Nowadays a broad collection of transcriptomics data is publicly available in online repositories. Methods for analyzing these data often aim at deciphering the influence of gene expression at the process level. Biological pathway diagrams depict known processes and capture the interactions of gene products and metabolites, information that is essential for the computational analysis and interpretation of transcriptomics data.type 2 diabetes mellitus is increasing dramatically, and to better understand this multifactorial disease, more insight into the concerted action of the disease-related processes is needed. The liver is a key player in metabolic diseases and diabetic patients often develop non-alcoholic fatty liver disease.The present study describes a comprehensive network biology workflow that integrates differential gene expression in the human diabetic liver with pathway information by building a network of interconnected pathways. Worldwide, the incidence of A publicly available dataset comparing the liver transcriptome from lean and healthy vs. obese and insulin-resistant subjects was selected after a thorough analysis. Pathway analysis revealed seven significantly altered pathways in the WikiPathways human pathway collection. These pathways were then merged into one combined network with 408 gene products, 38 metabolites and 5 pathway nodes. Further analysis highlighted 17 nodes present in multiple pathways, and revealed the connections between different pathways in the network. The integration of transcription factor-gene interactions from the ENCODE project identified new links between the pathways on a regulatory level. The extension of the network with known drug-target interactions from DrugBank allows for a more complete study of drug actions and helps with the identification of other drugs that target proteins up- or downstream which might interfere with the action or efficiency of a drug.The described network biology workflow uses state-of-the-art pathway and network analysis methods to study the rewiring of the diabetic liver. The integration of experimental data and knowledge on disease-affected biological pathways, including regulatory elements like transcription factors or drugs, leads to improved insights and a clearer illustration of the overall process. It also provides a resource for building new hypotheses for further follow-up studies. The approach is highly generic and can be applied in different research fields.The online version of this article (doi:10.1186/1471-2164-15-971) contains supplementary material, which is available to authorized users. Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by chronic hyperglycemia with disturbances of carbohydrate, lipid and protein metabolism resulting from defects in insulin secretion, insulin resistance, or both. Obesity, the excess accumulation of lipids in the body, is a major risk factor for T2DM. Metabolism in the liver, adipose tissue and skeletal muscle is of key importance for the pathogenesis of T2DM. The current study focuses on the liver. It is well known that lipid accumulation in the liver contributes to insulin resistance, hyperglycemia and hyperlipidemia [ipidemia . Hepaticipidemia . Publishipidemia , 5. Althipidemia .et al. [et al. identified a relationship between thyroid hormone action and the altered gene expression pattern. The present study uses state-of-the-art pathway and network analysis methods to integrate knowledge of differential gene expression with pathway information by building a network of interconnected pathways. The focus shifts from looking at single gene expression levels to changes on process level and beyond.Modern technology enables a global analysis of gene expression in liver tissue. Exploring published transcriptomics datasets available in online repositories revealed only one transcriptomics study investigating the human diabetic fatty liver. Pihlajamäki et al.  measuredet al. . PihlajaPathway analysis was used to find significantly altered biological processes. WikiPathways  was the Although biological pathways are usually seen as independent processes, they do interact with and depend on each other. Our workflow combines and integrates relevant pathways into one biological network which allows researchers to study the effects of a disease or treatment not only in the pathway of interest but also in downstream or related pathways. Further extending the network with additional information, like transcription factor (TF) regulation, provides a more complete picture of complex biological processes and will promote the understanding of the mechanisms of diseases.In this study a published and publicly available transcriptomics dataset generated by Pihlajamäki  was usedThe raw data for 9 obese, diabetic subjects and 5 lean control subjects was reanalyzed with ArrayAnalysis.org, an online microarray quality control and pre-processing pipeline . The datThe ArrayAnalysis.org statistics module uses the Limma package  of R/BioGene Ontology (GO) analysis was performed using the GO-Elite web-interface  to identhttp://www.pathvisio.org) to interpret and visualize the molecular changes on a pathway level. The human pathway collection containing 262 pathways was obtained from WikiPathways , of these were 118 up-regulated and 63 down-regulated in the obese, diabetic subjects compared to the lean control group. 68 of the up-regulated and 17 of the down-regulated genes have known functions in biological pathways , cholesterol metabolic process (GO:0008203), glucose metabolic process (GO:0006006), response to glucose stimulus (GO:0009749), cholesterol homeostasis (GO:0042632) and complement activation, classical pathway (GO:0006958). Furthermore, processes related to onecarbon metabolism, humoral immune response, protein-lipid complex subunit organization and organic anion transport were found.Biological processes in which differentially expressed genes are enriched were identified by performing pathway analysis in PathVisio. The statistical analysis resulted in seven significantly changed pathways   is significantly increased together with an increase in the GLUT4 enhancer factor . Most downstream AMPK targets were up-regulated, i.e., HNF4A, SREBF1, eEF2, TSC2, p21 and some are down-regulated, like PFK2 and TSC1.In the Statin pathway the inhibitory action of statin, a cholesterol-lowering drug, on HMG-CoA reductase (HMGCR) is depicted. The expression of HMGCR remains unaltered in the diabetic fatty liver compared to lean controls. Moreover, cholesterol synthesis is described in the pathway and almost all differentially expressed genes are up-regulated, like DGAT1, CYP7A1, SCARB1, LCAT and APOA4.Pathway analysis revealed seven pathways with a Z-score > 1.96  up-regulated. AGPAT2 is an enzyme that plays an important role in the production of glycerophospholipids and triacylglycerols. It is known to be relevant to the liver and development of hepatic steatosis . CDKN1A The integrated network was extended with transcriptional regulation to obtain a better insight in how biological processes affected in the human fatty liver are regulated. The CyTargetLinker app in Cytoscape was used to extend the network with proximal TF-target interactions from the ENCODE project . The appet al.[Figure et al.. In the et al.–27.FigurCyTargetLinker provides a regulatory interaction network for drug-target interactions from DrugBank. The combined network was extended only with approved drugs leaving out the ones that are withdrawn or experimental. In total 280 drugs were added targeting 76 gene products in the pathways, see Figure The insulin receptor (INSR) is targeted by nine drugs of which eight are categorized in DrugBank as antidiabetic and/or hypoglycemic agents and the ninth, Mecasermin, is an insulin-like growth factor used for long-term treatment of growth failure in children with severe primary IGF-1 deficiency . The INSFurthermore, two diabetes related drugs, Phenformin and Metformin, target the two subunits of AMPK, PRKAA1 and PRKAB1. Both subunits are also activated by a member of the adenine nucleotide family, adenosine monophosphate (AMP), which by DrugBank is categorized as dietary supplement and micronutrient. The network shows that several hypoglycemic agents, like Rosiglitazone, Glipizide or Pioglitazone, are known to target PPARG, a receptor which regulates fatty acid storage and glucose metabolism. All these drugs are known to be prescribed to T2DM patients depending on the state of the disease.It has been shown that insulin resistance conditions like obesity and T2DM are strongly associated with the accumulation of lipids in the liver, which is one of the main characteristics of NAFLD , 30. AltBiological pathway information is useful for better understanding the mechanisms affected by disease. As illustrated in this study, researchers can investigate the pathways in diseased subjects compared to control subjects to gain more insights into the causes of a disease and the mechanisms of disease progression. Although pathway collections cover many well-described biological mechanisms, they are incomplete, resulting in a bias towards well-studied processes. Nevertheless, pathway databases are growing and pathway analysis has proven itself as a valid and intuitive first step in the analysis process. Wiki-based community curated pathway databases like WikiPathways reduce the barrier to participation in pathway curation and allow experts to add new findings immediately to the pathway diagrams.While standard pathway analysis investigates each pathway individually, biological processes are not independent but rather interact with and influence each other. Therefore it is relevant to investigate the links between them as well as shared regulatory mechanisms. This study describes a workflow for further exploring the interplay between pathways involved in the human fatty liver by connecting them in a biological network and extending them with additional knowledge on TF regulation and drug targeting.Pathway analysis demonstrated that Triacylglyceride Synthesis and Adipogenesis are significantly altered in obese, diabetic subjects with a fatty liver compared to the lean control group. The pathway analysis was performed using gene expression measured in liver biopsies. Although the biopsies mostly consist of hepatocytes also other cells like immune, vascular and fat cells are present. Interestingly, two pathways related to drug