Biological systems are exquisitely poised to respond and adjust to challenges, including damage. However, sustained damage can overcome the ability of the system to adjust and result in a disease phenotype, its underpinnings many times elusive. Unraveling the molecular mechanisms of systems biology, of how and why it falters, is essential for delineating the details of the path(s) leading to the diseased state and for designing strategies to revert its progression. An important aspect of this process is not only to define the function of a gene but to identify the context within which gene functions act. It is within the network, or pathway context, that the function of a gene fulfills its ultimate biological role. Resolving the extent to which defective function(s) affect the proceedings of pathway(s) and how altered pathways merge into overpowering the system’s defense machinery are key to understanding the molecular aspects of disease and envisioning ways to counteract it. A network-centric approach to diseases is increasingly being considered in current research. It also underlies the deployment of disease pathways at the Rat Genome Database Pathway Portal. The portal is presented with an emphasis on disease and altered pathways, associated drug pathways, pathway suites, and suite networks.The Pathway Portal at the Rat Genome Database (RGD) provides an ever-increasing collection of interactive pathway diagrams and associated annotations for metabolic, signaling, regulatory, and drug pathways, including disease and altered pathways. A disease pathway is viewed from the perspective of networks whose alterations are manifested in the affected phenotype. The Pathway Ontology (PW), built and maintained at RGD, facilitates the annotations of genes, the deployment of pathway diagrams, and provides an overall navigational tool. Pathways that revolve around a common concept and are globally connected are presented within pathway suites; a suite network combines two or more pathway suites.The Pathway Portal is a rich resource that offers a range of pathway data and visualization, including disease pathways and related pathway suites. Viewing a disease pathway from the perspective of underlying altered pathways is an aid for dissecting the molecular mechanisms of disease. Molecular networks or pathways represent sets of interactions and reactions that together form a functional unit. While the physical boundaries of such units are in many instances fluid, their delineation by the research community establishes a paradigm for studying the working of biomolecules within a context. These can be the sets of reactions and interactions that underlie the transformation of compounds, those that are initiated or triggered by a signaling event, or those that aim to maintain the normal status quo of cells and tissue. As such, they define the metabolic, signaling, and regulatory pathways, respectively. However, when one or more components within such sets are deviant, their effects can lead to an overall disturbance of the system. This is the realm of altered pathways whose combined effects may merge into the diseased state—the disease pathway. Finally, there are the sets of reactions and interactions that illustrate the responses of the system to the treatment intended to handle the diseased state. They constitute the drug pathways that include the pharmacokinetics and pharmacodynamics drug pathways—how the system processes/metabolizes the drug and how the drug affects the system, the drug-target interaction(s) and response(s), respectively. Drugs and other compounds can elicit adverse effects, at times severe enough to be toxic; such reactions offer a toxicogenomic perspective and are viewed as regulatory response networks. The various categories/types of pathways are organized within a hierarchical structure in the Pathway Ontology (PW) , developA disease pathway, as opposed to the clinical, gene-centric view of the condition, offers a network-centric view—an approach increasingly being considered by scientists. Interestingly, despite a wealth of information on mutated genes, the number of those considered ‘driver’ genes in cancer (genes that can promote or ‘drive’ tumorigenesis) is relatively small. Collectively, they are found in a set of important pathways such as the Ras-driven Erk1/2 and phosphatidylinositol 3-kinase-Akt signaling, those mediated by receptor tyrosine kinases (RTK), or those that are involved in cell cycle and apoptosis, DNA damage control, chromatin modification, and transcriptional regulation ,10]. Al. Al10]. The provision of terms for altered pathways and the representation of disease pathways and diagrams as collections of altered pathways are features unique to PW and its use at RGD ,4],5].,[5].4],.4],Resources such as KEGG , the Smade novo purine biosynthesis, the transsulfuration or the remethylation pathways of homocysteine metabolism, as needed, or polyamine biosynthesis are all members of the ‘Methionine, Homocysteine, Folate and Related Metabolites Pathway Suite’ . Ho. HoCDKN2 product ,23]. St. StCDKN2 product -26] and andCDKN2 product . A numbe product . miRNAs, product . Early d product . PancreaTmprss2 gene and the Erg member of the ETS family is common in prostate cancer. The gene rearrangements have been mostly noticed in primary prostate cancer while the alterations/mutations of the receptor have been associated with the treatment-resistant, metastatic prostate tumors. The PI3K-Akt and p53 alterations are observed in both primary and metastatic prostate cancer. Mutations in a number of other elements are being unraveled and await further investigation with respect to their role in the initiation and/or progression of prostate tumors ,[33,[33TmprsVHL gene are thought to underlie distinct morbidity and mortality phenotypes. Mutations in components of the citric acid cycle (tricarboxylic acid or TCA cycle) create a pseudohypoxic environment that impacts on Hif modification, stabilizes the protein, and renders the pathway active, even when the VHL protein may be wild-type [PBRM1 gene known as BAF180 (a component of a chromatin remodeling complex) were found in ~41% of cases [VHL is a tumor suppressor gene first identified in patients with VHL disease—a rare hereditary cancer syndrome that exhibits vascularized tumors of the retina and other tissues. RCC, however, is primarily a sporadic condition, where bi-allelic inactivation of VHL due to chromosomal loss, mutations, or hypermethylation accounts for much of its incidence [Like prostate cancer, renal cell cancer (RCC), of which the clear cell RCC (ccRCC) is the most common type, results from a relatively small number of alterations in known pathways Figure . The maiild-type . In addiof cases . The exaof cases . VHL is ncidence .In RCC, as in the case of prostate cancer with altered androgen signaling, HIF has become an unregulated, condition-independent, constitutively active transcription factor. However, unlike the androgen receptor pathway with its rather distinct expression and functional range, the HIF pathway is ubiquitous, yet its alteration is localized. VHL, the dominant contributor to RCC, is rarely mutated in other sporadic tumors.Several drugs have been developed for the treatment of RCC ; their targets include the vascular endothelial growth factor (VEGF) and its receptors downstream of Hif-mediated transcription or the mTOR pathway which regulates HIF translation . Most drUnlike axitinib, which is selective for a particular target and is primarily used in the treatment of RCC, cisplatin is a drug used in the treatment of numerous cancer types. It is a platinating agent whose main target is agreed to be DNA where cisplatin and other platinating drugs form various DNA adducts. The DNA lesions activate the DNA damage response (DDR) system which, among a number of responses, can promote apoptosis and as such is toxic to proliferating cancer cells. The pharmacokinetics and pharmacodynamics of cisplatin are presented in a single diagram as the drug does not have a specific target Figure B. CisplaLike cisplatin, BPA can form DNA adducts, but its primary targets are nuclear and hormone receptors, mainly the estrogen receptors. BPA is used in the production of many types of plastics, the lining of food containers, medical and dental devices, and thermal paper. The high levels of BPA production are responsible for its widespread presence in the environment. In addition, BPA can also be found in food due to its leaching out of containers. BPA binds the ESR1 and ESR2 nuclear estrogen receptors and also the G protein-coupled estrogen receptor GPER. BPA can induce rapid activation of the Erk1/2 pathway via GPER in breast cancer; it may affect various immune responses and has been implicated in the etiology of many diseases and disorders  Figure B. Most cEstrogen signaling is essential for female sexual development and reproductive functions and plays important roles in bone and cardiovascular system function and in brain function [see Figure TP53 is the most commonly mutated gene in cancer. However, it is KRAS that has the highest mutation incidence in pancreatic cancer—twice its occurrence in cancer of the colon or small intestine, followed in decreasing order by endometrium and lung cancers and much lower in neoplasms of other tissues. Why is KRAS preferentially mutated in pancreatic cancer and why does this member of Ras have the highest overall mutagenic propensities relative to the other two, closely related Ras members, HRAS and NRAS? Differential codon usage in KRAS might underlie its higher mutagenic rate than its Ras cousins and distinct mutations may confer distinct functional behaviors upon the mutant proteins and prompt different outcomes, but