Systems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data.We show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes.Our study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.The online version of this article (doi:10.1186/s13040-016-0102-8) contains supplementary material, which is available to authorized users. A major effort in translational medicine is to understand the molecular basis of disease , 2. Analparticipation (protein A participates in Pathway X), sequence similarity (protein A is sequence similar to protein B) or protein interaction (protein A interacts with protein B), and typically, will be associated with some provenance information. Generally, the relationships will describe complex networks. Some genes or proteins, for example, may be associated with multiple diseases, while others may be implicated in only one or two conditions. Additionally, some candidate genes may encode large families of sequence-similar proteins, and may thus be involved in multiple protein-protein interactions. Identification of these wider relationships between entities can help in providing biological context and in hypothesis generation on disease mechanisms. Traversing paths through the generated network can suggest hitherto unexpected relationships between entities, (e.g. relationships between disease conditions).However, in order to build a higher level picture of the underlying processes involved in the disease pathology, it is necessary to integrate various classes of heterogeneous information, and to explore the complex relationships between entities such as diseases, candidate genes, proteins, interactions and pathways. These relationships could have a variety of different types, like Biological information is typically highly connected, semi-structured and unpredictable, and these features are important in deciding an appropriate way to represent and query disease-relevant biomedical networks. Representing multiple types of relationships will generally lead to highly connected networks. Additionally, the amount of information associated with a given entity will not be uniform. Some proteins have been extensively investigated experimentally, are well annotated and associated with multiple complementary pieces of information. For example, a gene may be involved in a protein-protein interaction with another protein, may be a drug target for a particular drug, may be known to be enhanced in a particular tissue type or may participate in a particular pathway. Other proteins can have little or no information associated with them and are annotated only as ‘hypothetical proteins’. Therefore, such datasets do not naturally fit into a classic relational database model which would have been originally designed to handle application cases with a large number of uniform entries associated with a limited number of data types and representing relatively few relationships in each data model. Although highly connected and sparse networks can be stored in a relational database, generally traversal-type queries (joins), which connect data linked by different relationships, become too computationally expensive and cumbersome to design. Traversal type queries are important for hypothesis generation, as they can reveal paths connecting entities that might not have been expected, and might not be apparent from visual inspection of the network. Another issue is that biological data are often semi-structured, i.e. lacking a structured data model, while still possessing some semantic annotation. As relational databases require all data to be transformed to conform to a pre-defined schema, developing parsers for such semi-structured data can be particularly problematic. Biological research is also unpredictable in that new types of information can emerge with little advance notice – for example when new analytical instruments or new data resources are released. In a relational database management system, capturing these new data types often means re-design of a database schema, which is generally a complex and costly process. However, in a research setting this information is often of great importance and can impact on our understanding of the roles of genes and pathways in disease networks. A framework that facilitates the rapid and seamless inclusion of new data types would therefore be particularly well-suited to support the dynamic and ever-changing requirements of biomedical research community. Graph databases are a natural way to represent highly connected, non-uniformly distributed, semi-structured and unpredictable data as found in many biological systems studies , 4. TheyRecently, there has been much interest in network representations of biological systems, particularly for network visualisation and network analytics. Several successful frameworks have been established and used to support bioinformatics analysis including, but not limited to, Cytoscape , Gephi  and NetwSeveral graph-based approaches have been developed for biological data integration such as Biozon , BN++ 99, Ondex The need to move towards considering graphs as biological knowledge repositories is particularly evident in the biomedical domain. Many important diseases have now been extensively researched for decades and the amounts of knowledge accumulated are both vast and heterogeneous. The large volume of