treatment in T2DM  are among the significantly altered pathways.AMPK is known as the metabolic regulator, and its activation influences many metabolic processes . Under cIn the Statin pathway, the expression of genes involved in cholesterol and triacylglycerol production  are all significantly up-regulated in the diabetic liver in humans. These findings indicate that hepatic lipid accumulation is facilitated by an increased expression of these genes.Regulatory elements like TFs are often not included in pathway diagrams in order to keep the diagrams comprehensible. Nevertheless, as this study confirms, TFs play a crucial role in the understanding of complex diseases since they often regulate multiple pathways simultaneously. Our analysis shows that TFs can be considered as additional links between pathways, and adding the regulatory interactions increases the overall connectivity of the network significantly. Typical TF network motifs  can be identified using standard network algorithms.Hepatocyte nuclear factor 4-alpha (HNF4A) is an essential TF in the extended regulatory network  and Metformin (AMPK). It might also be used to identify new drugs targeting pathway elements upstream, downstream or even parallel to the currently used targets. When studying the effects of drugs on a pathway, the grouped pathway nodes in Figure Besides the diabetes related drugs, there are many other drugs known to target genes in the selected pathways. HMGCR, a highly targeted gene product, is part of the Statin pathway depicted in Figure In general, the described workflow can be applied to different datasets, diseases and pathway collections. It also allows the analysis of multiple datasets together by performing separate pathway analyses and then combining the pathway results in one integrated network. The integration of pathway information into the network allows researchers to investigate the downstream effects of drugs and contributes to the identification of other treatment possibilities. Also, the link with other pathways that are affected or not affected by the disease state is important for predicting possible side effects of a drug. Including the information about the effects of other drugs in linked or related pathways can help to identify potential interferences with the efficacy and efficiency of a drug.In this study we demonstrated how pathway analysis results, which are often considered a final step in the biological interpretation of transcriptomics data, can be used and combined in a biological network to gain more insights in the interplay and relation between processes. Instead of starting with a large protein-protein interaction network and finding the important parts in it, we believe that building the networks based on relevant pathways can be another very useful approach to start the investigation. Also the inclusion of all elements present in the pathway provides a framework which can integrate different types of omics-data, like omics techniques applied to any gene product  as well as metabolomics or fluxomics. The biological interpretation might be more straight-forward because it builds on the pathway diagrams which are usually intuitive and well studied.Regulation by TFs or drugs does not only have effects on one pathway but also has effects on downstream processes. This integration leads to improved insight and also a much clearer illustration of the overall process, and the most important elements. Inclusion of information about drugs and micronutrients and their targets makes the mode of action of currently used compounds more understandable and can be useful to suggest drug repositioning and new drugs or micronutrient related lifestyle interventions.The tools used in this study, especially PathVisio, Cytoscape and CyTargetLinker, facilitated the data integration, visualization and interpretation immensely.http://www.ncsb.nl).This work was (co)financed by the Netherlands Consortium for Systems Biology (NCSB) which is part of the Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research Additional file 1:WikiPathways human pathway collection downloaded on 17 June 2014 (http://wikipathways.org/index.php/Download_Pathways).(ZIP 2 MB)Additional file 2:Table S1. 118 upregulated genes in obese, diabetic patients with a fatty liver. Table S2. 63 downregulated genes in obese, diabetic patients with a fatty liver. (PDF 62 KB)Additional file 3:Figure S1–S7. Images of all seven altered pathways with dataset visualized on the pathways. (PDF 452 KB)Additional file 4:Figure S8. Integrated network of seven interconnected pathways that are changed in the diabetic fatty liver. (PNG 1 MB)Additional file 5:Table S3. List of genes present in two or more pathways. (PDF 35 KB)Additional file 6:Figure S9. Drug extension simulation for all drugs. Figure S10. Drug extension simulation for antidiabetic drugs. (PDF 59 KB)Additional file 7:


Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives.Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods. A metabolic pathway is a network of related chemical reactions catalyzed by enzymes that collaboratively produce or degrade one or a few metabolites. Reconstruction of metabolic networks (pathways) plays an important role in studying biological systems. Together with other types of biological networks, metabolic pathways can help decipher relationships between genotype and phenotype, and elucidate essential mechanisms underlying cellular physiology .Most known metabolic pathways stored in the pathway databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) ,3 have bThe most common approach for metabolic pathway construction is based on mapping a group of gene and protein sequences of an organism to known reference pathways -11 accorAb initio methods that do not use known reference pathways, on the other hand, aim at inferring metabolic pathways from gene expression data or other data sources. Most of these methods employ probabilistic inference methods such as graphical models and Bayesian networks -20 in onAnother kind of ab initio methods represents a metabolic network with ordinary differential equa-tions (ODEs) that capture the dynamics of chemical concentrations in a metabolic system. The ODEs can include feedback loops that are often difficult for other methods to handle. Koza et al. reported a method using genetic programming to construct ODEs . A recenIn this work, we combine the strengths of both knowledge-based methods and probabilistic graphical model-based methods together in a novel fashion to improve construction of metabolic pathways. The protein-protein relationships underlying metabolic pathway networks are inferred by probabilistic inference methods under the constraints of knowledge extracted from existing reference pathways in the KEGG database. The knowledge includes individual gene products relationships and chemical reactions observed in the KEGG database, which are used as restraints to control the pathway network construction process. That is, instead of exploring any possible relationship between any two genes / proteins in consideration, the Bayesian networks learning method only sample from the observed relationships during the probabilistic pathway construction process. This knowledge-constrained inference not only drastically reduces search space, but also improves prediction accuracy. Since almost all the reactions in a pathway likely exist in one of many other pathways, the knowledge-constrained sampling generally does not compromise the coverage of pathway construction. And because individual relationships and reactions drawn from all other pathways rather than only ones in a reference pathway are considered in construction, the method is able to predict new relations and reactions not existing in a reference pathway. This new way of using known metabolic pathway information sets our method apart from traditional metabolic pathway mapping methods. Our experiment on constructing the metabolic pathways for yeast from scratch showed the performance of the method compared favorably to or was complementary with the pathway mapping methods.The rest of the paper is organized as follows. The Method section presents the method. The Results section describes and discusses the experiments and results. The Conclusion section summarizes the work and proposes some future directions.Our method integrates gene expression data of a species, its gene product (protein) sequences, and known metabolic pathways in the KEGG database to construct metabolic pathways. Figure Generally, given gene expression data and gene product sequences (i.e. protein sequences) of an organism, the method first maps genes into reference pathways to construct initial seed pathways, and then the seed pathways will be extended and refined by sampling more genes from gene groups clustered according to gene expression data. If the seed pathway contains only a few or no reactions / relations, the method can reconstruct a completely new pathway. The pathway construction is carried out by Bayesian network learning methods under constraints of existing gene-gene relations extracted from all the related pathways in the KEGG database. The details of the metabolic construction method are described in the sub sections below.Our metabolic network construction method has two main steps: (1) mapping genes to the reference pathways to construct initial seed pathways; and (2) extending and refining initial pathways based on gene expression data and observed relations and reactions. The first step, which is similar to other sequence-homology based methods, obtains initial pathway networks by mapping the genes against KEGG's reference pathways.The KEGG database ,3 has a The second step is to expand the initial pathways by adding more relevant genes, relations and reactions. In order to sample genes that are more likely to participate in the same pathway, genes are first grouped into clusters according to the similarity between their expression data. We used an Expectation-Maximization clustering method implemented in Weka  to clustGiven all the genes in the current networks including newly sampled ones, a set of knowledge constraints related to all the genes are extracted from the KEGG database. The knowledge set consists of two sub sets. One set includes all possible relationships between the genes that should be included into predicted pathways, and the other sub set contains those which new relationships