genes and 60,900 gene pairs.For the reference pathway database, we utilised Pathway API , which cAs a whole pathways can be quite large, analytical resolution can be enhanced by considering only the relevant subnets. A PDS is a pathway-derived and biologically coherent subnet. To determine a PDS, detected proteins found in at least half of the patients in mod and poor stage respectively were mapped to each pathway. Expression filters  were not used as they resulted in a very sparse number of small subnets. Unmapped pathway proteins are removed, causing the pathway to fragment into connected components or subnets. Subnets of minimum size 4 were extracted for analysis as PDSs. The minimum size requirement reduces large fluctuations in hit rates due to small sizes. The derived PDSs are treated as a cluster vector of features.In the PSP paper , each feThese profiles are used to examine the consistency and confidence of the derived relationships between samples. For simplicity, hierarchical clustering was used to examine sample relationships. Distances between individual profiles were calculated using Euclidean, while Ward’s was used to determine the clustering.To evaluate confidence in the clustering results, the R bootstrap resampling package pvclust was used. For the tree generated, p-values (between 0 and 1) are calculated. pvclust provides two types of p-values: AU (Approximately Unbiased) and BP (Bootstrap Probability). AU, which is computed by multiscale bootstrap resampling, is a better approximation to unbiased p-value than BP value which is computed by normal bootstrap resampling.HA = 〈HCA1,…, HCAm〉 and HB = 〈HCB1, …, HCBn〉, where A and B correspond to the mod and poor stages. The t-statistic score between the lists HA and HB is then computed by the standard formula:To perform feature selection, for each PDS C in the cluster vector, two lists are produced whereIf the t-statistic is significant, then the PDS C is differentially expressed between the mod and poor stages. As the calculated t-score may not necessarily follow a standard t-distribution, weighted randomisation via class label swapping was performed between mod- and poor-stage samples 10,000 times to produce the null distribution. If the t-score value is negative (positive), the empirical p-value is determined by the percentage of null-distribution t-scores that are smaller (greater) than the actual t-statistic value.For those PDSs regarded as significant (p ≤ 5%), we calculate a ranking score for the mod and poor stage respectively using the reported iTRAQ protein ratios. Suppose we have a PDS comprised of proteins A, B, C, D. A is supported by 4 mod-stage patients with ratio , B is supported by 1 patient with ratio of 5 while C and D are not supported. If the ratio is lower than 1, we convert it by taking its reciprocal. To find out how big is this ratio, we take difference = ratio – 1. The score, S, would thus be Σ + 4. However, with this scoring approach, PDSs with more proteins tend to be ranked high. For example, a PDS with 10 proteins  and patient i has a high ratio value on Ai and low ratio value on the other 9 proteins; this PDS will get a higher score than a PDS of size 4 with all patients having medium ratio values on all 4 proteins in this PDS. To improve the scoring function for such instances, we propose dividing S by the number of unique proteins that were reported in the patient. For example, suppose a PDS consists of proteins A, B, C, D, there are 2 patients reporting A and their scores are 1.1, 1.2 while 1 patient report B and his score is 5. The cluster score is (0.1 + 0.2 + 4)/2 (note that the denominator is not 4).To determine whether the hit-rate based methods (PSP and PDS) were reporting a large number of false positives, poor-stage patients (being the larger group) were divided into groups A and B randomly 10,000 times. A t-score, and accompanying p-value were calculated as before.http://www. geneontology.org. UniProtKB accessions were mapped to Ensembl Gene IDs via Biomart. To refine analysis, informative biological process terms (term that is annotated to at least 30 proteins and has no child term having more than 30 annotated proteins) were extracted from the GO OBO file [GO provides a controlled vocabulary for assessing cluster function and coherence. The annotation files and GO tree files (ver. 1.2) for Homo sapiens, dated 23 April 2011, were downloaded from Detailed curation rules for lipid-associated GO terms are provided under Supplementary Methods. Briefly, for MF and CC terms, it is based on indirect lipid-term and functional associations. For BP terms, two principles, criticality and generality are applied. The former is concerned with identification of lipid-related key events, for which no alternative steps are possible, while the latter only holds if the association is generalisable across various tissues or species.The lipid association of a complex is the hit rate (a/n) where a is the number of proteins (in the complex) annotated to at least one lipid-related GO term, and n is the size of the complex. We calculate the p-value using a Functional Class Scoring (FCS)-like procedure  where foDetermination of a complex’s novelty is done manually by searching for the presence of lipid-associated annotations or references in the literature. A harder task however, is identifying which lipids are associated with the protein complex. This is non-trivial as there are over 2,000 species of lipids, and many with unknown functions or poor annotations.The authors declare that they have no competing interests.WWBG and LW conceived the original ideas for this work. WWBG performed the computational as well as biological analysis, and wrote the manuscript. FM and LHS curated the lipid-related GO terms. MS and LW supervised, contributed to refinements and co-wrote the manuscript. All authors read and approved the final manuscript.Left: Histograms showing the lack of consistency in identified proteins between patients in the same cancer group (moderate and late). The hierarchical clustering tree on the right demonstrates the poor resolving power of the proteomics data due to lack of consistency, which justifies the need for novel approaches for tackling this issue.Click here for fileThe contributions of each pathway to a PDS. Pathway_ID correspond to the PathwayAPI pathway ID while PDS_contribution indicates the number of PDSs that particular pathway contribute based on the liver cancer proteomics dataset.Click here for fileSize distribution of pathway subnets or PDSs extracted from non-merged PathwayAPI using liver cancer proteomics data. Most PDSs are relatively small, and range from size 5 to 10.Click here for fileGO term distributions among significant PDSs. Click here for fileSorted significant GO terms according to the cancer hallmark classifications.Click here for fileClustered samples using HCL. Significance is calculated the AU score. Significant clusters are enveloped in red rectangles. The performance of clustering using different Pathway databases are shown. Included also is the original clustering result from PSP using biological (CORUM) and predicted complexes.Click here for fileSet of 58 significant PDSs derived from non-merged PathwayAPI. Shown also are the hit-rate p-values, expression scores (mod and poor), constituent members and annotated GO terms.Click here for fileCo-locating PDS and Complexes on same biological pathways. Pathway_id corresponds to PathwayAPI’s reference id. CORUM_id refers to CORUM databases’s index for biological complex. Also included in this table are the constituent members, and expression scores for PDSs and complexes for both mod and poor stage.Click here for filePredicted lipid-associated complexes. Cluster_ID is the same as CORUM’s unless it is prefixed by a G, which means it is a predicted cluster. The p-value is from PSP, which indicates whether this cluster is significantly differential between mod and poor groups. The lipid-content p-value denotes whether this cluster is significantly lipid-associated. Mod and poor scores indicates the expressional deregulation of the cluster in each cancer phase. Members indicates the constitution of each cluster.Click here for fileSignificant clusters found for each GO category for lipid association. Cluster_ID is the same as CORUM’s unless it is prefixed by a G, which means it is a predicted cluster.Click here for fileSupplementary_Methods Details for the proteomics experimental procedure.Click here for file


Ecto-5’-nucleotidase (e5NT) belongs to the family of metallophosphoesterases, hydrolyses AMP to adenosine, and is a regulator of the adenosine signaling pathway [ pathway . It has  pathway ,3. In a  pathway . Recentl pathway .