interconnected data means that its exploration using a network visualizer cannot be carried out easily and the heterogeneity of the data can make cost-effective management difficult in a relational database system.In this work, we have explored the potential of using a graph database to facilitate data management and analysis in order to provide biological context to disease-related genes and proteins. This application case used the well-established Neo4j graph database for building and querying a prototype disease map for a complex disease, such as asthma. Neo4j has a free community edition that can be run as a lightweight desktop database as well as a database server, and an SQL-like versatile query language for graphs called Cypher. The database comes with an immediately available easy-to-use web interface that can display the results as both graphs and tables and also has interfaces to R, Python and Java - core languages of modern bioinformatics research.We have integrated information from human protein-protein interactions, pathways, sequence similarity, disease-gene, gene-tissue and protein-drug associations. The selected examples are intended to illustrate how the Neo4j Cypher query language can be used to perform complex queries on this integrated dataset. We also show how information from gene expression studies can be easily added to the database and how the database can provide biological context for these differentially expressed genes. The main advantage of presented approach is in its ability to contextualise disease-associated genes by facilitating the identification and visual exploration of their network neighbourhood. In addition, as we have already explained, graph databases excel in traversal-type queries, facilitating the exploration of chains of connected concepts. This ‘link discovery’ can lead to new hitherto unexpected relationships being identified. Therefore, in our application case we have aimed to explore both of these data mining strategies, namely discovering connections between different entities of biomedical interest  and characterising the relevant context of diseases and biomarkers (queries in Listings 3 and 4).omics data , which provide a low-level perspective on the relationships between biological entities . Then we have added the more general information that relates these lower-level entities to the higher-level processes – e.g. different diseases. The queries presented here are intended to provide illustrative examples about how to leverage connections across these two levels – for example, by discovering links in the multi-omics layer relevant to understanding the differences between different related diseases or using that information for suggesting drug targets.The knowledgebase we constructed is organised according to the following principles. First, we integrated several typical multi-A graph database offers several options for representation of data. Connections between entities (i.e. nodes) can be encoded directly as edges linking them together . Additionally, properties can be associated with nodes , and nodes that share a given property could be understood to be linked. And lastly, nodes can be linked via edges to an intermediate node indicating a common property . For this application case, the choice was guided primarily by the concerns of efficiency in capturing the structure of the data. For example, a property is an appropriate choice when annotation is simple, like a category label. An edge can have properties of its own, so can capture situations where the relationship is qualified, like a protein-protein interaction with given methods  and associated with a confidence score.TM for the UniProt Human reference proteome on the Timelogic DeCypher system. The significance threshold was 1e − 05, with effective database size adjusted to 16.9 bln bases. The results were further processed by retaining bi-directional hits only and modelled in the graph database as sequence similarity edges. The set of sequence similarity data was refined to exclude duplicates based on the UniProt id pair: the similarity between protein1-protein2 and protein2-protein1 was represented by a single, undirected edge. A score was assigned to each edge, calculated as a negative log10 of the average of the two e-values; only hits for which the ratio between the alignment length and the shortest sequence was above 0.60 were included.All data used in this application case were imported into the Neo4j v2.3.1 graph database. The integration was done from a protein-centric perspective, where the nodes corresponding to the individual entries in the human subset of UniProt/Swissprot served as key anchoring points for all other types of annotation. As many of the databases we used are continuously updated, the point of reference when all data were acquired for this study is 27/03/2015. Sequence similarity was determined by running all versus all Tera-BlastPProtein-protein interaction data were imported from the EBI IntAct database  . In this paper, the UniProt identifier gives the label of the Protein node, but other information (e.g. gene name) can be chosen from the Neo4j interface to be displayed as the label as well. Information on the ENTREZ gene name and the protein name, specific to the Protein nodes shown in Fig. New experimental data may have significant