they do not explain what triggers the frequency of mutations in and their association with different tissues [A disease pathway brings together associated altered pathways and culprit genes within, along with deregulated miRNAs and other putative candidate genes, to provide a unique view of the possible molecular mechanisms underlying the condition. Normal processes are hijacked, harnessed, and modulated by tumor cells to serve their proliferative and invasive needs, as posited by Hanahan and Weinberg . The cas tissues . In a siKRAS to mutate may in some fashion be aided or activated within a challenged pancreatic tissue milieu, perhaps further promoting its plasticity and the instantiation of mutations in other genes. Likewise, do the unique features of the filtering system the kidney represents make it more vulnerable to unwanted hypoxic responses which would be easy to ‘satisfy’, by just silencing VHL? Are the reproductive tissues more likely to be affected by infection, and does a yet to be identified infectious agent confer upon the androgen receptor its features, reminiscent of acquired resistance?Interestingly, the pancreatic acinar cells and the most abundant cell type, under certain conditions such as injury and accompanying inflammation, can acquire plastic capabilities . FindingThe provision of diagrams offers a means to quickly visualize the individual aspects of given pathways and, in the case of disease pathways, to inspect the underlying altered pathways, how their unfolding differs from the regular ones, what features are shared, and which ones may be unique. Having access to a large collection of disease and associated altered pathways enables the user to quickly inspect, compare, and identify aspects that may be unique or aspects that may be intriguing. As such, it can prompt asking new questions or redefining previous ones, lead to the search for new or revised venues of inquiry, and overall help further the efforts aimed at deciphering the mechanisms that determine the initiation and progression of disease. Future work will focus on expanding the repertoire of published diagrams for disease and altered pathways and associated drug and response pathways. Also, pathways underlying epigenetics and transcriptional programs, which are at the heart of differential gene expression in which genes get expressed and/or spliced at certain times in certain locations, likely play a role in the issues raised above. In addition, new pipelines and new features/entries of individual diagrams, such as those for genes with non-synonymous variants in the sequenced rat strains and genes and variant structural models, are scheduled to be implemented. The overall increase of the interactive diagram collection and associated pathway annotations for the rat, human, and mouse genes represent an ongoing process in the multifaceted Pathway Portal project.The Pathway Ontology is being developed using the OBO ontology editor, developed by the Gene Ontology Consortium . The patThe authors declare that they have no competing interests.VP wrote the manuscript, originated the PW ontology, composed most of the PW diagrams, and assisted with the portal design, diagram publications, and pathway pipeline construction. GTH composed some of the PW diagrams, assisted with the diagram publications, and reviewed the manuscript. MT assisted with the PW uploads, ftp file placement, and pipeline implementation. JRS assisted with the diagram publications. SJFL and SJW reviewed the manuscript. RN is part of the RGD team. JDP supervised the pipeline construction. MS supervised the PW portal and pipeline projects. MRD, EAW, and HJJ cosupervised the RGD projects. All authors read and approved the final manuscript.


Ischemic stroke is a leading cause of mortality and permanent disability, with enormous financial repercussions on health systems worldwide. Ischemic brain injury results from a complex sequence of pathophysiological events that evolve over time. In order to examine the molecular mechanisms underlying middle cerebral artery occlusion (MCAO)-induced ischemic stroke, the GSE35338 affymetrix microarray data was obtained from the Gene Expression Omnibus database and the differentially expressed genes (DEGs) between samples from patients with MCAO-induced ischemic stroke and sham controls at various time points were identified. Furthermore, protein-protein interaction (PPI) networks were constructed by mapping the DEGs into PPI data to identify the pathways that these DEGS are involved in. The results revealed that the expression of 438 DEGs, which are mainly involved in cell death, oxidant reduction, cell cycle and cell-cell signaling, were altered in MCAO samples. The nodes of CXC motif chemokine 10 (CXCL10) and interleukin-6 (IL-6) were large, with degrees of >20. In conclusion, the results suggest that CXCL10 and IL-6 have important roles in the occurrence and progression of MCAO-induced ischemic stroke. Gene Ontology (GO)enrichment analysis was performed and a protein-protein interaction (PPI) network was constructed by mapping the DEGs to the PPI data. This information may facilitate the understanding of the molecular mechanisms underlying ischemic stroke and thus aid in selecting an appropriate and effective treatment strategy for patients.http://www.ncbi.nlm.nih.gov/geo/), which is based on the Affymetrix Mouse Genome 430 2.0 Array . In total, 21 specimens, obtained one day (n=5), three days (n=3) and seven days (n=3) following MCAO-induced ischemic stroke, and one day (n=4), three days (n=3) and seven days (n=3) following control sham surgery, were available based on the GPL1261 Platform.The transcriptional profile of GSE35338  was obtaThe probe-level data in CEL files (Affymetrix Inc.) were converted into expression measures and background correction was performed by the robust multiarray average algorithm  with defFor the GSE 35338 dataset, LIMMA package  in R lanGO analysis has become a commonly utilized approach for functional annotation of large-scale genomic data .http://www.genome.jp/kegg/pathway.html) contains information of the manner in which molecules or genes are networked. It is complementary to the majority of the existing molecular biology databases that contain information of individual molecules or individual genes.The KEGG pathway database  (http://http://david.abcc.ncifcrf.gov/home.jsp), a high-throughput and integrated data-mining environment, analyzes gene lists derived from high-throughput genomic experiments   databaseftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE) detects densely connected regions in large PPI networks that may represent molecular complexes , molecular function (MF) and cellular compartment (CC). In the present study, the majority of the enriched genes were upregulated in the MCAO samples, particularly in the samples from seven days following MCAO. The DEGs for the samples taken from one day following MCAO were most commonly associated with BP and CC, including the extracellular region, response to wounding and immune response . Similarwounding . The enrTo gain further insights into the changes in the biological pathways in the cells in the MCAO samples, the online biological classification tool DAVID was used and significant enrichment of these DEGs in multiple KEGG terms was observed . The mosTo construct the PPI network, PPI data was obtained from the STRING database. In the network, each edge is examined by a score as the edge weight to quantify the interaction confidence. To obtain the correlations, the PPIs with a score of ≥0.7 were selected . Next, tStroke is one of the most common causes of mortality and disability, with marked financial repercussions on health systems worldwide . Alteredin vitro (CXCL10, a chemokine that targets activated T cells and natural killer cells expressing CXCR3, has been implicated in inflammatory disease and is most commonly associated with T cell responses –25. CXCLin vitro . The CXCin vitro ,29. Prevet al (IL-6 is an acute phase reactant cytokine with pro- and anti-inflammatory properties . IL-6 iset al  also suget al (et al (From the results of GO enrichment analysis, it was identified that the majority of enriched GO terms of DEGs in the samples obtained from one day following MCAO were correlated with cell death and oxidant reduction. This suggested that cell death and the lack of oxygen may have an important role in the onset of MCAO-induced ischemic stroke. This finding is consistent with that of a study by Mergenthaler et al , which set al . By threl (et al  who repoet al (The resulting PPI network is unweighted, since each PPI occurred only once. As it is too large to yield more specific information, it is necessary to divide the network into sub-networks, which may represent functional modules or protein sub-complexes. In the present study, clustering using MCODE and first hub nodes identified five sub-networks. The main functions of subnetwork-2 and subnetwork-4 were correlated with the immune response. Lakhan et al  reportedet al . NotablyIn conclusion, the present study analyzed the gene expression profiles and pathways that may be involved in the progression of MCAO-induced ischemic stroke by using comprehensive bioinformatics analysis. It was identified that CXCL10 and IL-6 may have important roles in the progression of ischemic stroke and thus may be used as specific therapeutic molecular targets. Furthermore, ischemic brain injury resulted from a complex sequence of pathophysiological events that evolved over time. Notably, cell-cycle genes were only induced three days following MCAO. However, further studies are required to confirm these observations and determine their clinical utility in the therapeutic management of ischemic stroke.