Motivation: BioPAX is a standard language for representing complex cellular processes, including metabolic networks, signal transduction and gene regulation. Owing to the inherent complexity of a BioPAX model, searching for a specific type of subnetwork can be non-trivial and difficult.Results: We developed an open source and extensible framework for defining and searching graph patterns in BioPAX models. We demonstrate its use with a sample pattern that captures directed signaling relations between proteins. We provide search results for the pattern obtained from the Pathway Commons database and compare these results with the current data in signaling databases SPIKE and SignaLink. Results show that a pattern search in public pathway data can identify a substantial amount of signaling relations that do not exist in signaling databases.Availability: BioPAX-pattern software was developed in Java. Source code and documentation is freely available at http://code.google.com/p/biopax-pattern under Lesser GNU Public License.Contact:patternsearch@cbio.mskcc.orgSupplementary information:Supplementary data are available at Bioinformatics online. BioPAX is a community standard for pathway representation developed by a broad community of researchers working on pathways and related resources .Existing graph searching tools for biological networks are limited to simple binary graphs that use nodes for molecules and edges for the interactions between . GenericSupplementary Data. The tool can be used as a standalone application or a library to seamlessly integrate pattern searches into the existing software.Here we present a framework and software tool that allows users to specify complex patterns using the rich BioPAX ontology and to search any BioPAX level 3 model for those patterns. We demonstrate the tool with a pattern sample that we query in Pathway Commons (We define a pattern as a fixed number of BioPAX elements that satisfy a list of constraints. Constraints are re-usable objects that can be mapped to elements in the pattern.EntityReference (ER), which has at least two different PhysicalEntity associated (PE1 and PE2) , and a Conversion (Conv) that has PE1 and PE2 as participants on different sides.Consider a case where we want to detect reactions that post-translationally modify a protein—an important question for proteomic data analysis. An example is shown in generative constraints. For example, in PhysicalEntity objects, given that the ER is already assigned; thus, it is generative. However, the last constraint checks for inequality and needs both of its mapped elements already assigned to an object; thus, it is not generative.Constraints that can enumerate matching objects of last element for a given set of assigned prior elements are called To construct a pattern, users first should decide the types of objects in the element array and their relationships. The type of the first element should be specified explicitly. The types of the following elements are determined by the generative constraints. Every element in the pattern, other than the first one, should be generated with a distinct generative constraint. Currently, there is no look-ahead, in the sense that constraints cannot use elements that are supposed to be generated by the next constraints. For instance, the first constraint in Simple link traversing constraints: These can be any property linking two BioPAX elements, including transitive properties, such as a member of a complex with arbitrary depth.Element field constraints: Constraining element fields with either pre-set values or with values of other elements, such as two objects sharing or not sharing a GO term annotation.Constraints for BioPAX semantics: These constraints implement non-trivial reasoning, such as identifying inputs and outputs of a Conversion, linking entities through recursive homology relationships also taking complex memberships into account or detecting syntactically invalid structures like an input to a Conversion also being a controller.Logical operator constraints: Logical operators, like AND, OR and NOT, can be applied to constraints using those wrapper constraints.The software provides a wide range of constraints, which can be classified as follows:Users can run a graph search using constructed patterns on any level 3 BioPAX model. Previous levels of BioPAX can be auto-converted to level 3 using Paxtools. Running times of the searches depend on the pattern to be searched. The search method is iterative, i.e. it evaluates candidate members for the slots in the pattern linearly and outputs every possible matching.Supplementary Data. Patterns can be easily extended, combined and re-used, allowing users to define increasingly complex biological processes, such as signaling cascades as patterns, and to share them with other researchers.It is also possible to pre-assign some elements in the pattern to conduct more specific searches. An example is provided in the Supplementary Data.We demonstrate the use of pattern searches with a biologically interesting example in the Pathway Commons database, which is an aggregate of public pathway databases that provide their data in BioPAX. The example pattern detects pairs of molecules where the first one is controlling an interaction that the second one participates in. This is useful for capturing signal transduction in the cell. Unlike the simple example in Supplementary Data). This shows that there can be a lot to gain by searching patterns in public detailed models, even if there are databases specialized for the information of interest. The project Web site contains other ‘how to’ examples and documentation.Searching this pattern in Pathway Commons returns 32 599 relations between 4695 proteins. We compared these results with the relations that we could get from public signaling databases SPIKE (Funding: This research was supported by NIH grants (U41HG006623) and (GM103504).Conflict of Interest: none declared.


Genome-wide association studies (GWAS) led to the identification of numerous novel loci for a number of complex diseases. Pathway-based approaches using genotypic data provide tangible leads which cannot be identified by single marker approaches as implemented in GWAS. The available pathway analysis approaches mainly differ in the employed databases and in the applied statistics for determining the significance of the associated disease markers.So far, pathway-based approaches using GWAS data failed to consider the overlapping of genes among different pathways or the influence of protein–interactions. We performed a multistage integrative pathway (MIP) analysis on three common diseases - Crohn's disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) - incorporating genotypic, pathway, protein- and domain-interaction data to identify novel associations between these diseases and pathways. Additionally, we assessed the sensitivity of our method by studying the influence of the most significant SNPs on the pathway analysis by removing those and comparing the corresponding pathway analysis results. Apart from confirming many previously published associations between pathways and RA, CD and T1D, our MIP approach was able to identify three new associations between disease phenotypes and pathways. This includes a relation between the influenza-A pathway and RA, as well as a relation between T1D and the phagosome and toxoplasmosis pathways. These results provide new leads to understand the molecular underpinnings of these diseases.http://www.cogsys.cs.uni-tuebingen.de/software/GWASPathwayIdentifier/index.htm.The developed software herein used is available at  This argues in favor of applying new methodologies to unravel the complex architecture of common diseases GWAS typically focus on single marker statistics to obtain top hits et al., which used Wellcome Trust Case-Control Consortium (WTCCC) GWAS data on Crohn's disease (CD), highlighted the role of numerous genes involved in the IL12/IL23 pathway, which were only identified through meta-analyses of several GWAS A great number of studies highlighted the usefulness of pathway-based approaches to detect new genes/SNPs which otherwise would be skipped due to strict statistical stringencies, which are usually applied in GWAS to avoid false positive findings So far, most of the pathway studies based on GWAS data published to date have used only genotypic data or summary statistics to identify new pathways for different diseases. These approaches use different algorithms and database and they led to the identification of novel disease associated pathways for diverse complex phenotypes . The top hit pathways encompass mainly disease, infection and immune response pathways, such as Epstein-Barr virus infection, B cell receptor signaling and the antigen processing and presentation pathway. These findings support the theory of a connection between CD and microbe interactions Furthermore, our study clearly confirms the role of previously published pathways as important pathways in influencing the susceptibility to CD see , CD.To understand the role of common biological pathways in different phenotypes, we mined three phenotypes using SNP sets that are based on known and predicted protein-interactions and pathway information (see the methods section for more details). We observed a substantial overlap of eleven pathways between CD, RA and T1D see . These pWe here present the multistage integrative pathway analysis (MIP) that incorporates information from protein-interaction databases and GWAS genotypic data to perform a pathway-based analysis of GWAS. Using our MIP approach, apart from confirming previously published pathways, we also identified three pathways associated with RA and T1D. The identified pathways are considered to be associated to these phenotypes see , even thp-value smaller than 10−3, 10−4 or 10−5 and performed our analysis again. Finally, we compare these results to the original study that includes all SNPs. After this comparison 24 