impact on hypothesis generation on disease mechanisms. High-throughput transcriptomic experiments are a common original source of such new information, and are routinely used to identify lists of new potential candidate genes of biomedical interest. To evaluate the suitability of graph databases for management and integrative analysis of these data types we have included several such studies from the GEO database. We have extracted gene expression data from three asthma-related GEO studies, namely GSE27876, GSE43696  and GSE6 package . The genFigure Unlike the relational database, where tables and relationships are heavily influenced by technical constraints formalized as “normalization rules”, graph databases offer much more flexibility and are more closely guided by the needs of the intended application. For this application case we have chosen the entities that correspond to either logical or real entities of biomedical interest. From the multi-omics perspective, the two physical entities central to our application case are proteins and drugs. These can be members of multiple thematic sets – e.g. the proteins can be grouped by disease involvement, tissue and metabolic pathway and drugs can be linked to particular diseases. To allow us to explore the potentially complex inter-relationships between these sets, they are also represented as nodes and therefore can be referenced directly as part of the query pattern. The edges are used to indicate both i) the membership of an entity in a particular set (e.g. protein participates in a pathway) and ii) existence of relationships between individual entities (e.g. protein interacts with other protein).The level of the detail was chosen in accordance with the need of our intended application case. For example, in theory it may be valid to model an interaction as a concept rather than an edge and then link it to specific tissues to indicate whether it can occur there. However, by doing so an extra node and two edges will be introduced into the graph, which will increase the computational complexity of querying the database and make query construction more challenging for the user. In this case, due to the applied nature of the work presented here, it was therefore more appropriate to opt for a more generalized representation. However, we recognize that often it may be necessary to develop detailed representations in order to capture provenance and in the case of broad-content rather than thematically focused resources. To demonstrate how such a scenario can be accommodated in Neo4j, we have modelled transcriptomics component of our dataset in a way that captures all information about its provenance (GEOStudy node), experimental design (GEOStudy to GEOComparison relationship) and the analysis used (GEOComparison node) to process the data.Here we illustrate the use of a graph database for facilitating hypothesis generation on disease mechanisms. We describe four queries related to asthma, a complex respiratory disease whose symptoms include airway inflammation and remodelling. Asthma is associated with multiple phenotypes and co-morbidities and has similarities to other respiratory diseases such as COPD. A significant number of asthma patients do not respond to conventional corticosteroid therapy, suggesting a complex underlying aetiology of this disease at both physiological and molecular levels.We first show a simple query that returns proteins, (given by UniProt identifiers), which are common to asthma and other respiratory diseases, , and essential hypertension (E-HTN) respectively) using information from two sources, a review by Kaneko et al. 2013)  and, sep13  and, Listing 1: Cypher query to identify proteins common to asthma and other respiratory diseases using Kaneko et al. (2013) data Listing 2: Cypher query to identify proteins common to asthma and other respiratory diseases using DisGeNET data only Queries can easily be performed to select differentially expressed genes and relate them to the rest of the network. This example compares the network neighbourhoods of two sets of differentially expressed genes (DEGs) between Normal Control (NC) and Severe Asthma (SA) from two studies, GSE43696  and GSE6Listing 3: Cypher query to identify DEGs that map to asthma and other respiratory diseases as well as to signalling pathways.We have queried for a set of drugs from DrugBank DB that target proteins of similar sequence to known asthma biomarkers (associated to asthma disease through the Biomarker relationship in DisGeNET). The query, pattern and part of the relevant network are shown in Listing 4, Figs. Listing 4: Cypher query to identify drugs associated with proteins that have sequence similarity with asthma biomarkers.KRT8 gene) has sequence similarity with the P08727 protein, which was reported as a target for Tenecteplase,  and Tenecteplase (drug), an indirect relationship can be identified between these two entities given that the P05787 protein (Studies suggest that asthma patients may experience exacerbations early in the morning (see for example ). IndepeListing 5: Cypher query to explore shortest paths (in terms of graph representation) between core clock genes and asthma.CLOCK gene) is linked through Sequence Similarity and PPI relationships by Q99743 (NPAS2) and O00327 (ARNTL/BMAL1), respectively, and the protein Q49AN0 (CRY2) has