Pathway analysis is a set of widely used tools for research in life sciences intended to give meaning to high-throughput biological data. The methodology of these tools settles in the gathering and usage of knowledge that comprise biomolecular functioning, coupled with statistical testing and other algorithms. Despite their wide employment, pathway analysis foundations and overall background may not be fully understood, leading to misinterpretation of analysis results. This review attempts to comprise the fundamental knowledge to take into consideration when using pathway analysis as a hypothesis generation tool. We discuss the key elements that are part of these methodologies, their capabilities and current deficiencies. We also present an overview of current and all-time popular methods, highlighting different classes across them. In doing so, we show the exploding diversity of methods that pathway analysis encompasses, point out commonly overlooked caveats, and direct attention to a potential new class of methods that attempt to zoom the analysis scope to the sample scale. Pathway Analysis (PA), also known as functional enrichment analysis, is fast becoming one of the foremost tools of Omics research. The main purpose of PA tools is to analyze data obtained from high-throughput technologies, detecting relevant groups of related genes that are altered in case samples in comparison to a control. In this manner, PA methods seek to overcome the problem of interpreting overwhelmingly large lists of important, but isolated genes detached of biological context, which are the main output of most basic high-throughput data analysis, as differential expression analysis. PA methods provide meaning to experimental high-throughput biological data (HTBD) thus facilitating interpretation and subsequent hypothesis generation. This has been achieved on the basis of coupling existing biological knowledge from databases with statistical testing, mathematical analyses and computational algorithms.PA methods possess a broad range of applications in physiological and biomedical research. These methods aim is to help the researcher discover what biological themes, and which biomolecules, are crucial to understand the phenomena under study, given the HTBD analyzed. In turn, the clues that provides a PA enables the researcher to generate new hypothesis, design subsequent experiments, and further validate their findings. PA methods have helped researchers in the identification of the biological roles of candidate genes, selected to design new therapies for cancer, circumventing collateral damage to healthy cells  biology. At present, more than 35,500 prokaryotic and eukaryotic genomes are at public disposition in the National Center for Biotechnology Information site, as completely sequenced or in progress builds. Since the inception of the term “genomics” in academic literature, research in this field has changed and evolved over dramatically different aspects of human knowledge  they have constituted a scaffold for the development of new tools of study for complex biological phenomena.model.The acceptance of the pathway concept has led to many publications that describe them; but literature knowledge is hard to integrate one paper at a time. In this sense, the pathway concept has been transported from a literature scheme to an informatics framework, in the form of pathway data files compiling the information from different sources into a single who is interacting with whom, and how do these interactions happen, hence turning into a more detailed description of how do the molecular components of a biological process are working together.A pathway can be represented then as a data file, specifying which biological components are related in a common biological theme, and in some cases, the relationships they keep. Pathway data can be broadly divided into two different kinds: Gene-sets and Pathway Topology. Gene sets are simple lists of biological components, in which the listed entities share a common biological theme. In contrast, pathway topology not only lists the components of a pathway but also describes the interactions between them, showing Pathway topology may contain additional information relative to the connections between components. The character of the interactions between components can be directed, if the presence of a certain component is known to affect another in a given sense. Additionally, the character of a directed interaction can be positive or negative, depending on whether the role of the pointer component is to activate or inhibit the pointed component. An example of these three pathway representations: Gene set, non-directed pathway, and directed pathway can be found in Figure Although some authors have declared that gene sets are not legitimate pathways , also referred as “knowledge bases,” which condensate current biological knowledge of molecular interactions in pathway data collections. PDBs usually retrieve and structure data from different sources. Generally, experimental evidence is curated from literature, and computational analyses are carried out by the project itself to infer the possible functions of homologous biomolecules. Additionally cross-reference of data between similar databases is generally performed. For example, Reactome  or in plain text (.txt) formats. Encoding pathways in such formats makes them readable for both humans and machines. Examples of these standard languages are: the Systems Biology Markup Language  are in the form of directed acyclic graphs in which relevant categories are hierarchically ordered according to its relationship, for example, within the Gene Ontology categories. Heatmap formats are also popular for visual interpretation of results as pattern generation across related pathways and samples is easier to observe in this way. Additionally, most web-based and stand-alone software provide links to web pages in PDBs, as well as other online resources, for easier integration of the results. Figure Finally, the output phase consists in visualization and analysis of the results. More important, they are presented in a ranked list of relevant pathways, where the top pathways are often ordered by confidence values such as Though diverse in computations and data used to perform the analysis, PA methods share commonalities between different methods. Hence many authors have proposed different systems to classify them , and notably, univariate approaches are more powerful at more severe significance cutoffs (p ≤ 0.001). Nevertheless, every method found different sets of pathways to be significant, differences in the results are regarded due to the inherent use of different null hypothesis by each method. Additionally, as each test projects on different aspects of the data, they are complementary, increasing statistical power in the analysis of biological data, compared to individual use of any particular test , is that relevant pathways can be detected if the proportion of differential expressed genes, within a given pathway, exceeds the proportion of genes that could be randomly expected.p-value to the largest. Additional correction for multiple testing is generally performed, since evaluating data with several hypotheses simultaneously (in this case pathways) can lead to false positives. The final result from an ORA method generally consists in a list of the most relevant pathways, ordered in accordance to a p-value and/or a multiple-hypothesis-test-corrected p-value  analysis is that gives Omic data a biological context, allowing to formulate hypothesis and subsequently test them experimentally, thus easily turning into a knowledge generation cycle, proper of the Systems Biology approach.One of the most cited ORA methods is GoMiner . In this way analysis of two pathways with the same genes but different topologies would yield the same result  methods use all the available measurements in HTBD to evaluate their enrichment scores, discarding the ORA cut-off threshold limitation, but still using pathways as gene sets to perform their computations.t-statistic  test  A bThey use all available information and do not need an arbitrary cut-off threshold of differentially expressed genes.They can detect differences between pathways that are barely passing the differentially expressed thresholds and the ones that are passing them with significance levels several orders of magnitude.They can detect subtle but coordinated associations between gene-expression levels of molecules and their belonging pathways.Some methods can even identify the most relevant genes in any given pathway; for instance, GSEA calls these genes the core of the pathway.The fundamental advantages of FCS methods, over ORA are that:One of the first and most popular methods deploying the FCS approach is the Gene Set Enrichment Analysis  analysis is that interactions found in pathway topology, annotated in PDBs, bear information for interpreting correlated changes between pathway components. PTB methods can be seen as extensions of the ORA and FCS methods, as in general they perform along the same general steps, but they add pathway topology for assessing statistical relevance of the pathways.In the case of ORA extended methods, user selected genes are mapped on the pathway topology and subsequent network and statistical analyses are performed. In the case of the FCS extended methods, the HTBD along with the topology is used to compute the basal-level statistics, and proceed further in a similar way to a FCS approach  the gene perturbation factor, which uses a similar approach to the Page-Rank index used by Google , to successfully stratify the patient samples into clinically relevant clusters. PARADIGM, as well as the previously mentioned methodology Pathifier  variants, splicing variants, epigenetic modifications and postranslational modifications, as well as their potential influence in phenotype and pathway functioning  are becoming a new standard in modern life sciences research, demand of analytical techniques developed for NGS data analysis is growing. RNA sequencing (RNA-seq), is a NGS technology that generates data not only of the expression levels of the RNA transcripts in a tissue (or cell), but also data regarding sequence polymorphisms and structure of the RNA templates. Using standard PA methods to analyze RNA-seq data may give biased results due to over detection of differential expression for long and highly expressed transcripts , (grant no. 232647/2014).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.