and reactions can be sampled probabilistically from. Any other relationships not included of the two sub sets will be absolutely excluded. The first subset of relations can be obtained from the initial mapped network structures. The second subset can be derived from all the observed relations in the KEGG database. It contains all existing relationships in the KEGG database between ortholog groups (represented by the KO numbers) of the selected genes. The details about constructing the relation knowledgebase for all the genes of a species are described in the next sub-section. The KO relations can be transformed into gene relations through the mapping from genes to KO numbers. Given a gene set, a collection of all the existing relationships among them can be retrieved from the knowledge base.The two sets of extracted knowledge constraints are provided as another input besides gene expression data for Bayesian network learning methods for pathway predictions. The purpose is to use the constraints to restrain the search space of reactions and relations of Bayesian probabilistic sampling. Therefore, on one hand, our constrained probabilistic pathway construction is different from typical ab intio sampling techniques that explores the unconstrained pathway space composed of all the pairwise reactions and relations, which is often time consuming and more error-prone. On the other hand, our method samples from all observed relations and reactions between orthologs of genes extracted from whole database rather than the ones extracted from only one reference pathway as traditional pathway mapping methods do. The relation-level knowledge sharing across all the pathways of all the organisms is new and enables our method to take advantage all the existing knowledge of pathways to predict new relations and reactions.Given the constrained sampling space for a group of genes, we use Bayesian Networks (BNs), a classic type of probabilistic graphical models, to construct metabolic pathways. Reactions and relations (e.g. enzymatic relation) between proteins in metabolic pathways are represented by directed edges in a Bayesian network / graph (G). Given the gene expression data (D), the objective is to find such a graph that achieves the highest score in the following equation:This score is also known as the Bayesian Information Criterion. The  book by . The undThe graph search space is explored by manipulating its structure through three types of local operations on edges: adding, removing, and reversing. The search strategy is based on a greedy algorithm. In each iteration, it explores all possible local operations on the current graph, and chooses the move of yielding the highest-score to generate a new graph structure. The process will continue until the score cannot be further improved.The knowledge-based sampling approach aims to restrain the graphical structure exploration within a small bound space out of the overall search space. The edges appearing in an initial mapped reference network would be kept during the process of graph structure exploration, and a local operation will not generate an edge that does not exist in the knowledgebase.The purpose of building such a knowledgebase is to generate a realistic set of relations and reactions for pathway predictions, which restricts the relation search space only to observed relations in order to drastically improve search quality and efficiency.The knowledgebase contains a collection of relations and reactions associated with a list of KO numbers. In order to build such a knowledge base for a target organism, we first map its genes to KO numbers in KEGG database, and then extract the relations and reactions associated with the mapped KO numbers from all the KEGG reference pathway maps.Specifically in this work, we entered all the genes in Saccharomyces Cerevisiae (yeast) into the KEGG automatic annotation server (KAAS)  to map tBased on the KO number assignments and the resulted reference pathway maps, a knowledgebase can be constructed by extracting all the relations and reactions pertaining to the KO numbers. Each relation / reaction includes upstream and downstream entry nodes, a relationship type, and possibly related chemical compounds. Those KO-based entry nodes denote enzymes in metabolic pathway networks, and each of them may contain multiple KO numbers. The type information indicates the kinds of relation or reaction, such as protein-protein or enzyme-compound, etc. Generally, one reaction includes a pair of substrate and product.The objective of the experiment was to evaluate the performance of the knowledge-based probabilistic inference approach for metabolic pathway construction by comparing its predicted yeast metabolic pathway networks with real yeast metabolic pathways existing in KEGG pathway database. The new approach was compared with the standard homology-based mapping approach. The yeast gene expression data set used in this experiment was downloaded from .In order to evaluate the pathways predicted by the computation methods, we collected a list of known yeast metabolic pathways in the KEGG database as presumably true pathways. Totally 68 pathways were found, six of which were removed from the experiment because the nodes in their graphical pathway maps were not connected at all. The 62 remaining pathways served as target (or "true") pathways in the experiment. The statistics about the numbers of the node and edge in the target pathways were reported in Figure In order to generate input information for predicting the yeast metabolic pathways, the procedure de-scribed in the Method Section was applied to obtain gene-to-KO assignments and construct a relation knowledgebase for the yeast genes. The gene expression data set was preprocessed to filter out those genes with more than 20% missing values. The imputation of missing values for the rest of the data set was carried out with a R program by . The resThe yeast genes were mapped onto the reference metabolic pathways through the gene-KO assignments. The reference pathways share the same graphical structures with their corresponding specific yeast metabolic pathways, except that their nodes were represented in KO numbers. These initially mapped network structures comprised of the mapped genes and their relations were used as start points by the Bayesian network learning-based methods to construct more complete pathways. Besides the genes appearing in the initially mapped pathway network, another set of genes was sampled from the gene clusters. These two sets of genes were merged together to form a pool of candidate genes to predict metabolic pathway networks.The knowledge constraints about relationships of the candidate genes in the relation knowledgebase were provided for the Bayesian network structure learning method to select relations between the genes. Given the input genes and the knowledge constraints, we used a Bayesian networks structure learning tool implementing the knowledge-based method  to prediThe predicted pathway networks were comprised of directly connected nodes (genes), which represent the network structures of gene-product relationships underlying the metabolic pathways. The predicted results were compared with the 62 target yeast metabolic pathways obtained in the KEGG database. The comparison was conducted on relational edges presented both in target and predicted metabolic network structures. For example, given the underlying relation network of KEGG's Citrate cycle metabolism as target network shown in Figure A batch of the same experiments was carried out on the 62 target yeast metabolic pathway networks in KEGG. Figure The results in Figure In this work, we developed a new method combining the probabilistic Bayesian inference with observed relations and reactions to improve the completeness and accuracy of metabolic network construction. The results of this study indicate that the knowledge-based network inference approach was effective in predicting yeast metabolic pathways. It can extend an initially mapped reference pathway by selectively incorporating new relations / reactions in the relation knowledgebase extracted from all the pathway data in the KEGG database, which partially overcomes one major bottleneck of the most widely used pathway mapping methods. Furthermore, the knowledge-based network inference method consistently performs better than the pure ab initio probabilistic network inference method without exploiting the knowledge constraints in terms of both recall and precision, suggesting constraining the ab intio search space using relations / reactions extracted from other pathways is an effective way to improve the de novo prediction of a metabolic pathway. Overall, our experiment demonstrates that combining relation-level knowledge restraints and probabilistic graphical models-based inference is a promising approach to computationally reconstructing of metabolic networks.It is worth noting that our current implementation of the method did not predict compounds involved in reactions because no compound information was provided. However, the generic compound information can be added into predicted networks by extracting the compounds associated with predicted enzymes from the relation knowledgebase. Furthermore, the information about the compounds known to participate in a metabolic pathway , if available, can also be plugged in by comparing them with compounds normally associated with the predicted reactions.Our method can be extended in different ways. One aspect is to use different methods, such as function similarity rather than the gene expression clustering method to sample genes. Another interesting direction is to investigate the consistency between gene product relationships in predicted pathways and the statistical cause-effect relationships suggested by the Bayesian network inference. Moreover, the Bayesian inference methods used for learning metabolic network structures can be improved in terms of both recall and precision. Furthermore, more relevant data sources, such as protein-protein interaction in the protein interaction databases -30, protIn addition to reconstructing metabolic pathways, the novel way of exploiting existing knowledge at the relation level rather than the pathway level can be readily applied to construct other types of biological networks such as signal transduction pathways.The authors declare that they have no competing interests.