sequence similarity with Q16526 (CRY1). Interestingly, the O15534 protein, encoded by PER1, also a core clock gene, (Table PER1) and the rest of the core clock gene subset  which does not appear on the shortest paths network in Fig. ARNTL/BMAL1) and Q99743(NPAS2), .In traditional relational databases approaches, the technical structure often gets in the way of exploratory data analysis either by visualisation or through data mining techniques. Graph databases (explored here with Neo4j) offer a powerful but lightweight, intuitive and flexible solution that is more readily compatible with a biological network view, a representation that is now near-ubiquitously employed for modelling complex biological data. This study shows how a combination of a powerful query language (to narrow down the search space) with network visualisation can help data exploration and hypothesis generation. The use of graph database approaches has some similarities with previous systems for logic-based modelling of integrated datasets , 30.As we have illustrated in this work, graph databases offer many features that make them a particularly attractive option for a research-based setting, in particular in cases where it might be necessary to dynamically develop and interactively mine heterogeneous and not uniformly inter-related data. However, we would like to stress that in our opinion graph databases are best thought of as a complement to relational database technologies. There will clearly be cases where a relational database solution will be preferable, especially when data are dense and naturally fit the tabular representation where relational databases would offer much better processing performance.Another important consideration is the place of Neo4j in a wider family of NoSQL and especially graph databases. The concept of graph databases is still relatively novel and therefore currently there are multiple alternative systems under active development. However, one standard – Resource Description Framework (RDF) and its enabling technologies (triple stores and SPARQL query language) have gained particular prominence due to their importance for Semantic Web and Internet-powered federated data solutions. For this reason we would like to further elaborate on the differences between these two standards and specifically the roles they could potentially play in management of large biomedical datasets.Neo4j database belongs to the family of “Property graph” databases, which in contrast to RDF have conceptually distinct property elements that can be attached to both nodes and edges. In some respects, this syntax may be more familiar to users of object-oriented languages, as in contrast to RDF, there is neither a global uniqueness constraint nor specifically required format for node identifiers. In combination, these features make graph query syntax more compact and easier to read. Given that the Neo4j database is inherently a “closed world” solution, it is possible to collect the entirety of meta-data about the types and number of different entities and relationships between them, which can then inform further exploration. As SPARQL and RDF are primarily designed to operate in an “open world” setting of the Semantic Web, creating such summaries may not always be possible or can be very computationally intensive.However, we would like to acknowledge that the RDF formalism does have its undisputed advantages in facilitating integration and exchange of distributed data and issues like lack of properties on edges can be addressed by leveraging more advanced features like reification or named graphs. In our view, the matter of preferences between the properties databases like Neo4j or RDF triple stores comes down to whether a particular application case can actually benefit from the extra features available via Semantic Web technologies. As adopting such technologies will inevitably come with considerable costs in terms of technical overhead and increased complexity, property databases may be preferable when speedy and flexible development of a single non-federated resource is the ultimate requirement.The lack of schema in Neo4j may also be somewhat of a double-edge sword: although it does offer much flexibility, it also removes the interoperability standard from the data, which may make sharing and management of large distributed projects datasets more challenging. One possible solution to this would be to re-introduce some consistent semantics by binding critical attribute names and/or values of nodes and edges to a consensus controlled vocabulary. In a biomedical domain, this controlled vocabulary will likely come in the form of a suitable ontology. The ontologies could then provide a de-facto data modelling standard and many key concepts in the biomedical domain have already been modelled by projects like SNOMED , MeSH 3, GO 33]33] and OWe have explored here the utility of a graph database in providing a powerful yet flexible solution for disease network representations and for exploratory data mining and analysis to support hypothesis generation on disease mechanisms. Our study suggests that the Neo4j system offers a level of performance and an appropriate query and visualisation interface to effectively mine and manipulate these data. In particular, we have found the Cypher graph query language to be of great utility, because, as illustrated by our sample queries, it enabled us to generate a representative selection of common biologically-motivated queries with minimal efforts. As systems medicine projects continue to generate large amounts of heterogeneous datasets, graph database approaches may offer useful solutions for their knowledge management.BA, bronchial asthma; COPD, chronic obstructive pulmonary disease; DEG, differentially expressed genes; E-HTN, essential hypertension; GEO, Gene Expression Omnibus (database); GSM, samples from the GEO database; HPA, Human Protein Atlas; MiA, mild asthma; MMA, mild-moderate asthma; NC, normal control; RDF, the resource description framework; SA, severe asthma; TB, tuberculosisThe ENTREZ gene names and the protein names are given in Additional file


Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types —in conjunction with metabolite- and reaction-oriented data types . Third, Escher harnesses the strengths of web technologies  so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools. https://escher.github.io.We are now in the age of big data. More than ever before, biological discoveries require powerful and flexible tools for managing large datasets, including both visual and statistical tools. Pathway-based visualization is particularly powerful since it enables one to analyze complex datasets within the context of actual biological processes and to elucidate how each change in a cell effects related processes. To facilitate such approaches, we present Escher, a web application that can be used to rapidly build pathway maps. On Escher maps, diverse datasets related to genes, reactions, and metabolites can be quickly contextualized within metabolism and, increasingly, beyond metabolism. Escher is available now for free use (under the MIT license) at  PLOS Computational Biology Software Article.This is a The behavior of an organism emerges from the complex interactions between genes, proteins, reactions, and metabolites. With next-generation sequencing and various “omics” technologies, it is now possible to rapidly and comprehensively measure these components and interactions. These technologies have transformed the scientific process over the past decade. Data acquisition is substantially easier, but data analysis is increasingly becoming the primary bottleneck to discovery. To address the analysis bottleneck, there has been a demand for data visualization tools to complement statistical and modeling methods.pathway maps have long been a popular visual representation of metabolic pathways and other biological pathways. For each type of visualization, data can be associated with the biological components in the visualization. Visualizing data in this way contextualizes and enriches the dataset for scientists. Data-rich visualizations have been extremely valuable for viewing, interpreting, and communicating data.Biological visualizations often fall into categories characterized by biological scale, and the style of a visualization reflects the type of information at that scale. Three-dimensional objects are often used for representing protein structures , 2, one-A tool for visualizing pathway maps must satisfy a set of core features. The tool must (1) visually represent reactions and pathways clearly and in a way that is biochemically correct, (2) allow users to navigate and search through the visualization, (3) allow users to design and customize pathway maps, (4) allow users to represent diverse data types within the map using visual cues like size and color, (5) provide import and export features so that maps can be stored, shared, and exported to other tools, and (6) provide an application program interface (API) so the tool can be used within data analysis pipelines.The existing tools that satisfy these core features are all desktop applications. Briefly, these tools include Omix , CytoscaThe advantages of web applications include rapid deployment (no need to download an application or browser plug-in), greater cross-platform compatibility (e.g. mobile devices), flexible sharing, collaborating, and embedding features, as well as easy application development. Recently, a critical mass of performance enhancements and new libraries has made web tools comparable to desktop tools for many applications.A number of web-based tools exist for visualizing pathway maps: ArrayXPath , Pathwayany user can locally modify any element on the page at any time. If a web application is built on the DOM, then users can rapidly prototype new features and build extensions to the application while it is running. . This development platform includes a JavaScript shell for directly interacting with the web page runtime and a tool for inspecting and modifying every element in the web page document object model (DOM). Thus,  running . On the  running .Escher is a web application for visualizing pathway maps, and it is designed to be a fully featured pathway visualization tool that also harnesses all the advantages of the web. Escher has three key features that distinguish it from all existing pathway visualization tools, including the popular desktop applications. First, Escher makes building pathway maps fast and easy, using the information in datasets and genome-scale models to suggest pathways to the user—with this, pathway map design can be