Sufficient knowledge of molecular and genetic interactions, which comprise the entire basis of the functioning of living systems, is one of the necessary requirements for successfully answering almost any research question in the field of biology and medicine. To date, more than 24 million scientific papers can be found in PubMed, with many of them containing descriptions of a wide range of biological processes. The analysis of such tremendous amounts of data requires the use of automated text-mining approaches. Although a handful of tools have recently been developed to meet this need, none of them provide error-free extraction of highly detailed information.The ANDSystem package was developed for the reconstruction and analysis of molecular genetic networks based on an automated text-mining technique. It provides a detailed description of the various types of interactions between genes, proteins, microRNA's, metabolites, cellular components, pathways and diseases, taking into account the specificity of cell lines and organisms. Although the accuracy of ANDSystem is comparable to other well known text-mining tools, such as Pathway Studio and STRING, it outperforms them in having the ability to identify an increased number of interaction types.The use of ANDSystem, in combination with Pathway Studio and STRING, can improve the quality of the automated reconstruction of molecular and genetic networks. ANDSystem should provide a useful tool for researchers working in a number of different fields, including biology, biotechnology, pharmacology and medicine. There is no doubt that one of the most important sources of reliable biological data is the scientific literature. The well-known PubMed database contains more than 24 million abstracts, which makes it extremely difficult for researchers to manually analyze such huge amounts of data. Text- and data-mining approaches can be used for the automated extraction of information from scientific literature. However, another problem is obtaining information in a compact and convenient format that is suitable for further analysis. One of the approaches to this challenge is to present the extracted data in the form of associative molecular genetic networks that describe various interactions between genes, proteins, metabolites, biological processes and diseases.Pathway Studio , STRING Helicobacter pylori strains isolated from patients with chronic gastritis and gastric tumors [Here, we describe for the first time the ANDSystem package, which is dedicated to the reconstruction of associative networks based on an automated analysis of scientific publications, while providing a wide range of types of interactions between molecular and genetic objects, diseases and pathways. Recently, ANDSystem was used for the reconstruction of the associative molecular genetic networks associated with various human diseases, including myopia and glaucoma , dilatedc tumors .ANDSystem contains both server and client modules, including a knowledge extraction module, an ANDCell knowledge base and ANDVisio Figure . The knoThis module is based on shallow parsing technology ,12. Its Normalization is one of the most widely used approaches for the extension of dictionaries with synonyms . Thus, i1. Splitting of the full text into separate sentences.2. Fragmentation of each sentence by utilizing a sliding window with variable length.3. Normalization of text defined by the sliding window.4. Comparison of normalized text with normalized names of objects. The name of an object is considered to be found if the normalized text is identical to the normalized name of the object.5. Comparison of the non-normalized texts corresponding to the sliding window and the object name. If the initial texts are different from each other, the original text of the fragment is considered to be a new recognized synonym for the given name.Name normalization was performed with the use of the following algorithm:1. Conversion of text to one register.2. Removal of punctuation and dashes.3. Removal of articles such as "a," and "the," etc.4. English transliteration of Greek letters.5. Lemmatization based on context-free morphological analysis.6. Sort a list of words alphabetically.Thus, each normalized name was represented by the alphabetically ordered list of words in the normal form. As an example, consider the name of the biological process, "classic complement activation pathway" obtained from Gene Ontology (GO). In one of the papers , the autA statistical summary of the dictionaries used in ANDSystem is given in Table A semantic pattern is a structured record containing information about object types, dictionaries, text analysis rules or regular expressions and descriptions of the interaction semantics . The "-" symbol is used as a separator between these identifiers. The regular expression can also contain the information about allowable number of any words that can be placed between the names of objects in a sentence. Also, a regular expression may comprise a negation . For example, {metabolite} means that any object except those listed in the "Metabolite" dictionary is allowed. Figure In this study, we investigated the mechanism by which hepatitis C virus (HCV) core protein represses transcription of the universal cyclin-dependent kinase inhibitor p21 gene in murine fibroblast NIH 3T3 cells."  down_regulation-expression-GENE-ORGANISM;PROTEIN-2) down_regulation-expression-GENE-ORGANISM-CELLS.ORGANISM-PROTEIN-3) core protein represses transcription of the universal cyclin-dependent kinase inhibitor p21 gene. Template 2 also provides information about the organism where the event was observed (mouse for this example). The most detailed data will be provided by the third template, which includes basic information, as well as information on the involved organisms (hepatitis C virus and mouse) and cell line (fibroblast NIH 3T3). Only the third template will be considered by ANDSystem due to the highest priority. This approach can also be helpful in cases where participants of the interaction are mutants rather than native forms of proteins. For example, the sentence, "Overexpression of dominant-negative forms of Ras or RhoA completely blocked PDGF-induced p27 (KIP1) degradation, but only dominant-negative Ras inhibited cyclin D1 protein expression" (PubMed Id: 9407076) contains information that only the dominant-negative form of the Ras protein inhibits expression of the cyclin D1 protein. This event is described in the following sentence fragment: "dominant-negative Ras inhibited cyclin D1 protein expression." In particular, such a proposal can be performed by two types of templates:With the help of template 1, only the basic information about the regulation of gene expression can be extracted: inhibited-GENE-expression;GENE-1) mutant-GENE-inhibited-GENE-expression.2) Template 1 will extract false information that Ras protein inhibits expression of the cyclin D1 gene because this template does not include additional information about the mutation. At the same time, pattern 2 will provide a correct statement that mutant Ras protein inhibits expression of cyclin D1. In this example, template 2 has a higher priority.In the current version of ANDSystem, about 15 million PubMed abstracts published in the period ranging from 1990 to 2013 were analyzed. The extracted information describing 5,395,313 interaction events and involving 452,209 objects was stored in ANDCell.In addition to the data extracted from the texts of PubMed, ANDCell also contains information about the 905,799 interaction facts extracted from external databases, including protein-protein interactions from IntAct  and MINTIn addition to information about the interactions, ANDCell contains data about organisms and cells in which the interactions were observed.Homo sapiens, Mus musculus and Rattus norvegicus being the most highly represented organisms  in the form of bipartite graphs based on these queries. Vertices of such graphs represent the objects and edges represent the interactions between them. For the reconstruction of associative networks the user can set one or more names of objects of interest . Also, additional sets of parameters can be specified, including information sources about interactions, interaction types and object types.The ANDVisio interface containing a fragment of the network associated with cardiovascular human diseases that describes the interaction between diseases, pathways, microRNAs, proteins, genes and metabolites is shown in Figure To estimate the quality of data involving the interactions identified in the ANDCell knowledge base of ANDSystem, precision and recall values were calculated. Precision was estimated as the ratio of the number of correctly identified interactions to the total number of interactions in the testing set. The testing set was prepared as a number of interactions randomly selected from ANDCell. We estimated precision values for the 6 main types of ANDSystem interactions, including "interaction," "catalysis," "activity regulation," "conversion," "expression regulation" and "association," covering about 90% of all molecular-genetic interactions described in ANDCell , which plays an important role in the functioning of the cardiovascular system , while, the combined Pathway Studio/ANDSystem, STRING/ANDSystem and Pathway Studio/STRING networks contain 16, 14 and 15 shared pairs of interacting objects, respectively. The 3 pairs of interacting objects were found only in the ANDSystem network, including the following interaction types: 6 ≪association≫ and 1 ≪coexpression≫. The Pathway Studio network contained 3 unique pairs of interacting objects including 2 ≪Binding≫ and 1 ≪Expression≫. The STRING network contained only 1 unique pair of interacting objects. Thus, the combined use of different text-mining based systems can help to obtain the most detailed information about molecular genetic interactions.ANDSystem, which has the automated capability of reconstructing networks, was developed for the purpose of scanning literature to extract relationships between diseases, pathways, cell components, proteins, genes, microRNAs and metabolites. ANDSystem incorporates utilities for automated knowledge extraction from PubMed and analysis of factographic databases. ANDSystem accuracy is comparable to the accuracy of well-known text-mining systems, such as Pathway Studio and STRING, but in contrast to these systems, it provides a more detailed description of interactions between molecular and genetic objects. It is obvious that existing text-mining systems cannot extract full information about molecular-genetic interactions contained in scientific publications. In this study, we demonstrated that the combined use of ANDSystem with Pathway Studio and STRING reduced the information loss that results from using each system individually.http://pbiosoft.com/andsystemANDSystem is freely available under the following link: The authors declare that they have no competing interests.VAI, NVI and TVI developed text mining methods, algorithms and software for ANDSystem, PSD developed the ANDVisio program and a structure for the ANDCell database, OVS and EST provided evaluation of the accuracy of ANDSystem and its comparison with existing programs. NAK was the overall director of the research, and contributed to the writing and editing of this manuscript. All authors read and approved the final manuscript.


Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at  Given the results, we successfully integrated external resources  and pathway content with a single query.WikiPathways is a crowd-sourced online platform for biological pathways. It is based on the same underlying platform as Wikipedia. Pathways are saved as graphical images embedded in a set of meta data elements . Pathways are used as proxies of biological knowledge in their role as descriptors of processes. Yet integrating these hubs of biological knowledge with other biological data resources remains challenging due to a cacophony of file formats, identifier systems, and hidden content. We show the application of the semantic web to enable a straightforward integration of heterogeneous biological data sources. We have taken high-quality pathways from a curated set from WikiPathways and converted the content into a data format native to the semantic web. Here, data is expressed as a set of statements where the statements are built upon a set of web addresses.  For example, the following triple defines the glucose molecule as being part of the glycolysis pathway:Pathway analysis and visualisation of data on pathways provide insights into the underlying biology of effects found in genomics, proteomics, and metabolomics experiments –4. WikiPked data . The ResThe notion of a semantic web surfaces as you link across large sets of triples representing a vast number of objects and diverse types of concepts and predicates. The use of uniform identifiers, or URIs , providehttp://www.w3.org/2000/01/rdf-schema#>PREFIX rdfs: <http://identifiers.org/wikipathways/>PREFIX wp: <http://www.w3.org/2004/02/skos/core#>PREFIX skos: <http://identifiers.org/chebi/CHEBI:>PREFIX chebi: <wp:WP534 skos:member chebi:4167.wp:WP534 rdfs:label “Glycolysis and Gluconeogenesis (Homo sapiens)”@en.chebi:4167 rdfs:label “Glucose”@en.In order to contribute pathway knowledge to the semantic web, we have modeled the content of WikiPathways to form triple-based statements. The interactions and reactions curated at WikiPathways are particularly well-suited to enrich the overall connectivity of the semantic web. Pathways offer a meaningful context for relations between biological entities, such as proteins, metabolites and diseases that are otherwise defined in disparate databases. We report on the conversion process and the development of two new vocabularies essential in capturing the semantics behind pathway diagrams. Finally, we evaluate the use of the semantically linked pathway knowledge through specialized queries and third-party resources, showing how to link WikiPathways with disease annotations  ontologies are not added, however through plugins such as Pathvisio-MIM  mappingsThe WP vocabulary, focusing on biological meaning, issues URIs for biological concepts and disregards layout and other rendering details. Using URIs from this vocabulary allows stating that something is a Pathway, or that a DataNode is a chemical compound or gene product. The vocabulary also captures descriptive elements, such as labels, shapes and lines that help annotate and contextualize the pathway reaction details. The RDF generated consist of terms from the vocabularies developed in this context. This is done to be able to reflect the semantics used in the WikiPathways community. However, to allow integration with external pathway resources—which is the primary objective of this project—we need to link to external ontologies. For the subset of concepts in common with prior vocabularies, such as BioPAX, we utilize the SKOS data model to express a range of similarities from skos:exactMatch to skos:closeMatch , 28.With these vocabularies in place, the next step is the actual conversion of GPML files into triples using the GPML vocabulary. Then rules are applied to make the biological meaning explicit using the WP vocabulary. For example a directed interaction is captured in GPML as two “DataNodes”, a line and an arrowhead. The “DataNodes” have external references as properties. Rules are then applied to state that a line is a Directed Interaction, with a source and a target. http://sparql.wikipathways.org), which allows semantic querying of the data with the SPARQL query language [WikiPathways pathways are regularly curated by a team of volunteers that evaluate their usability for analysis and tag the pathways as “curated”. WikiPathways contains 1000 pathways in the curated set across over a dozen species that convert to a total of 1.6 million triples. The triples are loaded in a SPARQL endpoint  in the pathway diagram on insulin signaling in human [Different common analysis platform allow the integration of linked data for future analysis and visualization. One nice example of such a analysis platform is R, a widely used software environment for statistical computing and graphics. R has a SPARQL library , which ein human . This pain human .http://ncbi.org/biosystems). While BioPAX in fact is RDF, the NCBI system is not. NCBI BioSystems uses NCBIs native identifiers: GeneId, ProteinId, CID. We thus have a resource with pathways from different origins that are already described in the same way. Since for WikiPathways content we know how the different entities in these resources map to the GPML and WP vocabularies we can now use that to produce RDF using these same ontologies for each of the other pathway resources present in NCBI BioSystems. In fact, we can do the same for Pathway Commons where this approach will lead to an improved version of RDF with explicit mappings to the WP vocabulary. We made a prototype script available on GitHub to be used for this type of conversions from BioSystems [A number of resources provide content from multiple pathway databases, including Pathway Commons  and NCBIoSystems .https://dev.openphacts.org/docs). Other information collected in Open PHACTS describes other relationships like drug-target (from ChEMBL) and protein interaction (from UniProt). Having this all in one resource combined with a set of mapping tools allows fast analysis across the domains. By combining Open PHACTS API calls one can, for instance, find all protein targets for a drug and then all pathways that contain these targets.The semantically linked pathway data from WikiPathways RDF have also been integrated into the Open PHACTS discovery platform , 36. OpeIn collaboration with partners in the Open PHACTS project, we proposed guidelines for presenting data as RDF , most ofhttps://creativecommons.org/licenses/by/3.0/). This is stated in the VoID headers of the RDF made. These headers are automatically generated by the same script generating the WikiPathways RDF. Open PHACTS provides a template for these header files.WikiPathways content is covered by the Creative Commons Attribution 3.0 Unported license (http://jena.apache.org/)[https://github.com/wikipathways/wp2lod). The vocabularies were generated with a vocabulary framework called Deri Neologism (http://neologism.deri.ie/).We used a Java RDF framework, Jena (che.org/), to genehttp://rdf.wikipathways.org) and loaded on a instance of the Virtuoso Open-Source Edition (http://virtuoso.openlinksw.com/) and available through its SPARQL endpoint at http://sparql.wikipathways.org. The triples are also loaded on the Open PHACTS discovery platform (https://dev.openphacts.org/docs/1.5) where they can be accessed through eleven API calls.The resulting RDF triples are available from , Ensembl [wp:bdbEnsembl), UniProt [wp:bdbUniprot) for gene products and HMDB [wp:bdbHmdb), and ChemSpider [wp:bdbChemspider) for compounds like metabolites and drugs. The original identifier provided by the pathway curator is stored as a triple, with the predicate dc:identifier, and a URI from identifiers.org, which points to both the identifier and the resource.In the case of WikiPathways, which does not host a mapping service, we chose a unified identifier approach, where all identifiers are mapped ahead of time to a set of common identifier systems. In this way, the database effectively contains the results of a limited number of identifier mappings in form of partially redundant triples. For example, in the WikiPathways RDF, all identifiers have been unified to Entrez Gene  Click here for additional data file.