Bipolaris papendorfii UM 226, Daldinia eschscholtzii UM 1400, D. eschscholtzii UM 1020, Pyrenochaeta unguis-hominis UM 256, Ochroconis mirabilis UM 578, Cladosporium sphaerospermum UM 843, Herpotrichiellaceae sp. UM 238 and Pleosporales sp. UM 1110) were deposited in DemaDb. DemaDb includes functional annotations for all predicted gene models in all genomes, such as Gene Ontology, EuKaryotic Orthologous Groups, Kyoto Encyclopedia of Genes and Genomes (KEGG), Pfam and InterProScan. All predicted protein models were further functionally annotated to Carbohydrate-Active enzymes, peptidases, secondary metabolites and virulence factors. DemaDb Genome Browser enables users to browse and visualize entire genomes with annotation data including gene prediction, structure, orientation and custom feature tracks. The Pathway Browser based on the KEGG pathway database allows users to look into molecular interaction and reaction networks for all KEGG annotated genes. The availability of downloadable files containing assembly, nucleic acid, as well as protein data allows the direct retrieval for further downstream works. DemaDb is a useful resource for fungal research community especially those involved in genome-scale analysis, functional genomics, genetics and disease studies of dematiaceous fungi.Many species of dematiaceous fungi are associated with allergic reactions and potentially fatal diseases in human, especially in tropical climates. Over the past 10 years, we have isolated more than 400 dematiaceous fungi from various clinical samples. In this study, DemaDb, an integrated database was designed to support the integration and analysis of dematiaceous fungal genomes. A total of 92 072 putative genes and 6527 pathways that identified in eight dematiaceous fungi (Database URL:http://fungaldb.um.edu.my Alternaria spp., Bipolaris spp., Cladophialophora bantiana, Curvularia spp., Exophiala spp., Fonsecaea pedrosoi, Madurella spp., Scedosporium prolificans, Neoscytalidium dimidiatum and Wangiella dermatitidis are among the most important human pathogens commonly found in the tropical and subtropical climates , Malaysia. These clinical isolates were recovered from superficial skin samples, nails, subcutaneous tissues, and nasopharyngeal secretion, blood, and tissue biopsies  have been integrated into the DemaDb. Considering that dematiaceous fungal genomes will be generated from future projects, it is essential to manage and integrate the data generated from different analyses in a more organized manner. The current version of DemaDb is freely available at fungaldb.um.edu.my and an improved version with additional genomes is forthcoming.The rapid advancement of Next-Generation Sequencing technologies has led to the sequencing of many fungal genomes, paving the way to decipher their biology and the underlying mechanisms of fungal pathogenicity and multidrug resistance. Several web-based analyses are also available for annotation of genes predicted from high-throughput genomic data to gain insight into the fungal living system machinery . HoweverA total of eight dematiaceous fungi that isolated from various samples  were collected from University of Malaya between 2011 and 2014 . The genwww.jcvi.org/smurf/)  . The put/smurf/) .Figure E-value for the match in all predicted peptidases  developed by GMOD  is integThe DemaDb Pathway Browser based on the KEGG pathway database enables users to analyse molecular interaction and reaction networks for all KEGG annotated genes. Pathway Browser provides a summary of metabolic pathways of each fungus and number of genes in each pathway. Users can select the pathways that they wish to analyse without the need for laborious search for all genes in every KEGG pathway. The top 15 hit pathways are shown in the Pathway Browser .Figure All assembly, nucleotides and proteins data can be downloaded in FASTA format to accommodate users who wish to pre-process the data based on additional parameters or conduct large-scale comparative genomic analysis.DemaDb will be updated whenever a new genome and its detailed analysis are completed. Details for each release will be displayed in the homepage. Users are also encouraged to deposit and integrate their fungal genomes into DemaDb, which will be coordinated by the principal investigator of DemaDb. The users can send their assembled sequences (fasta format), predicted genes (fna format) or functional annotated proteins (faa format) directly to the principal investigator where the contact details are available in the ‘Contact’ section. The quality of the raw data will be accessed and reviewed, and, if approved, will be annotated and deposited into the DemaDb. Such collective works are important for comparative genomic analysis to gain a better understanding of dematiaceous fungi functional diversity and evolution.Up to date, we have produced a total of 398 ITS sequences from various dematiaceous fungi recovered from different anatomical sites. This large set of data will be archived into an updated version of DemaDb. Apart from the ITS sequences, isolation source as well as macroscopic and microscopic characteristics of these isolates will be incorporated to provide comprehensive information of each isolate. Also, a taxonomic engine will be constructed to allow users to identify their fungal isolate by conducting a similarity search against our curated database, and an ITS-based phylogenetic tree will be generated for taxonomic classification. This extensive collection of data would serve as a referral point for fungi isolated in tropical countries, such as Malaysia. As some fungal species appeared to be geographically restricted , this woTo the best of our knowledge, there is no integrated view of dematiaceous fungal gene expression profiles obtained from RNA-sequencing (RNA-seq). Currently, we are performing RNA-seq of these clinical isolates. In the future, the DemaDb database will collect these RNA-seq data and use a standardized method to identify the gene expression levels. Integration of RNA-seq data into the genome browser will be one of the new features in the upcoming DemaDb database.UM.C/625/1/HIR/MOHE/MED/31(No. H-20001-00-E000070)—principal investigator Professor Ng Kee Peng . Funding for open access charge: High Impact Research MoE Grant UM.C/625/1/HIR/MOHE/MED/ 31 (No. H-20001-00-E000070).This project was funded by University of Malaya and Ministry of Education, Malaysia under the High Impact Research MoE Grant Conflict of interest. None declared.


The maintenance of lactation in mammals is the result of a balance between competing signals from mammary development, prolactin signalling and involution pathways. Dairy cattle are an interesting case study to investigate the effect of polymorphisms that affect the function of genes in these pathways. In dairy cattle, lactation yields and milk composition (for example protein percentage and fat percentage) are routinely recorded, and these vary greatly between individuals. In this study, we test 8058 single nucleotide polymorphisms in or close to genes in these pathways for association with milk production traits and determine the proportion of variance explained by each pathway, using data on 16 812 dairy cattle, including Holstein-Friesian and Jersey bulls and cows.Single nucleotide polymorphisms close to genes in the mammary development, prolactin signalling and involution pathways were significantly associated with milk production traits. The involution pathway explained the largest proportion of genetic variation for production traits. The mammary development pathway also explained additional genetic variation for milk volume, fat percentage and protein percentage.Genetic variants in the involution pathway explained considerably more genetic variation in milk production traits than expected by chance. Many of the associations for single nucleotide polymorphisms in genes in this pathway have not been detected in conventional genome-wide association studies. The pathway approach used here allowed us to identify some novel candidates for further studies that will be aimed at refining the location of associated genomic regions and identifying polymorphisms contributing to variation in lactation volume and milk composition. There have been many attempts to identify the genes that control milk production and functional traits in dairy cattle since they have high economic value ,2. LinkaOne potential approach is to first filter genetic variants for association analysis by considering pathways of genes that are likely to be involved in lactation. The advantage of this method is that less stringent significance thresholds can be used than in traditional GWAS, since the level of multiple testing is not as high. This also means that associations of smaller effect can be detected. However, the approach does have the limitation that any mutations that affect the traits outside the selected pathways will be missed, which means that the variation we can identify may be reduced compared with that from whole-genome studies.For dairy traits, genes that are involved in mammary gland development, prolactin signalling and involution pathways are relevant candidates. Genes in the lactation pathway have been well-described but are largely inferred from mouse studies -13. DeveTo determine whether SNPs within key lactation pathways were significant for milk production traits, an association analysis was used. We analysed several traits, including fat kg, fat percentage, milk volume, protein kg, and protein percentage ,20. A ton represents the number of records [h2 is the heritability of the trait . Phenoth breeds :r2 is the repeatability  and l is the number of lactations. For the percentage traits, we were not able to fit weights for bulls in the model due to problems with convergence, likely because the heritability for these traits was high.where The linear mixed model used to determine the association between individual SNP and each milk production trait:y is the vector of phenotypes, expressed as the trait deviations for cows and daughter trait averages for bulls, β is the vector of fixed effects, including the overall mean and the effects of breed and sex, X is a design matrix allocating phenotypes to fixed effects, W is the vector of animal genotypes , b is the additive effect of the second allele of the SNP, Z is an incidence matrix mapping phenotype to animals, u is the vector of polygenic effects , and e is the vector of random residuals. The polygenic breeding values were fitted as random effects following a normal distribution A is the expected relationship among individuals constructed from the pedigree (which dates back to the 1940s) and where h ASReml .MAP kinase, P13K and frizzled were not included in the pathways since specific genes were not suggested in the reviews and these gene families have a wide range of signalling functions. The genomic location of these genes were determined using UMD3.1 in the NCBI database [−3 and P < 10−5) but 0.05 had the greatest power to detect enrichment (results not shown). The number of SNPs significant for each pathway was expressed as a proportion of the total number of SNPs in that pathway (PropSig).Gene sets for analysis were chosen using published reviews of three important developmental stages of the lactating mammary gland. These