pathways of the CD analysis, 31 pathways of the RA analysis and 24 pathways of the T1D pathways kept significant in all analysis runs. Thus, our method is not biased by single SNPs having very low p-values.Additionally, we showed that it is important to consider the influence of the top significant SNPs on a pathway analysis. These SNPs distort the statistical evaluation in a manner that a whole pathway is discovered to be associated to a disease although it is only one or few SNPs which are associated to it. We overcome this problem by excluding SNPs having a Up to now, there exist several pathway analysis approaches for GWAS. They differ in statistics, machine learning methods or text mining approaches which are applied for the identification of disease related pathways. Besides these fundamental differences, the used bioinformatics databases and/or the releases also vary between the studies see . Due to In this study, we used experimentally validated and predicted protein interaction data to extend the existing biological knowledge for the identification of disease associated pathways. Other approaches used such information in a different way. For instance, the application DAPPLE extracts existing interaction knowledge from pathway databases to build its own protein interaction networks a priori knowledge improves the quality of SNP sets and leads to more significant results. We designed four different kinds of pathway analysis methods to build pathway-based SNP sets. Two of these methods include protein-interaction data. In summary, 45.83% for CD, 77.42% for RA, and 70.83% for T1D of the best pathway sets are determined using the interaction methods , we choose to construct our pathways based on KEGG PATHWAY. Each of these databases has its own advantage and disadvantage. However, we chose KEGG, because its pathways are manually curated, represent a high-quality resource and provides a well-defined amount of metabolic and signaling pathways Our study also has a few limitations. Despite the use of an integrative approach in deciphering newly associated pathways for diverse phenotypes for any given pathway study, the basic unit of analysis is a pathway, which is extracted from existing databases. Despite the advancement in genomics, the function of many genes is not deciphered and hence those genes cannot be assigned to pathways. Moreover, recent studies also suggest the role of non-coding regions in influencing the susceptibility to complex phenotypes, therefore, like GWAS, pathways also capture a relatively modest amount of genetic variations. The technological advancement will expedite the annotation of the human genome, which will help to curate comprehensive pathway data sets for genetic studies.We used four publically available Wellcome Case-Control Consortium (WTCCC) GWAS datasets for our study: Crohn's disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) as cases, and the British Birth cohort as control. These datasets have already been subject to extensive quality control procedures, whose details are described elsewhere We developed a three step multistage integrative pathway (MIP) analysis pipeline to perform a pathway-based GWAS analysis for each WTCCC dataset. In the first step, we constructed a multi-layered data structure consisting of SNP, gene, known and predicted protein-interaction, and pathway data. Based on this data structure, four different kinds of SNP sets were generated in the second step. Finally, these sets were evaluated with a modification of the Fisher's combined statistic approach using 5000 permutations and a best list of the pathways was determined see .p-value smaller than a predefined threshold. For these three runs, we selected a threshold of 10−3, 10−4, and 10−5  SNP layer consists of SNPs and p-values determined using a single marker analysis of PLINK pathway layer contains pathways from KEGG; (iii) the gene layer provides genes for the SNPs, a gene interaction class (see below), and a connection to the pathway and protein layer; (iv) the protein layer combines data from UniProt and Pfam and provides all encoded proteins of the gene layer and protein domains; (v) the domain-interaction layer contains known and predicted domain-interactions from the DOMINE database . The combined file provides NCBI gene and reference sequence identifiers to map the SNPs onto genes. With this mapping method, it was possible to assign 98.7% of the SNPs to genes. After mapping the SNPs to genes using the recommended Affymetrix annotation, 37% of these SNPs are directly located in the coding region of their associated genes and 61.7% of these SNPs are located 5′ upstream or 3′ downstream of associated genes. Those SNPs are also included in the analysis.Depending on the domain interactions, we assigned an interaction class to each gene which reflects the number of interactions of the corresponding protein, and the interaction confidence of these interactions. We chose four interaction classes defined by the DOMINE database (ordered in descending interaction confidence): experimentally validated (EV), high interaction confidence (HC), medium interaction confidence (MC) and low interaction confidence (LC). The interaction class corresponds to the median of all interaction categories, i.e., at least 50% of the interactions must belong to the defined class or to a class of higher confidence.The advantage of constructing such a data structure is that SNP sets can be built that reflect the biological background and biochemical interplay of the SNP genes more precisely than randomly created SNP sets.pathway set, which contains all SNPs located in any gene of a specific pathway. In contrast the characteristic pathway set contains only those SNPs of genes occurring exclusively in the pathway. If a gene occurs in more than one pathway it is not considered for the analysis. In addition to that, the characteristic pathway set allows us to explore the influence of overlapping genes between different pathways.In the following, a pathway set describes a set of SNPs that meets different requirements. Thus, the basis of every set is a pathway, i.e., it consists exclusively of SNPs located on genes that are contained in this specific pathway. For our analysis, we built four different pathway sets: (i) simple pathway sets, (ii) characteristic pathway sets (iii) interaction pathway sets, and (iv) characteristic interaction pathway sets. The simplest set is the interaction pathway set additionally considers the gene interaction classes for the SNP-set construction. For each pathway, four different sets are created depending on the gene interaction classes: ultra-set, high-set, medium-set and low-set. The low-set is the superset of all interaction-pathway sets and includes the SNPs of genes of all interaction classes. The medium-set contains only SNPs of genes assigned to the EV, HC and MC class. The high-set is built by genes with EV and HC class and finally, the ultra set consists of the SNPs of the EV class Click here for additional data file.Table S1Overview of several pathway analysis studies. This table gives an overview of related pathway analysis studies for GWAS. The methods are briefly described and the investigated disease and used bioinformatics databases are listed.(XLSX)Click here for additional data file.Table S2Best lists of the MIP analyses of Crohn's disease. This excel file contains four spreadsheets presenting the results of our MIP analysis for Crohn's disease (CD): the best list of the analysis with all SNPs, the best list of the analysis using SNPs having a p-value ≤10−3, the best list of the analysis using SNPs having a p-value ≤10−4 and the best list of the analysis using SNPs having a p-value ≤10−5.(XLSX)Click here for additional data file.Table S3Best lists of the MIP analyses of rheumatoid arthritis. This excel file contains four spreadsheets presenting the results of our MIP analysis for rheumatoid arthritis (RA): the best list of the analysis with all SNPs, the best list of the analysis using SNPs having a p-value ≤10−3, the best list of the analysis using SNPs having a p-value ≤10−4 and the best list of the analysis using SNPs having a p-value ≤10−5.(XLSX)Click here for additional data file.Table S4Best lists of the MIP analyses of type 1 diabetes. This excel file contains four spreadsheets presenting the results of our MIP analysis for type 1 diabetes (T1D): the best list of the analysis with all SNPs, the best list of the analysis using SNPs having a p-value ≤10−3, the best list of the analysis using SNPs having a p-value ≤10−4 and the best list of the analysis using SNPs having a p-value ≤10−5.(XLSX)Click here for additional data file.Table S5Literature comparison. This table presents for each investigated disease the comparison of the MIP analysis results to literature and other pathways analysis tools. The red font indicates that this pathway was found by other pathway analysis tools. No highlighting is used if the pathway-disease association has been reported previously in other literature. A yellow highlighting indicates that other literature reported the pathway-association as a side-effect and orange is used if no supporting literature could be found. Abbreviations: CD =  Crohn's disease, RA =  rheumatoid arthritis, T1D =  type 1 diabetes.(XLSX)Click here for additional data file.Table S6Sensitivity analysis. This table presents the joined results of all four MIP analyses for each disease. The pathways which kept consistently significant during all analyses in all diseases are highlighted in yellow. Abbreviations: CD =  Crohn's disease, RA =  rheumatoid arthritis, T1D =  type 1 diabetes.(XLSX)Click here for additional data file.Table S7Comparison to GenGen. This table shows a comparison of the results of MIP and GenGen. The pathways which kept significant during our sensitivity analysis are highlighted in yellow. Abbreviations: CD =  Crohn's disease, RA =  rheumatoid arthritis, T1D =  type 1 diabetes.(XLSX)Click here for additional data file.