New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation andpenalties for multiple testing.The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge.Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data.The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation. The data provided for Genetic Analysis Workshop 19 (GAW19) offer extensive and complex genomic information  into biologically relevant pathways .Because pathway analysis is relatively new, methods for assigning genomic features to pathways and for testing the significance of these assignments in relation to phenotypes are still very much under development. This developmental fluidity was the context for this discussion group at GAW19.This report is based on the work of 7 research teams who presented their work in our group discussion at GAW19. In the following discussion, these teams are referenced by the name of the presenting author, as shown in Table Tables An overriding motivation for employing pathway analysis was to reduce the multiple-testing burden by basing inferences on the combined effects of probes or variants. Most teams used external bioinformatic data to inform pathway construction  provided for GAW19, including systolic (SBP) and diastolic BP (DBP) and hypertension (HT) status. Several teams also calculated the derived phenotype mean arterial pressure (MAP) from these data as (2/3 DBP + 1/3 SBP). All teams using the simulated data chose not to be blind to the generating model (they “used the answers”), which allowed them to estimate power to detect “true” functional variants and associated gene expression phenotypes. Most teams also used the provided null trait Q1 to estimate type 1 error rate. A limitation of this use of the simulated data (as noted by a reviewer of an early draft of this report) is that evaluation of method performance is necessarily sensitive to the assumptions of the generating model.Three teams  focused on the gene expression probes, while the remaining teams considered single nucleotide variants (SNVs) drawn from the GWAS genotypes, exome sequence data, or both. Combined analysis of gene expression and association has been considered in previous GAWs , but onAs noted, most teams made use of the simulated phenotypes as a basis for estimating power and type 1 error. Kos, Quillen, and Valcarcel constructed synthetic pathways (described more fully below) of gene expression probes (Quillen) or SNVs  representing predetermined numbers of randomly chosen genes; these synthetic pathways were then characterized by the number of genes that contributed to the simulated phenotypes as a basis for measuring the effect of functional “dosage” on analytical power. One team (Tayo) focused on a single biological pathway with known relevance to hypertension , using both real and simulated BP phenotypes.synthetic pathways (SPs) to test both the power and type 1 error of their proposed methods. SPs that represented genes used in the simulation generating model were, in Quillen’s terminology, “positive controls” to test the sensitivity of tests. SPs lacking such representation provided data on false positive rate, either in place of or supplementary to tests of the null trait Q1. All 3 teams used various metrics—for example, number of genes represented in each SP or number of generating-model genes represented—to further characterize the performance of their analytical approaches.Kos, Quillen, and Valcarcel independently conceived of using sets of probes or variants annotated to randomly selected genes to define In addition to the dimensional reduction achieved by grouping probes or variants in pathways, some teams also filtered variants based on predicted function .Lo developed biological pathways nominally based on a hypothesis that hypertension could be related to undetected type 2 diabetes . Pathway construction consisted of choosing a set of diabetes-related genes from the literature and then expanding these “seed” genes with publicly available gene interaction data. In the context of this workshop, this approach simply provided a biologically plausible way to construct pathways reflecting the number of genes and patterns of gene–gene interaction that might be encountered in a “real” study; this was tangential to the primary goal of characterizing performance of these investigators’ bespoke method for analyzing SNP-SNP interaction effects   or on prior biological information, rather than on empirical networks  . This waThe focus for methodological innovation in these studies was typically on the construction or definition of pathways. In general, established tools were used for relating pathway-defined probes or variants to the traits of interest, accounting for family data where appropriate: for example, variance components-based analyses implemented in SOLAR  , by Kos GCK and PAX4 on hypertension . As noted by the anonymous reviewers of these authors’ report, the nature of this joint effect—whether due to in-sample LD, interaction between the gene products, or some other effect—needs additional investigation.An exception to this pattern was the use by Lo of an analytical tool to detect gene interactions, previously developed by members of this team . In brieWe now consider selected approaches in more detail. Taking replicate simulations of DBP as the trait of interest, Quillen analyzed the random effect of similarity in gene expression among related individuals, with the goal of identifying pathways for which coexpression accounted for a significant proportion of variance relative to modeling the random effect of kinship alone. This analysis compared the effect of 16 methods for computing similarity matrices from the expression data; of these, simple correlation and extended Jaccard distance  outperfoBrunel developed pathways based on probes in the GAW19 data that were annotated to genes related to BP in Gene Ontology, or to genes related to these by published evidence of protein–protein interaction. They then used independent components analysis (ICA) to derive “meta-expression” phenotypes (ICA factors). Unfortunately, the use of phenotypes derived from the real gene expression data, rather than simulated data, prevented estimation of power and type 1 error rate, so it was not possible to determine if any increase in signal from data reduction could overcome the multiple-testing burden inherent in GWAS.Valcarcel proposed 2-step association tests of a simulated BP phenotype that consisted of variant aggregation (via either SKAT or burden test) for all variants in a pathway, followed by gene-centric aggregation tests within pathways that passed a chosen significance threshold. Their pathways were synthetic . The 2-step approach was more powerful, with reasonable control of type 1 error, than a 1-step gene-centric approach because of the reduction in multiple testing by prescreening of pathways.A primary focus in the Pathway-based Analyses Group was on managing the very large dimensionality of -omics data to increase analytical power. As noted in the Introduction, pathway analysis can also be employed for biological discovery, although this use was not focal for participants in this discussion group. Methodology for this sort of discovery is also in its infancy and would also be a worthy topic for future investigation, although provision of optimal simulation data for this purpose  would be a major challenge.Because many pathway-based approaches, including the pathway construction carried out by participants in this group, are heavily dependent on bioinformatic databases, our group discussions frequently returned to concerns about the current state of these resources, no matter how well curated – including inaccuracies, difficulty of interpretation, and (inevitably) publication bias. This suggests a possible contradiction at the heart of pathway analysis : many of our studies used pathway aggregation to reduce the multiple-testing burden that hampers gene and variant discovery, yet pathway construction based on prior knowledge runs the risk of constraining the scope of discovery. A possible way out of this dilemma, as noted above, is to search for empirical networks that may reveal unexpected gene interactions and thus novel biological insight.


Next-generation sequencing and metagenome projects yield a large number of new genomes that need further annotations, such as identification of enzymes and metabolic pathways, or analysis of metabolic strategies of newly sequenced species in comparison to known organisms. While methods for enzyme identification are available, development of the command line tools for high-throughput comparative analysis and visualization of identified enzymes is lagging.A set of perl scripts has been developed to perform automated data retrieval from the KEGG database using its new REST program application interface. Enrichment or depletion in metabolic pathways is evaluated using the two-tailed Fisher exact test followed by Benjamini and Hochberg correction.Comparative analysis of a given set of enzymes with a specified reference organism includes mapping to known metabolic pathways, finding shared and unique enzymes, generating links to visualize maps at KEGG Pathway, computing enrichment of the pathways, listing the non-mapped enzymes.http://sourceforge.net/projects/ec2kegg/.EC2KEGG provides a platform independent toolkit for automated comparison of identified sets of enzymes from newly sequenced organisms against annotated reference genomes. The tool can be used both for manual annotations of individual species and for high-throughput annotations as part of a computational pipeline. The tool is publicly available at  There is a growing need for computational high-throughput pipelines that would facilitate the genome- or transcriptome-wide annotations of identified genes and their subsequent comparative analysis with other annotated organisms. The new posed questions may include: how do metabolic strategies differ between a free living organism and its taxonomically close congener, which is an obligate parasite? What strategies do bacteria employ in their metabolism to adapt to different niches of a host, e.g. in blood http://www.kegg.jp/kegg/mapper.html) [Annotation of individual genomes with respect to identification of enzymes has been well developed and implemented in various packages, such as PRIAM , SHARKhuer.html) , but it er.html)  performsEC2KEGG has been developed to provide a command line tool for the automated comparative analysis of metabolic pathways between two organisms. In addition to the pathway enrichment analysis, the tool produces report about both the shared and unique enzymes for each organism, generates URL strings to visualize data using the KEGG Pathway maps. It also lists enzymes that are not part of the defined metabolic pathways.A query list of enzymes represented by EC numbers has to be obtained using a third party software, see e.g. the Background section. Information about organism specific genes, enzymes, and pathways is automatically retrieved from the KEGG database using its new representational state transfer application programming interface (REST API). Definitions of annotated organisms, pathways and their corresponding lists of enzymes are included in the package. However, it is recommended to periodically execute the enclosed get_definitions.pl script to update the definitions used by EC2KEGG.Since a given pathway can be over- or under-represented, the two-tailed Fisher exact test is employed to test whether a pathway is significantly differently represented in a given list compared to a reference genome. The background is defined by the entire list of genes in a reference genome. The number of genes in a pathway is defined by KEGG annotation for a given reference organism. P-values are subsequently adjusted to the multiple hypotheses testing using Benjamini and Hochberg correction . Of noteThe report contains KEGG Pathway ID, pathway name and category, the total numbers of: (i) all genes known in a given pathway, (ii) enzymes from a reference genome belonging to this pathway, (iii) enzymes found in a given list, (iv) shared and unique enzymes. These counts are followed by the corresponding lists of EC numbers, p-values, adjusted p-values, and the URLs to visualize a KEGG pathway using the following color code: green – an enzyme unique to a reference organism, red – an enzyme unique to a given list, yellow – a shared enzyme. Output is generated in the tabulated text format that can be redirected to a file or fed to subsequent processing through a pipeline. Only those pathways are reported that contain at least one enzyme from a given list or a reference organism.http://search.cpan.org/).The following perl modules are required to run the EC2KEGG package: libwww-perl - for internet communication with KEGG; Text-NSP - for computing the Fisher exact test; Statistics-Multtest - for correcting p-values on multiple hypotheses testing. All these modules are freely available from the Comprehensive Perl Archive Network : “KEGG API is provided for academic use by academic users belonging to academic institutions. This service should not be used for bulk data downloads”. The latter restriction does not apply to EC2KEGG, as it downloads very limited information for a given request. However, for the high-throughput analyses, the user may need to obtain the license from KEGG owners.There are restrictions imposed by the KEGG database on using REST API (http://sourceforge.net/projects/ec2kegg/) and installed on a computer by unpacking the compressed file. The package consists of the definitions files , two perl scripts, a README file, and the list of enzymes from Saccharomyces cerevisiae to serve as an example of a query list. EC2KEGG is platform independent and can be executed under any operating system, provided perl and three required perl modules are installed.The package can be downloaded from SourceForge . The package contains two perl scripts to be run from a command line. The first script has no parameters and is aimed to update the definitions necessary for execution of the main perl script.>perl get_definitions.plSaccharomyces cerevisiae (S. cerevisiae) enzymes against Schizosaccharomyces pombe (S. pombe):The main script takes two required parameters: (i) a KEGG code for a reference organism and (ii) a file name with the list of enzymes . For example, one can use the following command to compare the list of >perl ec2kegg.pl spo sce.ec > sce2spo.txtThe list of annotated species and their KEGG codes can be found in the definitions/kegg_org.txt file enclosed in the package.Pneumocystis carinii (P. carinii) is a pathogenic fungus that belongs to the Pneumocystis genus causing Pneumocystis pneumonia in mammalian hosts with weakened immune system. Despite decades of research on the fungus, its biology remains elusive. Both genetic studies and drug development are impeded by the lack of ex vivo culture of this obligate organism. In the efforts of deciphering its metabolic strategies to thrive in host lungs, the genome of P. carinii has been sequenced and currently available at http://pgp.cchmc.org/.P. carinii have been searched through for enzymes using SHARKhunt. EC2KEGG has been subsequently used to map the identified enzymes into metabolic pathways, and to compare with phylogenetically close but free living fungi: S. pombe, S. cerevisiae, and Aspergillus fumigatus (A. fumigatus). Table P. carinii enzymes to metabolic pathways and their comparison to the three reference fungi. There have been 481 enzymes identified in DNA sequences of P. carinii, including 328 mapped to KEGG Pathways and 153 non-mapped. P. carinii shows the largest overlap of enzymes with A. fumigatus, having shared 271 mapped and 84 non-mapped enzymes, respectively.Genomic sequences of Pneumocystis jirovecii[http://pgp.cchmc.org/.The pathogen displays depletion of pathways in the amino acid metabolism category Table , which ijirovecii. Moreovejirovecii,11, and Pneumocystis carinii, indicated a number of depleted metabolic pathways compared to its free living congeners, thus providing a basis for developing testable hypotheses about nutrients missing in the media to culture the pathogen.The new command line tool, EC2KEGG, enables the automated and high throughput comparative analysis of a given list of enzymes against a reference organism. In addition to computing statistically differently represented metabolic pathways, the tool produces the lists of overlapped and organism-specific enzymes, provides links to visualize highlighted maps from KEGG Pathway. Analysis of computationally identified enzymes within an under-investigated species, The author declares that he has no competing interests.The perl code for EC2KEGG package and text of the manuscript were written by AP.


Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological phenomena under study. Biological pathway data have been particularly useful in identifying functional associations of genes and/or gene sets. However, biological pathway information as compiled in varied repositories often differs in scope and content, preventing a more effective and comprehensive characterisation of gene sets. Here we describe a new approach to constructing biologically coherent gene sets from pathway data in major public repositories and employing them for functional analysis of large gene sets. We first revealed significant overlaps in gene content between different pathways and then defined a clustering method based on the shared gene content and the similarity of gene overlap patterns. We established the biological relevance of the constructed pathway clusters using independent quantitative measures and we finally demonstrated the effectiveness of the constructed pathway clusters in comparative functional enrichment analysis of gene sets associated with diverse human diseases gathered from the literature. The pathway clusters and gene mappings have been integrated into the TargetMine data warehouse and are likely to provide a concise, manageable and biologically relevant means of functional analysis of gene sets and to facilitate candidate gene prioritisation. There has been an exponential increase in the amount and complexity of biological data. Extracting meaningful biological insights from this vast array of data via functional analysis of the large resultant gene sets and to prioritise genes and gene sets for further experimental characterisation is a formidable challenge. Gene-set-functional-enrichment (GSFE) relies on a statistical analysis of the relative abundance of biological themes associated with a given gene set and identifies themes (and associated genes) that are overrepresented and therefore, likely to be more relevant to the biological conditions under study.It is increasingly evident that gene and proteins do not function alone, but rather as a part of complex pathways where they interact with various biomolecules . Therefore, an accurate representation of biological pathway information is essential to understand the biological relevance of genes and proteins within specific biological contexts. An ever growing number of pathway databases, thereby, constitute an increasingly important component of any computational framework for the functional annotation of genes and proteins. However, the available pathway resources often differ widely in scope and content, which severely hampers a unified analysis and interpretation of high-throughput biological data using diverse pathway repositories Integration of pathway repositories offers significantly attractive benefits in terms of more extensive and robust functional annotations, which in turn will contribute to a better understanding of gene function and regulation in complex biological systems. Furthermore, it also lends itself to providing a more concise and relatively discrete representation of enriched biological themes in combined GSFE studies . In recoHere we describe a new approach to integrating pathway data primarily for target prioritisation. While our method for pathway integration is simple and straightforward, the main novelty lies in its tight integration into the TargetMine data warehouse system We will first show how the various pathways can be clusters based on shared gene content, on the premise that significant overlaps in gene content between the pathways should reflect overall functional congruity between them. This notion will be confirmed by the biological relevance of the integrated pathway clusters using semantic similarities between Gene Ontology (GO) biological process terms Homo sapiens (human), Mus musculus (mouse) and Rattus norvegicus (rat)  for three organisms, us (rat) , B and Cus (rat) . Below wGene products may participate in multiple biological processes and pathways. Different pathway databases employ different approaches to compiling pathway information, and therefore may significantly differ in content; however, there remain some redundancies in pathway definitions within and across different databases. Therefore, we first examined gene overlaps between the pathways within each pathway database and across the three pathway databases. The gene overlap index . These included both intra-database  and inter-database  pathway comparisons. Amongst the intra-database pathway comparisons, we observed that 25 KEGG pathway pairs comprising 35 unique pathways  were remarkably similar  clusters and 20 singletons, 84 clusters and 14 singletons and 67 clusters and 10 singletons, respectively.After a visual inspection of the size distribution and the total number of multi-member clusters and singletons, we judged a cutoff of 0.7 to be optimal  . The patTo investigate whether the gene overlap-based distance metric resulted in well separable clusters, we assessed the overall p-value of <0.01, as this value was greater than the maximum (0.053) from 100 simulation runs (see Methods).The average intra-cluster The above observations suggest that our approach groups together pathways, which have a high fraction of genes in common and are therefore likely to be functionally related.A manual inspection of the pathway names within selected IPCs suggested that functionally similar pathways were grouped into clusters using our approach. For instance, cluster no27 “Metabolism of lipids and lipoproteins” included eight pathways (seven KEGG pathways and one Reactome pathway), all of which were associated with lipid metabolism . LikewisWe further performed a series of quantitative assessments to examine whether the IPCs consisted of functionally related pathways and were biologically coherent and suitable for gene set analysis and target prioritisation. Below, we individually describe our observations on these evaluations.purity and edit distance measures We employed purity scores for the IPCs when compared with the KEGG pathway categories and sub-categories were 0.48 and 0.22, respectively; these values were significantly higher than the average of the purity scores computed for randomised dendrograms    and wereedit distance scores for all the IPCs were 93 and 137, respectively, which were much lower than the average of the collective edit distance scores computed for randomised dendrograms    and wereThe above observations suggest that IPCs described above correspond more closely to the reference sub-types as defined in the KEGG pathway database than randomised clusters and are thus, likely to represent biologically meaningful themes for functional annotation.et al. Gene ontology (GO) annotations are one of the most useful and widely used means to estimate functional similarity between gene products. Semantic similarity is an approach to estimating the similarity or likeness between two terms of a given ontology (such as GO) FS) within and across IPCs .We first performed all-against-all pairwise pathway comparisons based on the GO term semantic similarities (GOSS) between their constituent genes (see Methods) and then we examined the overall functional similarity scores  . Furthermore, the average FS within and across the clusters, 0.32 and 0.34, respectively, were statistically indistinguishable within randomised dendrograms.The median 14426.5) , therebyTaken together, our observations suggested that IPCs were comprised of pathways, which shared an overall higher functional similarity with each other than with pathways from different clusters. Therefore, the pathway clusters were biologically meaningful and likely to represent coherent biological themes.To assess the effectiveness of the IPCs in target prioritisation, we performed GSFE analysis on different sets of genes, which were known to be associated with hepatitis C virus (HCV) pathogenesis −/−CoreTGvsWT and PA28γ−/−CoreTGvsCoreTG, respectively; see −/−CoreTGvsWT and PA28γ−/−CoreTGvsCoreTG gene sets highlighted enriched associations with 28, 28 and 29, IPCs respectively. These figures were much lower than the number of individually enriched KEGG  Reactome  and NCI PID  pathways associated with the above gene sets   . In our p = 6.84×10−18) , Reactome (488) and NCI PID (119) pathways associated with the NS5A infection network. The enriched IPCs included no23 “Endocytosis | Tight Junction” (4×10−18) . Cluster−/−CoreTGvsCoreTG network, two autophagy associated factors GABARAPL1 and GABARAPL2 were mapped to enriched IPCs no011 “Cytokine Signaling in Immune system| Cytokine-cytokine receptor interaction | Herpes simplex infection | Tuberculosis” and no012 “GPCR ligand binding | Neuronal System| Neuroactive ligand-receptor interaction” . These two genes would not have been identified by the standard pathway analysis, because they were mapped to two KEGG pathways hsa04140 “Regulation of autophagy” and hsa04727 “GABAergic synapse”, which were components of no011 and no012, respectively and neither of which showed significant association with the original gene set   . Recent p = 0.003) and Stat3-downreg was associated with an enriched pathway cluster mmu021 “Rheumatoid