included the mammary development pathway  and the database . The SNPTo determine if the proportion of significant SNPs observed for each pathway was significantly greater than by chance at an experiment-wise level, distributions under the null hypothesis of no association were constructed with random permutations of the data. A list of 24 617 uniquely annotated bovine genes was created from the Ensembl Biomart database ,28. FromTo account for differences in functional clustering of genes in the experimental pathways and in the random control gene sets, we compared the distance between genes on the same chromosome [see Additional file KEGG annotations were used to determine the gene sets that represented other biological pathways ,32.Finally, a variance component analysis was used to determine whether the SNPs within each pathway explained a greater proportion of the genetic variance than an equal number of randomly selected SNPs from the whole genome. The model fitted wasg is a vector of random effects, assumed distributed G is a genomic relationship matrix, constructed using the rules of [where terms were the same as above, and rules of . The genEstimates of the variance components h ASREML . The proPropSig) was compared to the null distributions, the mammary development pathway was significantly associated with protein percentage  for each pair of traits. Milk volume was negatively correlated with fat percentage and protein percentage, while fat percentage and protein percentage were highly positively correlated  than the involution and mammary development sets, since it only represents only one signalling pathway, while mammary development and involution represent the combined effects of several sub-pathways Table  and [seeKEGG annotations for genes in the prolactin pathway showed 12 associations with the JAK-STAT signalling pathway, followed by the PI3K-Akt and insulin signalling pathways Table .ATF4, IGFBP4, IRF1, LIFR, OSMR, PTK2, STAT3, STAT5A and STAT5B , this pathway was still significant, even for protein percentage. Note that removing the GHR gene from the analysis is questionable because the growth hormone receptor is a vital component of the lactation pathway since it interacts with several relevant substrates during lactation [CEBPD, MYC and PTK2 genes on BTA14 (because they were in the region of DGAT1) did not affect the overall significance of the mammary development pathway. The clustered expression of the genes in a pathway, i.e. they are expressed with other secreted milk genes [The reason why the involution pathway explained the greatest level of variance in milk production traits in our variance component approach, although only half the number of SNPs of the mammary development pathway were available, could be because this pathway includes genes in or close to a previously described QTL with quite large effects on milk production traits Figure , particuactation . Similarlk genes , may resOne of the main limitations of our approach is that if a mutation that affects milk production is not in the analysed pathways, it will automatically be excluded. Perhaps even more importantly, our interpretations could be biased if irrelevant genes are included in the pathways. This may have occurred in cases where broad-acting cellular processes are represented in the gene sets. Improved descriptions of pathways would increase the power to identify genomic regions that influence these traits. The pathways used in this study were primarily derived from mouse studies and are relatively poorly described in cattle. For mammary development, the signalling interactions in the placode epithelium are particularly poorly described. For the prolactin signalling pathway, little is known about the downstream signalling of progesterone receptors. For the involution pathway, it is not known how membrane apoptosis is triggered although this would represent a significant contribution to the description of this biological process. Approaches such as microarray and RNAseq technologies using time-course data could help refine this method so that it represents more closely the true biological action. These approaches have successfully identified genes acting at different physiological states in the lactation cycle. Another potential limitation of our study is that the phenotypes were averages of several records across lactation. The same analyses could be performed using just early or late lactation records. Lactation curve parameters have been used in similar modelling experiments and may further refine these numerous SNP associations .Finally, the value of KEGG pathway annotations was questionable. The relevance of these annotations for the target traits is difficult to establish for genes that are involved in broad and numerous biological processes. A further problem is that KEGG annotations are heavily dominated by cancer-related information.We have successfully used the information from characterised mammary development, prolactin signalling and involution pathways to identify novel SNP associations with milk production traits. The proportion of significant SNPs in or near genes from the mammary development pathway was considerably greater than expected by chance for protein percentage. Of the three pathways studied, the involution pathway was highly associated with milk production traits and explained the highest level of variation above that expected by chance (up to 13% for protein kg). While we have reported many novel candidates useful for further studies, we must point out that pathway-based methods are restricted by the quality of annotations and completeness of pathway information.The authors declare that they have no competing interests.LR performed the GWAS and pathway analyses and drafted the manuscript. BC, BH, LR and MG conceived and designed the study. BH performed the variance component analysis. JP assisted in the interpretation. MG assisted with the pathway design. All authors read and approved the final manuscript.Table S1b. The minimum and maximum phenotypes for production traits in dairy cattle. Phenotypes are expressed in standard deviations, with a mean of zero within each breed. Adapted from [Number of phenotypes for production traits. Sample sizes adapted from . Table Sted from .Click here for fileDescription and measurement of milk production traits. All non-production traits are expressed as a percentage of the standard deviation from the phenotypic mean.Click here for fileFigure S1b. Distribution of number of daughters per bull. X-axis is labelled with the mid-point of each bin.Distribution of number of lactations for cows. X-axis is labelled with the mid-point of each bin. Click here for fileDistance between genes in lactation pathways and control permutations. Control plots are scaled to represent 10 000 replicates of randomly selected genes of size equivalent to the experimental pathway.Click here for filea) fat kg, b) milk volume, c) protein kg, d) fat percentage, e) protein percentage.Permutation tests for SNP within the mammary development pathway. Associations were created using 800 k SNP data from Holstein and Jersey cattle. Purple bars represent the null hypothesis distribution. SNP sets were randomised from the 200 kb region spanning 67 genes. The vertical red line is the experimental result (e.g. the observed proportion of SNP in that pathway), while the green line is the P ≤ 0.05 significance threshold for the pathway from the permutation test, for Click here for filea) fat kg, b) milk volume, c) protein kg, d) fat percentage, e) protein percentage.Permutation tests for SNP within the prolactin signalling pathway. Purple bars represent the null hypothesis distribution. SNP sets were randomised from the 200 kb region spanning 27 genes. The vertical red line is the experimental result (e.g. the observed proportion of SNP in that pathway), while the green line is the P ≤ 0.05 significance threshold for the pathway from the permutation test, for Click here for filea) fat kg, b) milk volume, c) protein kg, d) fat percentage, e) protein percentage.Permutation tests for SNP within the involution pathway. Purple bars represent the null hypothesis distribution. SNP sets were randomised from the 200 kb region spanning 40 genes. The vertical red line is the experimental result (e.g. the observed proportion of SNP in that pathway), while the green line is the P ≤ 0.05 significance threshold for the pathway from the permutation test, for Click here for fileGene abbreviations. Gene families are represented in bold.Click here for filea) mammary development with BTA14 removed and b) involution with BTA20 removed, both for protein percentage. Red lines represent the significance of the pathway. Green lines show the P value cut-off.Control permutations with major QTL regions removed. Description: Histograms show Click here for file


KEGG is a comprehensive public biological database that contains large collection of human curated pathways. KEGGscape utilizes the database to reproduce the corresponding hand-drawn pathway diagrams with as much detail as possible in Cytoscape. Further, it allows users to import pathway data sets to visualize biologist-friendly diagrams using the Cytoscape core visualization function  and the ability to perform pathway analysis with a variety of Cytoscape apps. From the analyzed data, users can create complex and interactive visualizations which cannot be done in the KEGG PATHWAY web application. Experimental data with Affymetrix E. coli chips are used as an example to demonstrate how users can integrate pathways, annotations, and experimental data sets to create complex visualizations that clarify biological systems using KEGGscape and other Cytoscape apps.In this paper, we present KEGGscape a pathway data integration and visualization app for Cytoscape ( It contains pathway data sets that have comprehensive annotations and high quality human-curated, hand-drawn diagrams. Most biological pathway databases store data as machine-readable graph topologies, which leave much of the details about how the diagrams were drawn excluded from the data files. This is a problem when third-party developers want to reproduce the pathway diagrams in their applications. In contrast, the KEGG PATHWAY database stores graphics information in machine-readable KEGG Markup Language  format. Thus, in these pathway diagrams, biological entities, such as enzymes or compounds, are manually laid-out and the diagrams are easy to understand for biologists.Kyoto Encyclopedia of Genes and Genomes . Furthermore, KEGG Atlas (http://www.genome.jp/kegg/atlas.html) provides a comprehensive network view of global metabolic pathways. Recent improvements to KEGG Atlas, such as Pathway Projector2 and iPath23, have made it possible to perform basic data integration and visualization like mapping the expression values to node graphics. However, despite these features, it is difficult to integrate external data sets and create custom visualization. Furthermore, they are limited to those on existing desktop pathway analysis applications. To ameliorate these problems, several projects for integrating a user’s own models onto the KEGG pathways have therefore been developed .The KEGG PATHWAY database is deployed as a web application using static bitmap images for pathway