Though rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it has the potential to discover hidden disease pathogenic mechanisms by combining statistical methods with biological knowledge. Generally, algorithms or programs proposed recently can be categorized by different types of input data, null hypothesis or counts of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive an empirical distribution for test statistics for evaluating the significance of candidate pathways. However, evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be addressed before we apply them widely with confidence.Two algorithms which use summary statistics from GWAS as input were implemented in KGG, a novel and user-friendly software tool for GWAS pathway analysis. Comparisons of these two algorithms as well as the other five selected algorithms were conducted by analyzing the WTCCC Crohn's Disease dataset utilizing the MsigDB canonical pathways. As a result of using permutation to obtain empirical p-value, most of these methods could control Type I error rate well, although some are conservative. However, the methods varied greatly in terms of power and running time, with the PLINK truncated set-based test being the most powerful and KGG being the fastest.Raw data-based algorithms, such as those implemented in PLINK, are preferable for GWAS pathway analysis as long as computational capacity is available. It may be worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset, since the methods differ greatly in their outputs and might provide complementary findings for the studied complex disease. Simple single-marker tests used in genome-wide association studies (GWAS) have contributed to the discovery of many loci responsible for the variation observed in complex traits or disorders -3; neverBiological pathways, which are actually series of actions among molecules in a cell that lead to a certain product or a change in a cell , are usuThough several methods have already been proposed, there is no consensus as to the best method for conducting a GWASPA, especially when the underlying causal mechanism for disease at the functional level is not yet clear ,24. ResuAs one of those complex diseases investigated at the earlier stage of GWAS , evidencKGG is an open-source Java package developed for whole genome gene-based analysis, pathway analysis and protein-protein network analysis. Currently, it contains two classical algorithms for performing downstream pathway analysis after getting gene-level statistics by GATES , a novel(j) are the ordered jth p-values (j from 1 to M) of the individual SNPs mapped to gene G; me is the effective number of independent SNP p-values among all M SNPs, after accounting for the LD structure among these M SNPs; me(j) is the effective number of independent SNP p-values among the top j SNPs, after accounting for the LD structure among these j SNPs.Note: P G (i) calculated by GATES, is the ith ordered gene p-value (i from 1 to k) among all k genes in pathway A.Note: Assume k genes mapped to pathway A, PNote: N is the total number of genes in the whole gene list; Q is the number of those N genes in the pathway A; n is the total number of genes passing gene p-value threshold; and q is number of genes in pathway A out of those n genes.Among the other five selected algorithms, only Aligator takes SNP-level summary statistics as input as GATES-Simes and GATES-Hyper does; for Aligator, a gene is treated as significant if it contains at least one SNP with p-value below predefined threshold. Each pathway is then tested for whether it contains more significant genes than expected by chance . GSEAfor880 canonical pathways  were adopted to perform pathway analysis on this raw permuted data when the other three algorithms  on SNP level summary statistics from logistic regression analysis of raw simulated data. Each algorithm would produce a set of pathway level p-values. Since the null hypotheses assumed that no pathways were enriched by disease-susceptibility SNPs or genes for this simulated dataset, we estimated Type I error for each algorithm empirically by calculating the proportion of pathways with nominal p-values smaller than the critical threshold (set at 0.05) out of all 854 pathways. One sample Kolmogorov-Smirnov tests were performed to investigate whether observed pathway p-values followed a  uniform distribution or not. Then algorithms which had an appropriate type I error rate were applied to the original CD dataset so as to prioritize potential causal pathways. In order to conduct these comparisons fairly, the same value was set when a predefined threshold was needed. False discovery rates (FDR)  were comTable Quantile-quantile (QQ) plots  for origA QQ plot of SNP-based test statistics from the WTCCC Crohn's Disease dataset shows that a bunch of individual SNPs and a few genes were significantly associated with CD Figure . Table 3Table It has been suggested that multiple genes in immune system functional pathways, especially those containing different Interleukin factors, might be involved in causation of Crohn's Disease ,29. Our Due to complexity caused by SNP, gene, and pathway relationships, re-sampling strategies like permutation are always utilized to derive an empirical distribution for test statistics to evaluate the significance of candidate pathways. However, it comes with a cost of time and memory. Usually two approaches are used to lighten the computational burden, either a revised adaptive or optimal permutation of raw genotype/phenotype data ,47 or a There are also some limitations of our study. While the WTCCC CD dataset is an ideal model GWAS, the performance of GWASPA algorithms may not be as good with other datasets, where studies are smaller and less is known regarding function at the outset. We recommend applying two or more algorithms from different categories  in practice, especially when we cannot readily distinguish the scenario in which various susceptibility variants confer moderate risk to disease versus the scenario in which a major effect variant in a pathway plays a dominant role in complex diseases ,20. In aGWAS pathway analysis, which prioritizes candidate pathways associated with complex disorders, could serve as an important complement to individual SNP analysis and gene-based analysis. Though all algorithms selected in this study do not have inflated Type I error rates, they vary greatly in terms of power and running time. The PLINK truncated set-based test was the most powerful, but the two summary statistics-based algorithms implemented in KGG were the fastest. However, raw data-based algorithms should be preferred for GWAS pathway analysis as long as computation capacity is available, since they preserve the intact data structure and tend to be more powerful than summary statistics-based algorithms. When underlying disease causal mechanism is ambiguous, which is common for complex diseases, it is worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset.http://bioinfo.hku.hk:13080/kggweb/home.htm.KGG is implemented using Java; therefore a Java Runtime Environment (JRE) is required to run KGG. Currently, installation of JRE and KGG are supported for Windows, Mac OS × and Linux. Three command files  are provided for users to run KGG easily. A graphical user interface will automatically appear once initiating the command file. Documentation, source code, and precompiled binaries can be downloaded from The authors declare that they have no competing interests.HG devised the study, performed simulation and data analysis, and drafted the manuscript. ML developed the KGG package and implemented GATES-Simes and GATES-Hyper algorithms. SC and PS contributed to study design and revised the manuscript. All authors read and approved the final manuscript.Potential CD-associated pathways by different methods. Table S1. Positive pathways for Crohn's disease from enrichment analysis of 93 CD susceptibility genes. This table contains 28 enriched pathways  from GeneTrail analysis. These pathways are treated as positive pathways for CD in this study. Table S2. Number of significant pathways (FDR < 0.05) detected by PLINK-Ave and GATES-Hyper algorithm with different LD and p-value threshold setting. This table shows impact of varying LD pruning and p-value threshold on detecting significant pathways for Crohn's disease. Table S3. Overlapping between pathways detected by different raw data based algorithms. This table presents significant pathways (FDR < 0.05) detected by PLINK-AVE, GRASS and GSEAforGWAS. The overlap between each pair of algorithms and also with positive pathways is illustrated by 'Yes' or 'No'.Click here for file


Over the past 15 years, computational models have had a considerable impact on basal-ganglia research. Most of these models implement multiple distinct basal-ganglia pathways and assume them to fulfill different functions. As there is now a multitude of different models, it has become complex to keep track of their various, sometimes just marginally different assumptions on pathway functions. Moreover, it has become a challenge to oversee to what extent individual assumptions are corroborated or challenged by empirical data. Focusing on computational, but also considering non-computational models, we review influential concepts of pathway functions and show to what extent they are compatible with or contradict each other. Moreover, we outline how empirical evidence favors or challenges specific model assumptions and propose experiments that allow testing assumptions against each other. Basal ganglia (BG) contain a variety of both glutamatergic and GABAergic fiber tracts. Why is BG organization that complex? Two influential theories, published more than 20 years back  and contains an additional glutamatergic synapse  and substantia nigra reticulata (SNr), two largely analog BG output nuclei that tonically inhibit the thalamus Figure . The dire Figure . Corticae Figure  are eithIn the last decade, the rise of computational simulation techniques has boosted model development. Today, there is a multitude of different models, none of which yet accounts for all relevant empirical findings (section 9). Most of these models assume a clear anatomical separation between