arthiritis” (p = 0.019) (p = 4.73×10−7). The above association would not have been identified by the standard pathway analysis, because Cfh was mapped to three Reactome pathways REACT_86987 “Innate Immune System”, REACT_144679 “Regulation of Complement cascade” and REACT_103920 “Complement cascade”, which were components of no005 but individually, none of the three pathways showed significant association with the original gene set   . Furtherctively) . Our obsAn IPC representing a general biological theme of the immune system  was enriched in all of the gene sets above. To confirm that this result was not an artefact of the clustering method, we performed a functional analysis of gene sets associated with non-immune human diseases, Atherosclerosis, Hypercholesterolemia and Pancreatitis. Our enrichment analysis revealed an enrichment of five IPCs for each of the three gene sets, respectively. These figures were much lower than the number of individual enriched KEGG  Reactome  and NCI PID  pathways associated with the above gene sets .p = 1.13×10−21)  , which is consistent with the xenobiotic stress and glutathione depletion associated with chronic pancreatitis Amongst the most significant associations, IPC no027 “Metabolism of lipids and lipoproteins” was associated with the Hypercholesterolemia gene set (3×10−21) , which ictively) , which iThe above examples suggest that our IPCs were able to provide a relatively quick, manageable and meaningful approximation of biological themes associated with diverse gene sets.http://targetmine.nibio.go.jp/pathclust/). It allows a user to upload a list of candidate genes  to TargetMine and create a gene list. The user can then retrieve enriched IPCs and examine their pathway and gene content. Further analysis of these genes and pathways may be performed using TargetMine with its query builder or pre-defined templates.A web interface, tightly connected to TargetMine, was developed for visualising the IPCs and performing GSFE  is the closest to our approach in that it integrates pathway data from KEGG, Reactome, NCI PID and BioCarta based on gene/protein overlaps and provides a standalone web interface to query large gene sets for human pathways within a data warehouse hiPathDB adopts a full integration approach where individual pathways are consolidated into a unified derivative superpathway based on shared components. This method provides a holistic and a concise view of biological processes including cross talks between different signalling pathways, but it also results in a loss of information at the molecular level Other differences between our IPCs and the clusters  defined in the popular DAVID gene functional classification tool include 1) automatically assigned informative names for the IPCs (in contrast to the DAVID clusters with no representative names), and 2) visualisation of IPCs as network graphs to allow the users to examine connections and relationships between constituent pathways.We describe our approach to integrating pathway information from public repositories based on shared gene content into functionally coherent pathway clusters. The resultant IPCs provided a convenient way to identify broad functional categories relevant to the biological phenomenon under study and thereby enabled swift candidate gene prioritisation. Since our approach relies only on gene overlap between pathways, its inherent flexibility ensures that data from additional pathway repositories (and even non-pathway gene sets) can be readily accommodated to expand the content and coverage of the IPCs.We assessed the quality of the IPCs using multiple independent measures, including the agreement with the reference sub-types defined in the KEGG database and intra- and inter-cluster semantic similarity scores. With the help of these measures, we established that the IPCs were functionally coherent and biologically meaningful. We further demonstrated the ease of employing the IPCs to analyse large gene sets extracted from the literature.Our fully automated approach has been integrated into the TargetMine data warehouse and enhanced its ability to investigate complex biological systems for better target discovery. It has also enabled seamless updates of the IPCs synchronised with TargetMine updates, which are scheduled every month in general.Inferred from Electronic Annotation) GO annotations for the corresponding genes above were retrieved using the TargetMine data warehouse An overview of our approach to overcoming the challenges encountered in integrating diverse pathway data is shown in i = 1,…,N and N is the total number of pathways), let Next, we examined the agreement between different pathways within and across pathway repositories based on the overlaps of their gene composition. For each pathway R was calculated for each pair of gene overlap profiles (PD as www.r-project.org). The dendrogram was partitioned at incremental PD cutoffs and the resulting clusters of related pathways were manually examined to select the most suitable cutoff (see Results).The collective gene overlap indices for each pathway were then collated to generate corresponding gene overlap profiles. We defined each row Once the pathway clusters were established, the fully contained pathways were reintroduced into the clusters that included their “parent” pathways. .To provide the pathway clusters with informative labels, we examined the gene composition of each pathway cluster and identified the pathways that collectively contributed ≥50% of the genes within a cluster. Their entry names in the original database were then assigned to the corresponding cluster. .purity and edit distance measures as defined by Brown et al. We adapted the purity was defined as the fraction of the constructed pathway clusters that consisted entirely of KEGG pathways belonging to a single reference sub-type (“category” or “sub-category”) and were therefore, “functionally homogenous”. Here, purity reflects the efficacy of our approach in resolving the pathway clusters into functional categories corresponding to KEGG pathway sub-types; a purity score of 1 indicates that all KEGG pathways within each pathway cluster were mapped to a single KEGG sub-type, whereas a purity score of 0 indicates that none of the pathway clusters were functionally homogenous.In this study, edit distance was computed as the minimal number of split and/or merge operations, which were required to transform individual pathway clusters into a KEGG pathway sub-type. For instance, if the pathways corresponding to the KEGG sub-type “Immune system” are distributed across two clusters, each containing other KEGG pathways, two split and one merge operations would be sufficient to transform the two clusters into a single cluster containing all pathways within the KEGG “Immune system” sub-type. The edit distance for this process would be 3.Likewise, in this study, To assess the statistical significance of these measures, 100 randomised dendrograms were generated by shuffling the pathways across the clusters in a manner such that the number of pathway clusters and the number of pathways within a given cluster were preserved. The randomised dendrograms were used to create 100 random sets of pathway clusters.p-value of the significance of these two observations (purity and edit distance) using the fraction of the purity and edit distance scores amongst the randomised conditions that was greater than the actual purity and edit distance score of the constructed pathway clusters.We defined the et al. We extended the GOSS FS) between a pair of pathways, FS may be naturally defined by calculating all possible pairwise GOSS values between genes in Next, we defined functional similarity  and used as controls. The intra- and inter-cluster To assess the statistical significance of the intra- and inter-cluster p-values were further adjusted for multiple test correction to control the false discovery rate using the Benjamini and Hochberg procedure p≤0.05.Functional enrichment analysis was performed on human and mouse gene sets extracted from the literature. These included gene sets associated with HCV pathogenesis The visual representation of the pathway clusters was implemented with JavaScript libraries including jQuery and Cytoscape Web.Table S1Integrated pathway clusters A) Human. B) Mouse. C) Rat.(XLSX)Click here for additional data file.Table S2i,jOI ≥0.8.Pathway pairs which share (XLSX)Click here for additional data file.Table S3A) Enriched IPC associations for the HCV pathogenesis-associated datasets. B) Enriched IPCs associated with genes within the HCV pathogenesis-associated datasets, which were mapped to one or more non-enriched pathways.(XLSX)Click here for additional data file.Table S4A) Enriched IPC associations for the Lung tumourigenesis-associated datasets. B) Enriched IPCs associated with genes within the Lung tumourigenesis-associated datasets, which were mapped to one or more non-enriched pathways.(XLSX)Click here for additional data file.Table S5Enriched IPCs associated with genes within the Non-immune human diseases-associated datasets, which were mapped to one or more non-enriched pathways.(XLSX)Click here for additional data file.