diagrams, and user-provided date is integrated with KEGG Mapper  and statistical network analysis (CentiScaPe9), which are also mostly open source software. Additionally, Cytoscape has a flexible network visualization function and is optimized for large-scale network analysis. There are several applications dedicated to biological pathway analysis  that support KGML by default. Although Cytoscape does not have a built-in function to load biological pathways, if this task is done with a separate app, users can take advantage of its large-scale network analysis features, variety of analysis apps, and data visualization of biologists-friendly human curated pathways.Cytoscapehttp://apps.cytoscape.org/apps/kgmlreader) for Cytoscape 2 series, is an app that imports KEGG pathway diagrams from KGML files and provides a new way to use KEGG pathway diagrams as data integration blueprints in cooperation with Cytoscape core features and an existing variety of apps. KEGGscape is completely re-designed for the new Cytoscape 3 API and supports signaling pathways in addition to metabolic pathways, including the global metabolic pathways used in KEGG Atlas  for converting XML elements directly into Java objects. This conversion creates two types of data: pathway topology and its graphical representations. Pathway topology and its properties are converted into CyNetwork and CyTable objects, which are the standard data model in Cytoscape 3. In KGML, all graphical information, such as the color of enzymes or shape of compounds is stored under <graphics> tag. Instead of setting the graphics details of nodes and edges directly from this information, Cytoscape generates Visual Style, which is a collection of default visual properties and visual mapping function, for each pathway based on the information under this tag. KEGGscape follows a standard CyNetworkReader design guideline, which enables Cytoscape to detect KGML files automatically.KEGGscape is a Cytoscape 3 app written in Java programming language and is designed to load pathway data files in KGML format. KGML is an XML file format designed by the KEGG project and contains the topology of pathways and visual representations of all elements in the diagram. KGML has formal specification as a DTD (Document Type Definition) file, which enables the use of unmarshaller (12), Python, or Excel. Once the data files are ready, Cytoscape can read them into an on-memory session and visualize the data on the KEGG pathways. Imported data sets only use standard Cytoscape data objects, and users can then access all of the standard Cytoscape features to create custom pathway visualizations. An actual workflow will be presented in a later section.Although KEGGscape can read all information of the pathways saved in KGML files, some of the pathway visualizations in Cytoscape look slightly different from the original hand-drawn diagrams available on the KEGG website. The cause of this issue is missing graphics information in the KGML files.As an example workflow, we integrated and visualized a KEGG pathway and gene expression profile using KEGGscape and external tools. In this example, the differentially expressed genes between two groups, mutants and controls, in a global expression profile are mapped on the KEGG pathway, as too are the t-test results.http://www.bioconductor.org/) to prepare the gene expression matrix data. We normalized Affymetrix GeneChip data by the robust multi-array average (RMA) method with the Bioconductor packages ecoliLeucine13 and affy14. The leucine regulatory protein (Lrp) is a DNA binding protein and known as a leucine responsive global regulator15. The p-value for each probeset between four lrp mutant strains and four control chips was calculated by rowttest method in genefilter package16. From this calculation, we obtained a list of genes that are differentially expressed . We sent these probeset identifiers to KEGG Mapper and picked the highest hit, which was the glycine, serine and thereonine metabolic pathway (KEGG ID: eco00260) for visualization.To perform this pathway analysis in Cytoscape, we used Bioconductor  from external platforms like R and Cytoscape to automate common tasks in the visualization process.http://apps.cytoscape.org/apps/keggscapeThe app website:https://github.com/idekerlab/KEGGscapeLatest source code:https://github.com/F1000Research/KEGGscape/releases/tag/V1.0Source code as at the time of publication:http://dx.doi.org/10.5281/zenodo.1056017Archived source code as at the time of publication:License: Apache License Version 2.0 de facto standard pathway visualization platform. Cytoscape-based visualization of KEGG pathways has always been cumbersome and limited. KEGGscape gets over many of these limitations by streamlining the process, and improving the translation of the graphical elements of KEGG pathways. This makes KEGGscape a very useful resource for the scientific community.KEGG is one of the foremost sources of metabolic pathway data. Cytoscape is theFirst, the authors should mention that KGML files are currently freely available on the KEGG website. This will be news to some readers of this paper, since KEGG has gone through various distribution and licensing models.Second, the authors should expand their discussion of merging gene expression data with KGML diagrams. The power of network visualization is in pathway data integration. If there are special challenges in integrating with KEGG pathways, these should be brought forth. The mention of custom python scripts and fuzzy ID matching suggests that this integration task can involve special challenges.The manuscript is clear and informative. There are only two changes I would ask for:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The paper describes an interesting and relevant Cytoscape plugin; it is well written (there are a few typos), but I think it can be improved in places. An important reservation is that I cannot determine at this moment how common or serious the data loss in KGML reading is (see below). That is it should be clarified whether this paper is a first report, a work in progress or a finished project..Title and AbstractI find the title appropriate, but it may be promising more than what the app actually does. As far as I can tell, the app is not a general solution, but one specific for KEGG. The abstract is short on setting the state of the domain and, like the Introduction, does not refer to existing research in fields beyond their own. In this way, the abstract is less than clear what unsolved problem was resolved.Introductionmost biological pathway databases" without citing or even naming them, which I find suboptimal as they critique those databases and to which the authors contrast their solution. Unlike the Introduction currently seems to suggest, KGML is not the only pathway format that tracks graphical information (e.g. GPML used by PathVisio). The first paragraph currently suggests it solves a problem that others have been tackling too. (P.S. under what license is the KGML specification available?).The Introduction sufficiently explains the setting of the plugin. One thing that strikes me is the lack of references to related work. For example, it mentions "The Introduction focuses on technical issues of the integration. It could also say a bit about identifier mapping and the role of licensing in data integration. That is, given  the enormous impact of KEGG it can be wondered why this kind of integration with KEGG has not been done yet, because people have done this for other data sets for years (both open source and proprietary). Or, if it has, it should be cited.Check the hyphenation of KEGGtranslator (KEGG-translator versus KEG-Gtranslator).The comment that Cytoscape does not have a built-in function to load biological pathways probably refers to formats, not the pathways themselves.Since the app focuses on KGML support, some reference to apps supporting other formats  seems appropriate.FiguresMinor comments:The figures are too hard to read, both in print (where it is impossible) but also in the PDF. This should really be addressed.ImplementationThe Implementation sections can be improved: the section is short on details on the KEGGscape source code, the design of the app and the KGML reader in particular, build instructions, development model, testing (tests seem absent in the code repository).I also note issues with the KGML readers. For example, connections clear on the KEGG website are missing and misplaced when read into Cytoscape  and labels are often misleadingly placed and often unconnected (a limitation of KEGG), though accurately copied from the KGML, it seems. The first data-loss clearly indicates a problem with the reader. These things must be discussed in the Limitations subsection, in my opinion (and/or returned to in the Future Plan at the end).on-memory -> in-memory?<graphics> tag -> <graphics> element .What is "Visual Style"?Under what license is the DTD available?ResultsMinor comments:The Results section seems to accurately describe what they did, but not how they did this. I think something like a methods section (or an Open Notebook) that explains how the steps are performed (in more detail) would be helpful. However, in the end I was able to figure out how to import a KGML file (which is not under Import).Another example is reference to a custom Python script without description. In fact, thinking about this again, for an app that does data integration, I would actually expect the app to take care of data processing as much as possible. While I understand that general preparation starting from raw data is best done in other tools, this Python script does not seem to do more than format handling of some kind. Is that correct? If so, why is this not done by the app? Fortunately, this observation was made by the authors too, and they return to it in the Future plan subsection.probeset -> probe setthereonine -> threonineConclusionMinor comments:The Conclusion section does not sufficiently summarize the paper: it should reiterate what the problem is that is solved and what makes KEGGscape unique. Although I do not want to imply that I think this paper is not relevant (it is)!Maven supports, may be a good start here, but is currently missing from the source code repository.Also the limitations should be returned to here , particularly in relation to the "Future plan". The authors’ focus should really be on getting the KGML reading performing well: data loss, and data corruption are serious issues. A decent testing toolkit ascheck the hyphenation of KEGGscape (KEGG-scape versus KEG-Gscape)Minor comments:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. KEGGscape is valuable Cytoscape app that enables flexible visualization and manipulation of KEGG pathway maps. The capability for visualizing data sets  on pathways in Cytoscape is particularly appealing. However, the example presented in the paper does not contain sufficient information to enable a reader to duplicate the process. First, the authors do not cite the origin of the gene expression dataset they use in their example. Second, as far as I can tell, there is no tutorial provided with KEGGscape. At a minimum, the authors should provide instructions for opening a KGML file and loading a dataset.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.