semi-automated. Second, Escher connects genes and enzymes to the reactions they catalyze, so that genomic data can be visualized in the context of the reaction network. We show how Escher can be used to visualize reaction data (metabolic fluxes), metabolite data (metabolomics), and genomic data (transcriptomic data), bridging the gap between these data types. Third, Escher uses the advantages of web technologies so that pathway maps can be adapted, extended, shared, and embedded. We illustrate the export and development features of Escher, including native support for scalable vector graphics (SVG) export, a downloadable tool for converting Escher maps to common standards for representing layouts, and application program interfaces (APIs) for developing new applications that extend the functionality of Escher.constraint-based reconstruction and analysis (COBRA) model, a collection of all the reactions, metabolites, and genes known to exist in an organism  or constraint-based model (CBM)) [To build a pathway map, one first needs a source for the names, stoichiometries, and associated genes for each biochemical reaction in an organism. This information is provided by a l (CBM)) . While Cl (CBM)) , so Eschl (CBM)) –25.Edit menu; these include tools for navigating the map (Pan mode), selecting and modifying elements (Select mode), adding reactions (Add reaction mode), rotating the current selection (Rotate mode), and adding and editing text annotations (Text mode).The Escher interface is centered around a canvas for the pathway map . In the Add reaction mode, a new pathway can be added to the canvas. Clicking on the canvas or an existing metabolite opens the new reaction search box. The search box can find reactions with a number of queries: reaction identifiers (IDs) and display names, metabolite IDs and display names, and gene IDs and names . The Settings page includes a detailed set of options for coloring and sizing elements based on statistical features of a dataset . Here, examples are provided for each data type, and the files required for recreating the visualizations are in the supplementary data.Three types of data can be visualized on an Escher map: reaction data, metabolite data, and gene data. And Escher supports visualizing a single dataset, or visualizing the comparison of two datasets using a number of comparison functions  [iJO1366 central metabolism was loaded  and the dataset (Data>Load reaction data function. (Datasets can be JavaScript Object Notation (JSON) or comma separated values (CSV) files, as described in the documentation.) Two settings were changed for this visualization: The absolute value of reaction data was visualized so that negative fluxes appear as large values, and the secondary nodes were hidden to simplify the visualization.To demonstrate the visualization of reaction fluxes, an s (pFBA) , 27. The dataset  was loadMap>Export as SVG, and the text labels of the high flux reactions were made larger for the figure.The resulting figure shows reaction fluxes for fermentation pathways . It was E. coli central metabolism , and fragments per kilobase of exon per million fragments mapped (FPKMs) were calculated using the Cufflinks functions cuffquant and cuffnorm [To demonstrate the use of gene data on an Escher map, transcript abundances for aerobic and anaerobic growth of lication . The datlication  ) [adds the values of two genes connected with an OR rule. When two enzymes are required together for catalysis—as in an enzyme complex—these are connected with an AND rule. Escher can take the mean or the minimum of the two values connected with an OR rule; this option is selected on the Settings page. For a comparison of two datasets, the gene reaction rules are evaluated for each dataset separately, then a comparison is made between the two resulting values ) . When eig values .2 of fold change between the conditions. However, Escher cannot yet display statistical significance for the datasets, so it should be paired with statistical tools .http://jquery.com) and Bootstrap (http://getbootstrap.com). The Escher JavaScript code can be compiled into a single JavaScript file, and a JavaScript API is available for interacting with and extending an Escher visualization .A Python package for Escher is also available , and thiEscher includes a database of pathway maps and genome-scale models. Pathway maps are currently available for a number of organisms, and new pathway maps will be continually added to the database from our group. The maps in the BiGG database are being converted to the new Escher format . We alsoescher/jsonschema/1-0-0), and the schema can be enforced using the JSON Schema validators available in a number of languages (http://json-schema.org). Thus, Escher maps conform to a well-defined specification that can be generated by other tools and scripts.Both Escher maps and COBRA models are stored as JavaScript Object Notation (JSON) files. JSON is a useful, plain-text format for storing nested data structures. For Escher maps, a JSON Schema has been defined  [Escher represents biochemical reactions as transformations from a set of reactants to a set of products, and each reaction can be assigned enzymes using a Boolean n (SBGN)  and Systn (SBGN) –37. Eschn (SBGN)  and libSn (SBGN) .https://github.com/zakandrewking/escher). Documentation for Escher is