the different pathways. Although this is likely a simplification  implementation. Such mathematical implementations offer important advantages, including, but not limited to the following: they allow computing the effects of non-linear interactions between simulated neurons that would be impossible to compute mentally. Moreover, they are innately precise, thus preventing fuzzy assumptions; if some of a model's various assumptions contradict each other or do not interact well, the model will fail to produce meaningful output. Finally, computational models produce predictions that do not immediately originate from their assumptions. Such predictions might, for instance, relate to model performance during specific behavioral tasks. As a note of caution, however, computational models are often hard to grasp intuitively: a set of mathematical formulas does not innately reveal what function a model serves. Rather, extensive and often iterative simulations are required to reveal these functions. To report and review computational models, thus, verbal and graphical descriptions of model assumptions and outputs are required as well. These, however, may suffer from lack of precision and in any case simplify a model's “real” computational details.In the context of BG functioning, computational modeling has been particularly fruitful in recent years. The complexity of BG anatomy and physiology, in light of their substantial interactions with cortex, thalamus and other sub-cortical nuclei makes them a good target for computational modeling.The striatum (which is part of direct and indirect pathways) receives topographically organized inputs both from intratelencephalically-projecting cortical cells and from axon collaterals of cortical pyramidal-tract neurons Figure . CorticoNext to its MSNs, striatum contains cholinergic and several types of GABAergic interneurons Tepper, . While GSTN (which gives rise to the hyperdirect pathway) receives topographically organized inputs from frontal and motor cortices , including internal cognitive and emotional states, based upon activation of any other representation , they have been hypothesized to establish and execute sequences of motor processes by linking each single response of a sequence to its respective predecessor  information by reducing dimensionality of cortical information . This hypothesis therefore directly contradicts the global-facilitation hypothesis outlined in section 4.1. Recent computational models mostly follow Mink's  may serve as stimuli for stimulus-response associations as well. In an even broader generalization, the direct pathway may interlink any two cortical representations  result in reversal-learning deficits in non-human primates . In the model by Schroll et al. , therefoIn consideration of the long indirect pathway's relatively slow conduction velocity (section 2.2), this pathway has been hypothesized to provide termination signals for motor execution Nambu, . In lineThe global-response-termination hypothesis is well compatible with the hypothesis that the long indirect pathway globally inhibits responses (section 5.1). Response termination, however, requires a delay in suppression such that responses can be initiated first.We do not know of any direct functional evidence for a termination function of the long indirect pathway.According to Brown et al. , the shoBoth indirect pathways have been hypothesized to establish a surround inhibition of unwanted motor programs during motor responding Mink, . Mink 1 proposedChallenging the subdivision of BG fiber tracts into direct, indirect and hyperdirect pathways, BG have been proposed to consist of a selection pathway, containing what is referred to as direct and hyperdirect pathways in this review, and of a control pathway, vaguely consisting of what is termed long and short indirect pathways here , while synaptic plasticity in this pathway is not yet well understood (section 2.4).Based on its fast and relatively global effects on GPi, the hyperdirect pathway has been proposed to globally prevent premature responses until response selection has been completed Frank, . Along tBeing instructed to maximize rewards, subjects are supposed to learn to choose the stimulus of each pair that provides better average outcomes . In subsequent test trials, pairs are shuffled such that high-conflict pairs  and low conflict pairs  may be presented. Using such a task, human patients ON deep brain stimulation (DBS) of the STN , the hyperdirect pathway has been hypothesized to establish surround-inhibition of unwanted motor programs during responding . We don't know of any empirical study investigating the strict surround-inhibition hypothesis against the more unspecific surround-inhibition hypothesis.The hyperdirect pathway has been hypothesized to clear information from working-memory and to thus allow for updating of working-memory content  representations Nambu, . As outlCenter-surround collaboration has been proposed for direct and indirect pathways as well (section 5.5). Mink  assumed facilitating activation of cortical representations.According to a different set of hypotheses, global inhibition of cortical representations via the hyperdirect pathway is powerful enough to overrule any specific activation caused by the direct pathway Figure . WheneveAn again different set of hypotheses (section 5.6) suggests that the long indirect pathway modulates the direct pathway's effects. Suri et al.  proposedConflicting hypotheses on pathway functions may be empirically tested against each other. Critically, such tests will have to link brain processes to overt behavior and will thus have to be performed in awake animals or humans. In the following sub-sections 8.1 to 8.3, we suggest a few such experiments.Models are relatively unanimous about the direct pathway's functional contribution to motor responding. With regard to its potential involvement in working-memory processes, however, two relatively incompatible hypotheses have been proposed (section 4.5): according to the first, the direct pathway takes part in gating working-memory content (Gruber et al., training of working-memory tasks, since this might more consistently involve BG participation than an already automatized task (cf. Antzoulatos and Miller, To test the maintenance against the updating hypothesis, an experimenter could inactivate direct-pathway MSNs in genetically modified mice (cf. Hikida et al., inactivating the direct pathway, however, natural variances in direct-pathway gene expression could be related to working-memory performance (cf. Heinz et al., Related studies could also be performed with human subjects: rather than It has been proposed that the short indirect pathway learns response inhibition based on aversive events (Frank et al., A, show an increase in activity during competitive response B and that there are other neurons that behave vice versa. If the long indirect pathway is responsible for such a surround inhibition, lesions of STN and GPe should each eliminate the phasic increase in GPI activity. If, however, the short indirect pathway is involved, only lesions of GPe should eliminate it. If the hyperdirect pathway is critical, only STN lesions should abolish the phasic increase in firing.While some authors hypothesize surround-inhibition of unwanted motor programs to be implemented via long or short indirect pathways (section 5.5; Mink, Although existing models of BG pathways account for a variety of anatomical, physiological and biochemical data, some major findings have not yet been implemented at all. Table It may be noted that Table In the Introduction we posed the question, why BG contain such a multitude of nuclei and fiber tracts. By reviewing influential hypotheses on the functions of BG pathways, we hope to have outlined potential functional advantages of such complexity. It has to be admitted, however, that all of the interpretations reviewed have been developed from a reverse-engineering standpoint, asking why BG are complex, given that this is the case. They do not answer why complexities evolved in the first place or if there might have been simpler solutions that would have guaranteed equivalent functionality.Most theorists assume that BG nuclei and fiber tracts give rise to separate pathways and that these pathways fulfill distinct functions. While they mostly agree that the direct BG pathway activates specific cortical representations, the functions of indirect and hyperdirect pathways are under intense debate. We have outlined various hypotheses on these pathways' functions and suggested that they may be grouped according to these pathways' hypothesized interactions with the direct pathway. Specifically, we have identified push-and-pull opposition, center-surround cooperation, global blocking of direct-pathway effects and modulation of direct-pathway effects as major proposed interaction patterns.We hope to have motivated stringent empirical tests of hypotheses on pathway functions, believing that theory-based research promises exciting advances in the understanding of BG complexity.