Large quantities of information describing the mechanisms of biological pathways continue to be collected in publicly available databases. At the same time, experiments have increased in scale, and biologists increasingly use pathways defined in online databases to interpret the results of experiments and generate hypotheses. Emerging computational techniques that exploit the rich biological information captured in reaction systems require formal standardized descriptions of pathways to extract these reaction networks and avoid the alternative: time-consuming and largely manual literature-based network reconstruction. Here, we systematically evaluate the effects of commonly used knowledge representations on the seemingly simple task of extracting a reaction network describing signal transduction from a pathway database. We show that this process is in fact surprisingly difficult, and the pathway representations adopted by various knowledge bases have dramatic consequences for reaction network extraction, connectivity, capture of pathway crosstalk and in the modelling of cell–cell interactions. Researchers constructing computational models built from automatically extracted reaction networks must therefore consider the issues we outline in this review to maximize the value of existing pathway knowledge. Mycobacterium genitalium, one of the simplest living organisms . This is a powerful and proper use of these representations, and does not significantly affect the extracted reaction network. However, meta-entities may also be used for ease of data entry and visual representation. This latter use can, as illustrated, cause unintentional loss of connectivity. Ideally, database designs should distinguish between the two, and only the former should be captured in the exported underlying data.Collecting distinct entities into meta-entities serves two purposes. Firstly, use of meta-entities allows the compression of information that would otherwise cause a database to undergo a combinatorial explosion in size  appears in the related ‘APC/C-mediated degradation of cell cycle proteins’ (REACT_6828.1) pathway. The signalling events in which these molecules participate are described in the same publication , yet havThis has important implications for the interactions between pathways , and requires additional checking against previously grouped sets of molecules. Such crosstalk has been demonstrated to be of key importance in regulation of phenotype , 45. StrInteractions and reactions described in pathway databases are recorded as occurring between entities that are in a specific subcellular location, such as the cytosol, extracellular region or various organelles. This information is useful in determining the reactions in which a molecule can participate, and captures sequestration and compartmentalization within the cell, in addition to allowing description of events related to secretion and molecular transport.Mycobacterium tuberculosis (tuberculosis) infection. Interactions in this pathway occur in the host cell, in the pathogen or in the extracellular region between the two, and are obviously separated in the pathway visualization. However, no distinction is made between the cytosol of different cells in the exported version (BioPAX Level 3) of the Reactome database (Supplementary Data S1). This results in the effective fusion of the cellular systems of the pathogen and its host, and prevents accurate simulation of the interactions as illustrated in Botulinum toxin’ inadvertently describing Botulinum neurotoxins (e.g. P18640) as human proteins, and the attribution of West Nile Virus RNA to H. sapiens in the BioPAX version of the BioCarta-sourced ‘West Nile Virus’ pathway retrieved from the NCI-PID database.Almost all of the databases mentioned so far also contain information detailing interactions occurring between cells. Recording the cellular location of a given entity becomes more difficult when two (or more) cells interact within the system. For example, the Reactome database contains information governing host–pathogen interactions in latent Additional problems arise in the context of whether a reaction can be considered to be part of a normal healthy cell or tissue. Diseases that affect cellular signal transduction are frequently included in databases, but do not distinguish between normal events in healthy cells and events that occur owing to infection. Similarly, databases do not uniformly record information about the tissue specificity of signalling pathways and reactions. This can cause problems with model quality when, for example, synaptic signalling reactions are incorporated into models of inflammation.One suggestion for avoiding this unintentional merging of organism-, tissue- and disease-specific entities and interactions would be to specify databases at the reaction level with respect to a single canonical cell free of disease, and to support such a model in the specification of data exchange standards. Additional cell types can then be added to provide data relating to additional interacting cells or organisms. A model of an otherwise healthy cell with latent tuberculosis infection would then be represented as the interaction data available for the canonical cell, with the addition of interactions associated with the cell type describing the pathogen. Any contradictions between the two sets of interactions would then have to be resolved. Such an approach would make it easy to generate reaction networks describing both healthy and diseased cells, and even allow generation of clean data sets covering multiple infections (such as HIV-infected cells with latent tuberculosis) for simulation and analysis, a highly desirable outcome for applications to personalized medicine.Databases, including Reactome, KEGG, PID and Panther Pathways, containing broad-scale information represent the best collections of high-quality reaction and interaction data available to the scientific community today. These data sets have immense potential in predictive and explanatory cellular-scale modelling, and enable systems-level descriptions and models of cells.Features and undocumented issues surrounding the implementations of these databases have important implications for computational network modelling. Databases are designed with multiple stakeholders in mind–the database designers, the curators and data entry teams, small-scale analysts, large-scale analysts and people using visual analysis of pathway maps, all have different requirements, and some of the issues we discuss here are a product of compromise among the competing demands of these stakeholders . Our anaOur analysis has focused on signal transduction systems; however, databases describing these and other processes in a systems context face similar challenges. A good example is that of metabolism, where the primary modelling techniques require mass- and stoichiometrically-balanced reactions. Grouping of entities through reactions at the database level makes instantiating models based on these data vastly more difficult. Addressing these issues for metabolic systems has required extensive reworking and modification of databases to ensure that they can be used for and facilitate modelling applications .The advent of whole-cell computational modelling and the expanding use of high-throughput techniques require ever more-sophisticated data analysis methods. While established first-generation methods such as enrichment analysis do not suffer greatly from the issues described here, the structural issues that we discuss become limiting and will be addressed as the field moves beyond enrichment and towards mechanistic models. Handling these issues and thus facilitating systematic extraction of basic reaction information will allow development and improvement of computational data analysis using these data sets. This combination will drive new and exciting experimental work, and dramatically enhance our ability to work with and exploit cellular level phenomena in a systematic, evidence-based fashion.Supplementary data are available online at http://bib.oxfordjournals.org/.Large-scale models using prior knowledge about detailed signal transduction mechanisms are becoming more common.These models require the extraction of a reaction network, and one source of such data is community-sourced data in major databases.Knowledge representation in these databases has unintended consequences for computational modelling.Use of sets of entities and duplication of entities cause unintended loss of connectivity in database-derived reaction networks.Multicellular interactions are not currently well captured in databases and can result in misleading reaction networks.Additional work is needed to capture behaviours such as splice variation of proteins in a way that does not further hinder analyses.


There is a need for formalised diagrams that both summarise current biological pathway knowledge and support modelling approaches that explain and predict their behaviour. Here, we present a new, freely available modelling framework that includes a biologist-friendly pathway modelling language (mEPN), a simple but sophisticated method to support model parameterisation using available biological information; a stochastic flow algorithm that simulates the dynamics of pathway activity; and a 3-D visualisation engine that aids understanding of the complexities of a system’s dynamics. We present example pathway models that illustrate of the power of approach to depict a diverse range of systems. This Community Page presents a biologist-friendly method for compiling detailed graphical models of biological pathways from the literature, including their subsequent parameterization for use in dynamic simulations of their activity. This Community Page is part of the Cool Tools Series.Biologists the world over love a good diagram. The old adage of a picture being worth a thousand words holds true. Diagrams continue to form the bedrock of our efforts to communicate ideas about the complexity of biological systems and pathways. They act as pictorial reviews of what is known about the components of a given system and how they interact. However, there is a fundamental problem with most biological pathway diagrams: they are produced on an ad hoc basis with no rules governing their composition or content. As a result, they convey information only in the narrow context of the accompanying legend, text, or talk, and as a collective resource are of limited use as they cannot be directly compared or integrated.If pathway modelling is to be more widely adopted by biologists and the results of their efforts useable by others, a new approach is required. Here, we describe a modelling framework that consists of a formalised graphical language that supports the depiction of complex biological interactions and molecules, allowing the creation of detailed models of events based on known information. Once parameterised, these models can then be used directly for experiments that seek to simulate their activity under a variety of conditions. The approach utilises flexible and simple-to-use tools that make diagrams easy to draw and edit as new knowledge arises, and to model their behaviour. Most importantly, the resulting pathway diagrams are useful in the communication of ideas, in hypothesis testing, in enhancing the understanding of the dynamic characteristics of the system modelled, and thereby in helping to drive forward biological research objectives.The post-genomic era has led to the generation of a massive amount of data on the interactions between molecules across cell types and disease processes. Pathguide.org  lists anwww.yWorks.com) software is the primary pathway drawing tool, providing a platform that is robust, flexible, and free to use. It has a range of features that allows a user to link parts of the model to external resources or other supporting data  and provides an easy-to-use platform for diagram visualisation and editing. An extended description of construction of mEPN models is provided in http://dx.doi.org/10.1101/047043).A number of years ago, we started to explore the problem of how does one draw a “good” diagram of a biological pathway. In order to ensure the usability and widest appeal of our approach, we have strived to ensure that the diagrams  produced were able to depict events as described in the literature in as much detail as possible whilst maintaining their readability. We also wanted the approach to be understood and useable by relatively junior biologists, as in many cases they were the ones constructing models. To these ends, we developed the modified Edinburgh Pathway Notation (mEPN) scheme . These shapes