available and was generated using Sphinx and ReadTheDocs (https://escher.readthedocs.org). This documentation includes a description of the Escher features and detailed information on the JavaScript and Python APIs.Escher is hosted on GitHub, with a public bug tracker and tools for community contribution to the codebase (https://pypi.python.org), can be used to integrate Escher maps with data analysis and simulation workflows. Using the available functions, datasets can be applied to Escher maps, and the resulting maps can be saved as standalone web pages, saved as JSON or SVG, or exported using the EscherConverter as a command line utility. The Python package works directly with COBRA models using COBRApy [The Escher Python package, which is available from the Python Package Index # run the local server  are available for both JavaScript and Python to enable users to build, modify, and export maps programmatically. The specific functions in the APIs are defined in the Escher Documentation. New web applications can be built on top of the basic Escher functions by developing with the Escher JavaScript API. The Documentation provides details on implementing a very simple web page with an embedded Escher map.http://semver.org) so that application developers can rely on new versions of Escher to be backwards compatible.Escher version 1.1 is now available. Bug fixes and new pathway maps will be released regularly, and a number of Escher applications are currently in progress. Escher releases will follow the Semantic Versioning guidelines . Because Escher is an open-source project, contributions from the community—bug fixes, use cases, code contributions, etc.—will be encouraged and will be an important factor in making Escher a sustainable, long-term solution to the challenges of visualizing biological pathways.Project name: Escherhttps://escher.github.ioProject home page: https://github.com/zakandrewking/escherProject source: Open-source license: MIT licenseOperating systems(s): Platform independentProgramming languages: JavaScript, Python, and JavaOther requirements: noneAny restrictions to use by non-academics: no limitationsS1 Filehttps://github.com/zakandrewking/escher.This source code is for Escher version 1.1.2. The latest Escher source code can be cloned or downloaded from (ZIP)Click here for additional data file.S2 Filehttps://escher.readthedocs.org.This documentation is for Escher version 1.1.2. The latest Escher documentation can be found at (ZIP)Click here for additional data file.S3 Filehttps://escher.github.io.Requires Java™ version 1.8 or higher. The latest version of EscherConverter is available on the Escher homepage at (ZIP)Click here for additional data file.S1 Data(ZIP)Click here for additional data file.S2 Data(ZIP)Click here for additional data file.S3 Data(ZIP)Click here for additional data file.S4 Data(ZIP)Click here for additional data file.


S. cerevisiae. Four aspects of the methods’ performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost.Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in  Identifying the molecular pathways underlining vital cellular functions and pathological states is one of the fundamental problems in biomedicine. Gene regulatory pathways, governing the production of proteins through controlling gene expression, are among the most studied molecular pathways. Under physiological conditions, gene regulatory pathways react to the internal and external signaling of the cell, such that correct amounts of protein are produced when needed. Functional gene regulatory pathways are critical in promoting normal cell growth, differentiation and morphogenesis12345Two types of data can be used for causal network reconstruction: observational data and experimental data. Observational data is obtained from passively observing the changes of gene expression across time or the natural variation of gene expression among subjects or conditions. With the advancement in high-throughput assay technology over the past two decades, abundant observational datasets on gene expression are freely available in public repositories and new observational datasets are relatively cheap to collect891012active learning of casual networks has been developed recently1415161714To take full advantage of the cost-efficiency of the observational data while maximizing quality and completeness of causal network reconstruction, several methods for S. cerevisiae were explored by reconstructing the local causal pathways of transcription factors. The reconstruction performance of 54 active learning methods/variants from three families of algorithms was assessed by comparing to the experimentally derived gold-standard networks. The best methods for local causal pathway reconstruction for this dataset were identified. To the best of our knowledge, this is the first study to systematically examine the quality of local causal pathway reconstruction from real biological data using active learning methods.In a previous study we have systematically evaluated the local causal pathway discovery performance of various active learning methods on several simulated datasets of different characteristics. As an applied bioinformatics extension of our previous work, the present study evaluates the performance of various active learning methods when applied to discover local causal pathways