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cancer is a disease of genome alterations that arise through the acquisition of multiple somatic DNA sequence mutations. Some of these mutations can be critical for the development of a tumor and can be useful to characterize tumor types or predict outcome.We have constructed an integrated biological information system termed the Roche Cancer Genome Database (RCGDB) combining different human mutation databases already publicly available. This data is further extended by hand-curated information from publications.The current version of the RCGDB provides a user-friendly graphical interface that gives access to the data in different ways: (1) Single interactive search by genes, samples, cell lines, diseases, as well as pathways, (2) batch searches for genes and cell lines, (3) customized searches for regularly occurring requests, and (4) an advanced query interface enabling the user to query for samples and mutations by various filter criteria.http://rcgdb.bioinf.uni-sb.de/MutomeWeb/.The interfaces of the presented database enable the user to search and view mutations in an intuitive and straight-forward manner. The database is freely accessible at  Facilitated by the development of new high throughput techniques like SNP arrays the identification of genes involved in complex diseases such as cancer has made enormous progress over the recent years. This resulted in a huge amount of information on human genetic variations which have been described in various genes associated with a wide variety of diseases -3. SequeThe proportion of mutations causally implicated in cancer is still unknown especially due to the high number of mutations between different tumors -8. AlthoDespite the technological progress and increase of data, the benefit of this information is still limited due to the ever increasing number of databases  and the Roche Cancer Genome Database (RCGDB) [To facilitate the analyses of cancer mutations, we developed the second version of the  (RCGDB) , an inteIn the current version the RCGDB combines a huge variety of information on the human cancer genome. We integrated somatic mutations, which are not inherited but acquired during lifetime in somatic cells of an organism and might cause tissue specific tumors. This information is gathered from COSMIC at the Sanger Institute , the CanIn addition to germline and somatic mutations, we retrieve SNPs and their corresponding frequencies from the international HapMap project . We focuFurthermore, our system contains amplification data from the database of genomic variants and the NCBI SKY/M-FISH & CGH database , as wellhttp://www.biocarta.com and Roche internal networks.The cancer genome data is further enriched by pathway information from KEGG , BioCartA major aspect in designing the user interface was that users should be able to search and view mutations in an intuitive and straight-forward manner, without having to understand the architectural details of the warehouse system. To facilitate this, we grant access to the RCGDB via different ways:First, the database offers a Google-like web interface to search for cancer genome information on genes, samples, cell lines, diseases, as well as pathways. As a special feature each search is supported by an auto-suggestion functionality allowing, e.g. in the gene search, to query by NCBI GeneIDs, names, or synonyms.After performing a simple gene search the system presents a single page with all gene centered information. An example is shown in Figure For cell line queries we offer searches by cell line name or frequently used synonyms. The resulting page presents a card with general information on the sample  on the left-hand side. On the right-hand side, different sections display all known cancer genome information for the sample. This includes a list of all mutations and a table of all mutated genes. Below that we give an overview of all genes, which were found not to be mutated. For genes not mentioned in one of these two sections the database contains no mutation information for a given cell line.In addition to the cell line search the RCGDB offers an interface to query samples by tissue and histology. The system will check all samples with the given filter for mutations and present all non-mutated and mutated genes in these samples. For each mutated gene we show the percentage of mutated samples, allowing to filter for the most mutated gene in the given tissue and histology.Another possibility to query the system is given by the disease search interface based on OMIM disease names or IDs. After performing a search we present all genes associated with this disease by OMIM and all HapMap SNPs found in the region of these genes. These SNPs are not directly associated with the given disease but rather with a gene which is known to be associated with the disease.For researchers working on special pathways we integrated KEGG and BioCarta pathways along with Roche internal pathways. After selecting the desired pathway we present a static pathway image where all genes are linked to the corresponding gene search page. For protein families within KEGG pathways the system will directly link to a gene batch search for all members of the family.The above-mentioned interfaces also allow batch searches for genes, samples and cell lines whereby multiple entries are separated by a single white space. As a result, on the left-hand side the system presents an overview of all matches found. If the search name is an alias, which can be unambiguously matched to a single gene or cell line, the corresponding match is shown. For ambiguous matches the user can refine the search by selecting the desired gene or sample. This directly updates the mutation findings presented on the right-hand side, as well as the section on mutated genes or mutated cell lines respectively  for regularly occurring requests of higher complexity. The concept behind the smart searches are specialized interfaces for different applications, presenting the data in a highly condensed way. An example is the "mutation status" smart search as shown in Additional file The smart searches are not only used for customized search interfaces but also for integrative analyses. An example for this is the pathway enrichment analysis query  and single-gene LSDBs are publicly available and offer different kinds of human variation data by various interfaces. Unfortunately, the diversity of mutation information systems and the underlying data models make it difficult to mine human variation databases in an integrative approach. Thus for a comprehensive analysis of human variation data the researcher still has to search multiple databases.These drawbacks have been overcome by the integrative concept underlying the RCGDB. The main idea here is to offer data spanning a variety of data sources and data types. The database not only offers the complete data available in the integrated data sources, but in addition includes mutation data from 14 publications which were curated by hand. To the best of our knowledge this data is currently not available in any one of the other databases. The huge benefit of this concept is illustrated in Table Another important feature of the RCGDB compared to already published databases on human variation data can be found in our powerful search interfaces: Mutation databases offer mostly access via gene, sample and tissue search, which we extend by batch searches and additional pathway and OMIM disease searches. Our advanced filter search and especially our smart searches are very convenient and allow for full access to cancer genome data. Users whose primary interest lies within a specific biological problem will prefer the convenient Google-like web interface or customized smart searches providing powerful platform-independent visualization of the data. The search pages accept names, synonyms, or IDs and suggest potential matches by auto-completion. These suggestions during typing the search string will be very helpful for users if the NCBI GeneID or the exact scientific name of the gene is not known.http://rcgdb.bioinf.uni-sb.de/MutomeWeb/.With the Roche Cancer Genome Database we already cover a wide range of cancer related data and offer a convenient way to use this data in an integrated fashion. For the future, we will continue to integrate new external cancer related data into the warehouse system. The Roche Cancer Genome Database is publicly available at The authors declare that they have no competing interests.JK designed the database, implemented the importer and the user interface, and drafted the manuscript. DM, SK, and HB participated in the design of the user interface and tested biological use cases. HPL provides the hardware infrastructure and tested the complete system. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/4/43/prepubSupplementary Figure S1: Two examples for batch searches: On the top, the result page of a gene batch search for the members of the HER-family. Here, the researcher used the synonym "her2" and the unknown gene "her" for the ERBB2 gene. At the bottom, the result page of the cell line batch search shows all mutations, mutated genes, as well as the "non-mutated" genes.Click here for fileSupplementary Figure S2: Two examples for smart searches: The "Mutation Status" smart search finds the mutations of the HER-family, KRAS, and BRAF in five different cell lines. The resulting matrix is shown in the blue box. The "Pathway Enrichment Analysis" smart search determines for all KEGG pathways whether the number of mutated genes found in prostate carcinoma samples is statistically significant.Click here for fileSupplementary Figure S3: An advanced search for all mutations of EGFR or ERBB2 in breast samples, which are no cell lines. The found mutations and simple statistics are shown at the bottom.Click here for file