are not supported by yEd or, indeed, other generic network editors and are not used in the mEPN scheme.Protein complexes are represented in SBGN using either a “stack of proteins” glyph in the case of homo-multimers or as a series of individual protein subunit glyphs within container node in the SBGN scheme. In mEPN, all such entities are represented as a single rounded rectangle glyph in which the number and name of constituent entity nodes are defined by the node’s label; we see no advantage graphically or otherwise in rendering complexes by the means proposed by SBGN. In drawing larger complexes, we have also often found it useful to show individual proteins/subunits as individual entities joined by edges with no arrows  so that the structure and relative position of individual subunits might be better communicated graphically (see SBGN has specific glyphs for association (binding) and dissociation, but almost all other processes are represented by a square box. This gives no information on the process they represent and is an important omission. In mEPN, we currently list 40 types of processes that one might wish to define when constructing a pathway, including all of those associated with the BioPAX level 3 pathway exchange language . Whilst We have simplified the list of edges representing interactions between entity nodes down to interaction, catalysis, inhibition, and the special use case action potential, and do not recognise many of the options available listed by SBGN , as we have found them difficult to explain to biologists and not necessary to the depiction of pathways.Finally, perhaps the most important issue for computational modelling purposes is that in the current version of mEPN, we define all nodes as being either places or transitions, a division that makes biological sense in terms of what they represent and conforms to the requirement of Petri net models.Express3D, and SBGN diagram or a Petri net can is shown in An example of a simple model of the signalling pathway of Interferon-β (IFNB1) represented in the mEPN scheme, BioLayout At its most basic level, a pathway diagram constructed using the mEPN scheme is a simple network composed of nodes and edges. Nodes fall into one of two categories representing either pathway entities or processes. Entity nodes generally represent biomolecules , and each class of entity is represented by a glyph of a particular shape. Process nodes represent an event occurring to or between entities , and are generally represented by a circle with a two- to three-letter label distinguishing between the different types of processes they represent. The lines that join nodes, called edges, define the direction of an interaction and in some cases the nature of it . A model of the well-known pathways of the glycolysis and the tricarboxylic acid (TCA) cycle constructed using mEPN is shown in A hallmark of many modern biological investigations are genome-scale analyses, which continue to produce huge amounts of data. These inevitably implicate hundreds or thousands of biological entities as being of interest due to their change in abundance in a given experimental or clinical paradigm. However, interpretation of the meaning of such observations represents one of the greatest challenges in biology today. Gene Ontology (GO) enrichment analyses may indicate that a set of gene or protein “hits” are implicated in a given pathway or process, but what role they play or how they function together is not defined. Similarly, and as mentioned above, protein–protein interaction databases may be used to find out if there is any evidence that a set of proteins share a functional association, but the information returned is generally without context. An alternative approach is to overlay results onto pathway diagrams, thereby indicating which of those represented have been regulated. The researcher can then understand not only which pathways have undergone regulation but which specific elements have been altered, allowing them to hypothesise how a given perturbation might affect a pathway’s activity or downstream events. This method is very useful but limited by the quality and scope of the reference pathway diagram. There are a number of tools that currently support data visualisation in the context of networks and pathways, including CellDesigner , NaviCelThis brings us to perhaps the most challenging aspect of pathway modelling: that of simulating their activity. To truly comprehend a pathway, one needs not only the pictorial representation or “connectivity map” but also to understand the dynamics of the system under different conditions. Computational and mathematical modelling studies aim to simulate biological systems to explain a pathway’s observed activity. When this is achieved, models can be used to explain or predict a system’s response to perturbations  or to predict missing components. Over the years, numerous computational models have been published for a diverse assortment of biological systems (see the BioModels database for examples ). ResearExpress3D (www.biolayout.org). As part of this development, we enabled the import and visualisation of mEPN models and thereby the simulation of activity flow through such models. In order to achieve this end, we modified the mEPN scheme to define all nodes as representing either places  or transitions . An extended description of the SPN modelling system is provided in http://dx.doi.org/10.1101/047043).Process diagrams share much in common with Petri nets, a modelling approach that has been used to model many different types of systems, with a growing literature describing their use in modelling biological pathways –41. PetrModels need to be parameterised prior to running a simulation, i.e., the initial conditions for the experiment need to be set. With this modelling platform, parameterisation is, in principle, relatively simple to perform and is based on information that may be available to a biologist. The primary parameterisation is the structure of a model itself; this is the major determinant of where tokens can flow to and from, and how long it takes for them to move from place to another. Parameterisation is also carried out through the placement of tokens on entities at the starting points of a network, i.e., on places that have no parents. An entity without tokens, or where token number is set to zero, can be considered not to exist . The more tokens placed on an entity node prior to running the model, the more of that entity (or the greater its activity) at the beginning of a simulation. The assignment of tokens to an entity node can be arbitrary or inferred from experimental evidence, such as gene expression or proteomics data, where available.Express3D. BioLayout has been designed to recognise the different glyphs used in the mEPN scheme and translates all 2-D mEPN glyphs into their 3-D equivalents, e.g., circles become spheres, rounded rectangles become rounded cuboids  and connect with a transition node, i.e., the process that is to be inhibited. This system includes two types of inhibitor edge representing non-competitive and competitive inhibition. The first operates on the basis that if any tokens are present on the inhibitor, flow through the transition is completely blocked. In the second case, the number of tokens residing on the inhibitor is subtracted from the number of tokens flowing through the transition. As no tokens are lost through inhibitor edges, it has become standard practice to draw inhibitor places with an associated output transition (sink). Without this, flow through a negative feedback loop is completely and irrevocably stopped as tokens accumulated on the inhibitor remain there, blocking further flow through the target transition. An inhibitor with a sink transition node attached effectively has a half-life (as tokens are lost from the inhibitor via the sink) and, depending on the configuration of the feedback loop, e.g., path length between input and inhibitor and type of inhibition (amongst other factors), the system may exhibit a range of oscillatory activities .Described above are the characteristics of flow simulations through basic pathway motifs. It should be noted that this method is scalable, and large pathway models consisting of hundreds or possibly thousands of nodes can be constructed and used in flow simulation experiments. We present a recently constructed model of the influenza life cycle in macrophages as an example of such a large-scale model . See alsDescribed above are the basic characteristics of the flow simulation algorithm and how this approach maps onto pathway diagrams assembled using the mEPN scheme. This interoperability between an advanced graphical notation scheme and a powerful and rapid dynamic modelling platform opens up pathway modelling to a wider audience. However, modelling a pathway of any size is not a trivial undertaking—it takes skill, knowledge, and dedication. From a biologist’s perspective, we would argue that the time and effort required to engage in this activity is rewarded. For most researchers, a current pathway diagram describing their system of interest will not exist, and constructing it themselves can be an extremely useful exercise. It provides an opportunity for a biologist to formalise what they know in a form that can be communicated to their peers or coworkers. It also provides a platform where additional information can be added by others, thereby offering a medium for debate and a route to establishing a common understanding. Indeed, one of the most valuable aspects of pathway modelling is the process of model construction itself and the opportunity it provides to systematically collect and discuss available knowledge. By performing this act, it exposes what is not known or, as is sometimes the case, why events as described could not possibly work the way they are reported to. In a teaching environment, students can be engaged in the activity. They gain valuable experience in learning to mine the literature. They have to learn to be critical of the reliability of information and to judge how to represent information when it is incomplete or conflicting. How they go about this task and what they produce in terms of a “finished” diagram can be judged and marked in the same way as any written assignment.Virtually Immune website  and BioLayout Express3D as a means to model their dynamics. In these respects, we believe that the computational framework presented here represents a major advancement in modelling biological pathways. We also envisage that the tools and approaches described here will be useful to other groups: for instance, those investigating single cell biology, where the response of individual cells in a population is heterogeneous and stochastic [The modelling framework described here is flexible enough to model all the basic network motifs found in all biological pathway types ,44, alloochastic  or synthS1 FigExpress3D, the software automatically recognises it and prompts users to run an SPN simulation using this window. The user defines the number of time blocks and runs, whether the variance is displayed, the form of stochasticity with which the model is parameterised, and the transition type. The set-up used for generating visualisations in this article is illustrated. (B) On completion of the SPN simulation run, this window provides a time for the running of the simulation and prompts the user to repeat the simulation, save the SPN results, or open the animation dialog. (C) The animation control dialogue window allows the visualisation of the SPN simulation to be defined. Size and colour of nodes as well as the appearance of token flow and the speed of the animation can be defined here.(A) When an SPN-ready model is loaded in BioLayout (PDF)Click here for additional data file.S2 Fig4 virions/cell Virus Output). The model outputs are comparable to the levels of (F) mRNA and (G) protein accumulation seen in previously reported experimental in vitro infection.(A) Illustration of the modular structure of the IAV life cycle that facilitates its readability and allows for pathway expansion as new data become available or as a focus of interest grows. (B) When our virtual cell is challenged by a limited amount of virus , neuraminidase (NA), matrix protein 1 (M1), and nonstructural protein 2 (NS2) both at the (C) mRNA and (D) protein level leading to (E) viral progeny (approximately 1E(PDF)Click here for additional data file.S1 GraphML(GRAPHML)Click here for additional data file.S2 GraphML(GRAPHML)Click here for additional data file.S3 GraphML(GRAPHML)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.S1 Video(MP4)Click here for additional data file.