Resistance to chemotherapy severely limits the effectiveness of chemotherapy drugs in treating cancer. Still, the mechanisms and critical pathways that contribute to chemotherapy resistance are relatively unknown. This study elucidates the chemoresistance-associated pathways retrieved from the integrated biological interaction networks and identifies signature genes relevant for chemotherapy resistance.An integrated network was constructed by collecting multiple metabolic interactions from public databases and the k-shortest path algorithm was implemented to identify chemoresistant related pathways. The identified pathways were then scored using differential expression values from microarray data in chemosensitive and chemoresistant ovarian and lung cancers. Finally, another pathway database, Reactome, was used to evaluate the significance of genes within each filtered pathway based on topological characteristics.By this method, we discovered pathways specific to chemoresistance. Many of these pathways were consistent with or supported by known involvement in chemotherapy. Experimental results also indicated that integration of pathway structure information with gene differential expression analysis can identify dissimilar modes of gene reactions between chemosensitivity and chemoresistance. Several identified pathways can increase the development of chemotherapeutic resistance and the predicted signature genes are involved in drug resistant during chemotherapy. In particular, we observed that some genes were key factors for joining two or more metabolic pathways and passing down signals, which may be potential key targets for treatment.This study is expected to identify targets for chemoresistant issues and highlights the interconnectivity of chemoresistant mechanisms. The experimental results not only offer insights into the mode of biological action of drug resistance but also provide information on potential key targets  for further drug-development efforts. Still, the mechanisms and related biological pathways that contribute to chemotherapy resistance are relatively poorly understood. Numerous attempts have been made to mitigate or eliminate chemotherapy resistance, based-on certain assumptions about the various mechanisms, but low response rates and poor clinical outcomes for patients can be attributed to our inability to identify and subsequently target major molecular interactions associated with such resistance. Many genes have recently been reported to determine sensitivity to multiple drugs include drug transporters and metabolizing enzymes -4, and cal drugs . To idenll lines .in vivo by binding to and causing cross-linking of DNA which ultimately triggers apoptosis [However these studies did not take into account the drug active pathways, including the regulatory interactivities of genes influenced by the drug. The drug active pathway plays an important role in the drug responses of the cellular system affected by the drug and the prediction of side-effects, which is also a very important issue for identifying and validating drug target genes through their regulatory relationships. Moreover, considerations should be taken of drug resistance mechanisms, including reduced intracellular drug accumulation, increased detoxification of the drug by thiol-containing molecules, increased DNA damage repair, and altered cell signaling pathways and apoptosis mediators . In addipoptosis . For exaCombining pathway structure information mined from the re-constructed large biological interaction network with gene differential expression values, this study elucidates the particular platinum-based chemoresistance-associated pathways. Genes deemed relevant for chemotherapy resistance were also determined. Results of this study demonstrated that the identified pathways can increase chemotherapy resistance. This approach can identify pathways with a response dissimilar to that of known modes of biological action, and these new hypotheses can be used early in the drug development process to avert repeated and costly clinical trails. The major contributions of this approach are: (1) to reveal the phenomenon of chemoresistant mechanisms and related interactions between genes by combining pathway structure information with gene differential expressions; (2) to provide crossing validation candidate signature gene sets by calculating the values of betweenness centrality and degree in large complex networks; and (3) to propose new hypotheses for chemoresistant mechanisms through systems biology.This section covers the graph-theoretical properties, biological network constructions, and data sets.Basic graph-theoretical properties and representations used by this study are as follow:DEFINITION. A graph G =  = (V(G), E(G)) consists of a vertex set V(G) with vertices (or nodes) i v∈ V(G), and an edge set E(G) with ∈E(G).G with biological information yields a biological network B Nas follows:A graph DEFINITION. Let B N=  be a network with vertices v∈V, edges e∈E, and a function δ: Y → P (Y = V ∪ E) that maps vertices and edges onto their respective properties p∈P.δ: Y → P is at least subjective because for all p∈P, there exists a y∈Y with δ(y) = p.Depending on the particular network representation, in a biological network vertex properties can include genes, proteins or chemical elements, and edge properties may refer to specific interactions, such as binding or regulating. The mapping To integrate heterogeneous biological networks, we identified three types of interactions relevant to a network: (i) protein interactions, such as protein-DNA binding or multi-state protein phosphorylation by kinases during signaling, (ii) regulatory reactions including co-expressions in regulons, and positive and negative regulation, and (iii) metabolic reactions. For protein interaction data, we parsed the Pathway Interaction Database (PID) , a highlOur goal was to use protein interactions and regulatory reactions assembled into metabolic pathways without introducing duplicated links and elements. To merge interactions from various sources, the genes' alias names must be arranged in advance. Furthermore, we recorded the directions of interactions between genes (or proteins) as well to the graph. We joined the proteins as vertices to the integrated large network and connected them to any co-regulated genes by adding new edges. From a biological viewpoint of transcriptional relationships, a number of genes may regulate themselves or regulate each other, resulting in cyclic relationships while re-constructing the large network, which makes it more difficult to determine simple shortest paths. We dealt with this problem by merging vertices as demonstrated in Figure et al. presented the results of a preliminary investigation into the molecular phenotype of patient-derived ovarian tumor cells in the context of sensitivity or resistance to carboplatin [ex vivo setting. Gabriela et al. investigated the response to cisplatin of a panel of NSCLC cell lines and found an inverse correlation between sensitivity and damage formation resulting from this agent [Peters boplatin . They cois agent . Furtheris agent  ) time for a graph with m vertices and n edges. Seed nodes were determined either by users' interesting genes [el al. unpublished data). The merged CEBPD-regulated genes are listed as user-interested genes in Table The first step in the pathway identification process was seed node selection. Here, particular vertices were tagged as "seed nodes", and the shortest paths between them were identified by Yen's algorithm . From a ng genes  or were ng genes , (ii) geng genes , and  is a graph such that each vertex of L(BN) represents an edge of BN; and two vertices of L(BN) are adjacent if and only if their corresponding edges share a common endpoint in BN.et al.'s statistical scoring system which captures the amount of gene expression change in a given pathway [p-value m pfor each expressed gene m in the microarray data and then converted the m pinto a z-score by Formula 1.To filter and identify the "significant pathways"  we followed Ideker  pathway . To rate-1 denotes the inverse normal cumulative distribution function. In random data, p-values are distributed uniformly from 0 to 1 and z-scores follow a standard normal distribution, with smaller p-values corresponding to larger z-scores. The aggregate score of a set of genes in a pathway can be calculated by summing the m zover all m in the pathway,where Φk-shortest path approach guarantees effective pathway identification through a particular set of seed nodes. The scoring functions (Formulas 1 and 2) contribute an appropriate constraint filtering pathways. Once the top n pathways have been selected, the analysis of pathways process can be performed.Under this scoring function, the pathways of all sizes can be compared, with a high score indicating a biologically active pathway and pathways were then filtered by an assigned threshold score. In summary, the N1B  and N2B , and a matrix representing the correspondences between V1 and V2. Let s(e) and t(e) be the origin and terminal nodes of edge e so the intersection of the pathways is defined as:The main purpose of performing pathway intersections is to determine whether different cancers have identical chemoresistant mechanisms. Comparing two pathways requires the identification of the corresponding vertices. The correspondences between vertices in the pathways are given by matching the genes' official symbols. In general, the correspondences can be many-to-many for the reason that a vertex (e.g. representation of an enzyme) may catalyze different reactions in the pathway and may be catalyzed by multiple vertices as well. In other words, graph comparison is an NP-hard problem which typically can only be addressed by exhaustive enumeration techniques. Here, we present an approach using the vertices and edges labels of the given pathways. Consider two graphs, 1v∈V1, v2∈V2 and e1∈E1, e2∈E2. In other words, the intersection between all 1v∈V1 and v2∈V2, and the intersection between corresponding edges e1∈E1, e2∈E2 under the condition that ∀ 1v, 2v: δ(1v) = δ(2v) and ∀ 1e, 2e: δ(1e) = δ(2e). An edge e∈ N1B is selected if both the originating and terminating vertices have δ-corresponding vertices in N2B.where i bis the ratio of the number of shortest paths between a pair of nodes in the network that pass through node i and the total number of shortest paths between those two nodes, the unscaled betweenness of node i is To assess the importance of genes within each filtered pathway, we also implemented the betweenness centrality and degree centrality for each node. The degree and betweenness centrality of genes were calculated using the Reactome database  as a basn is the number of nodes in the network. The betweenness centrality is positive and always less than or equal to 1 for any network. The degree of a node in a network is the number of connections or edges by which the node is related with other nodes. Degree centrality is the number of links that connect the node to the network divided by the number of nodes in the network minus 1. It is a local measure that does not account for network context. However, changes in nodes with high degree centrality are likely to influence a large number of nodes in the network. The degree centrality was calculated by Formula (5).where Formula (5) indicates the degree centrality of an undirected graph. As for a vertex representing the gene (or protein) in an undirected graph, the higher the degree, the more reactions it interacts with and the more important the vertex is.As described in previous section, we integrated the PID, KEGG and TRANSFAC public databases, and further eliminated duplicated reactions and elements. Accordingly, 8173 genes and 9308 interactions were retained, for which both detailed and summarized database results are presented in Table Ovarian cancer is among the most malignant of all lethal diseases in women. Currently, the preferred treatment regimen for ovarian cancer is combination chemotherapy primarily with platinum-based drug such as cisplatin or carboplatin. While this treatment course has shown promising effects in a high percentage of cases, the development of chemoresistance is a significant hurdle to successful treatment outcomes . Hence, et al. performed an immunohistochemistry analysis of 56 patients with advanced serous ovarian carcinomas using archival paraffin-embedded specimens and demonstrated that c-KIT was expressed in ovarian carcinoma and was statistically correlated with chemotherapy resistance [As shown in Figure sistance . C-KIT ein vitro. Ohta et al. investigated whether the inhibition of PI3K increased the efficacy of cisplatin in an in vivo ovarian cancer model [et al. also confirmed that wortmannin blocked Akt phosphorylation and the downstream targets of the PI3K/Akt cascade, such as BAD (Bcl-2-associated death protein) and nuclear factor-kB in vivo by immunohistochemical staining and Western blotting. Moreover, Lee et al. used human ovarian cancer cell OVCAR-3 and cisplatin-resistant subclone OVCAR-3/CDDP cells to study the roles of PIK3CA  and PTEN on the resistance of human ovarian cancer cells to cisplatin-induced apoptosis [in vivo results support the proposition that our algorithm can identify chemoresistance-associated pathways.As indicated in Figure er model . Blockinpoptosis . They syet al. [In Figure et al.  found thet al. .The main analysis of this experiment focused on whether different cancers identical chemoresistant mechanisms and whether these chemoresistant mechanisms share some genes in common. After performing intersection by Formula 3), 88 pathways remained  to analyze the betweenness centrality (Formula 4) and degree (Formula 5) values of genes in this pathway; and (5) to identify the chemoresistance-associated genes.The major goals of this analysis were: (1) to explore pathways or genes involved in chemoresistant mechanisms; (2) to delineate how these genes or pathways interact with each other; (3) to test whether the As shown in the Diagram 4, several pathways contributed to this result: the colorectal cancer related pathway, the hedgehog signaling pathway, the WNT signaling pathway and the notch signaling pathway. In addition, some other pathways, such as the p53 signaling pathway, the MAPK signaling pathway, and the focal adhesion were partially involved as well. Platinum-based cancer drugs (including cisplatin and carboplatin) are among the most potent anti-tumor agents, displaying clinical activity against a wide variety of solid tumors. Its cytotoxic mode of action is mediated by its interaction with DNA to form DNA adducts, primarily intrastrand crosslink adducts, which activate several signal transduction pathways, including those involving ATR, p53, p73, and MAPK, and culminate in the activation of apoptosis . Resistap-value < 0.05 calculated in the expression data for ovarian cancer and lung cancer. For example, the expression values for the AKT gene, are not only significantly different in both cancer expression data sets, but the value of betweenness centrality and degree are higher than 3.8E-4 and 9.71E-4 . In biological terms, the betweenness centrality of a gene measures how many pathways or signal transductions go through that gene. Our experimental result indicates that the AKT gene plays an important role in chemoresistance-associated pathways. Gagnon et al. suggested that some Akt isoforms, such as Akt2 and Akt3, are involved in chemoresistance to cisplatin and that these isoforms could be putative targets for gene therapy for uterine cancers [Table  cancers . They pe cancers . The exp cancers . Thus, wet al. suggested that WNT signaling plays a role in ovarian tumorigenesis [et al., the WNT signaling pathway passes signals to the Notch signaling pathway [p-value indicate that DVL may be involved in platinum-based chemoresistance.As indicated in Figure igenesis . The WNTigenesis ,47. The  pathway . The Notet al., the expression of the CEPBD was induced by cisplatin and specifically elevated in a cisplatin resistant subline and transactivated SOD1 gene expression in the human bladder urothelial carcinoma NTUB1 cell line [p-value = 4.01E-04) does not interact with other genes or pathways. We were curious about what caused the chemoresistant mechanism after SOD1 was regulated. Cisplatin caused DNA damage as well as reactive oxygen species (ROS), which triggered cell cycle arrest or/and apoptosis. Cisplatin induced CEBPD by an as of yet unidentified mechanism which activated the SOD1 gene expression. Superoxide anion (O2•-) is dismutated by SOD1 and converted to H2O2 which can be further neutralized to water and oxygen by catalase [Most of the results analyzed in the previous section are supported by known biological evidence, which indicates that this work is able to predict candidate chemoresistance-associated genes. We were particularly interested in CEPBD (CCAAT/enhancer binding protein delta) and its transcriptional regulated gene, SOD1 (Cu/Zn-superoxide dismutase). Several reports have implicated CEBPD as a suppressor gene -29. Accoell line . This stcatalase . The redAs shown in Figure Although platinum-based chemotherapeutic agents are widely used for the treatment of endometrial, cervical and breast cancers, chemoresistance caused by molecular mechanisms still remains a major therapeutic problem. The platinum-based anti-tumor agent is a DNA-reactive reagent which causes cell cycle arrest at various phases in the cell cycle and induces apoptosis. Hence, the drug active pathway plays an important role in drug resistance in the cellular system. It is also a very important issue in the identification and validation of drug target genes by supplying their interactive relationships. This approach elucidated the particular chemoresistance-associated pathways in large biological interaction networks. Genes deemed relevant for chemotherapy resistance were also likewise determined. After identifying the chemoresistance-associated pathways, the scoring procedure filtered the significant pathways according to the genes' differential expressions. Consequently, this allowed for the identification of dissimilarities between the responses of chemosensitivity to the chemoresistance expression cancer data. In particular, we identified genes and pathway components such as the Hedgehog signalling pathway, the WNT signalling pathway, and the notch signalling pathway, that are relevant to chemoresistance for ovarian and lung cancer. The advantage of comparison analysis is in recognizing the divergent and convergent mechanisms of chemoresistance between cancers. Through systems biology methods, biologists can perform a comprehensive survey to upon which to base hypothetical assumptions.The advantages of pathway intersections analysis include: revealing whether different cancers have same chemoresistant mechanisms, and determining whether some common genes involved in these chemoresistant mechanisms. As expected, we observed a great deal of correspondence between the response interactions of ovarian and lung cancer expression data by intersecting pathways. The analysis of platinum-based chemotherapeutic agents revealed insights into common responses among the chemoresistant mechanisms as well as the candidate genes such as Bcl-2, AHR and, most importantly, SOD1. The results also indicate that the WNT signaling pathway, the Notch signaling pathway and the FAK pathway are involved in ovarian and lung chemoresistance. Therefore, further analysis of our computational experiment results may reveal additional chemoresistance mechanisms, which indicates this approach can anticipate target identification and chemoresistance in the future development of cancer drugs.in scilicon and enables easier comparisons with the generated graphs. Furthermore, by exploring signature genes involved in chemoresistance mechanisms, this approach sheds light on how these genes or pathways interact with each other, and provides analysis of the betweenness centrality and degree values of genes in pathways. In summary, this method is sufficiently flexible to accommodate various types of biological network information and experimental data, and offers not only insights into the mechanisms of chemoresistance but also provides information on potential candidate target genes for future drug-development efforts.Pathways with a dissimilar response to that of known modes of biological action can be easily identified early in the drug development process to avert repeated and costly clinical trails. This approach reveals chemoresistance-associated pathways The authors declare that they have no competing interests.SYC carried out the design of study, participated in the system implementation, wrote the program codes and drafted the manuscript. AMH participated in analysis the experimental results and helped to draft the manuscript. JHC participated in drafting the manuscript and WSC participated in writing the program codes. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/4/23/prepubPathway lists. The pathways used in this study are shown in additional file Click here for fileRepresentation of the notations used by this work. This additional file demonstrates the notations used in pathway representation.Click here for fileSignificant results following pathway intersections. The main analysis of this experiment focused on whether different cancers have same chemoresistant mechanisms and whether these chemoresistant mechanisms share some genes in common. We demonstrated the concept and the numeric results in this supplementary file.Click here for filePathway intersection results and analysis. We demonstrated another pathway intersection result. In this pathway, the start node and end node are NF-KB and CENTG2, respectively. Several sub-pathways were involved in this experimental result, such as Apoptosis, Focal adhesion, and Jak-STAT signal pathway. More detailed analysis was shown in this file.Click here for file