from real biological data. The gene regulatory relations in In this section, we present the results of different active learning methods  for locaThe quality of adjacency discovery, i.e. the ability to correctly identify the members of the local causal pathway of a given transcription factor of interest, was evaluated using the sensitivity, specificity and distance (combined metric of sensitivity/specificity). Again, a discovered gene is considered a true positive if it is a member of the true local causal neighborhood, regardless of whether it is correctly identified as an upstream regulator or a downstream target of the transcription factor . Figure To evaluate the accuracy of edge orientation in the local causal neighborhood , the proportion of correctly oriented edges are calculated see . Two ODLThe experimental cost is measured by the percentage of genes manipulated in order to orient the local causal pathway with respect to total number of genes in the dataset see . The ODLThe quality of complete pathway discovery of the local causal pathway is affected by two factors: (1) the quality of adjacency discovery, and (2) the accuracy of edge orientation. Sensitivity, specificity, and distance (combined metric of sensitivity/specificity) of complete pathway discovery was used to evaluate the quality of complete pathway discovery . We remiThe quality of adjacency discovery, accuracy of edge orientation and experimental cost are the three key dimensions defining the performance of active learning algorithms. The three performance metrics are used to construct profiles for different active learning algorithms and are presented in a radar plot . Among aFirst, it demonstrated the feasibility of accurate reconstruction of local gene regulatory pathways from high-throughput observational data and limited number of experiments, using active learning methods. Second, it assessed the performance of various active learning methods on real biological data. Four performance metrics were evaluated: (i) adjacency discovery quality, (ii) edge orientation accuracy, (iii) complete pathway discovery quality, and (iv) experimental cost. The results of this study could serve as a guideline for the choice and further modification/improvement of existing active learning methods.The current study has two major contributions. Overall, we found that the active learning methods are suitable for local causal pathway discovery and they produce accurate local causal pathways with low experimental cost under the experimental conditions tested in this study. Tested active learning methods learn the skeleton of the causal network up to a Markov equivalence class and then select variables for manipulation aiming to distinguish the true causal relations. The two-phase procedure employed by active learning methods exploits the cost efficiency of observational data and the power of experimental data to accurately identify causal structures that are otherwise generally undistinguishable1617212223Among the active learning methods examined in this study, ODLP variants achieved the best local pathway reconstruction quality with low cost on the 5 transcription factors examined. In term of experimental cost, since ODLP methods are specifically designed for local pathway discovery, it is not surprising that its cost is much lower than the methods that are designed for global causal network discovery. In fact, after modifying ALCBN and HE-GENG methods for local discovery, drastic improvement in efficiency was achieved without compromising the discovery quality. However, despite similar efficiency to the other active learning methods, the ODLP algorithms also achieved superior pathway reconstruction quality. There are two potential reasons that ODLP outperforms other active learning methods in term of complete pathway discovery quality. First, during the adjacency discovery phase, the ODLP algorithms address potential multiplicity in the data . Expansion of this study to discover the dynamic interaction of gene transcription factors requires sufficient longitudinal observational data and experimental data collected under different biological conditions. Likewise, new active learning algorithms should be designed to better suit the discovery of interactions in dynamic networksThis work can be extended by evaluating the performance of the algorithms on more real-world datasets in biomedicine and other scientific disciplines. The current study is the first to evaluate the performance of the ODLP along with other state-of-the-art active learning algorithms. We would ideally like to compare the performance of the active learning algorithms over all transcription factors characterized in our gold standard regulatory network. However, we have only examined and reconstructed the local causal pathways for 5 out of 114 transcription factors due to resource limitations. In general, for individual ALCBN and HE-GENG variant, constructing the local causal pathway for one single transcription factor takes on average 30 days of a single CPU time. Reconstructing the local casual pathways of a single transcription factor using all 48 ALCBN or HE-GENG variants investigated in this study costs about 1,440 (30 × 48) days of single core CPU time. Running each additional multiple, randomly chosen subsets of 5 TFs will require 7,200  days single CPU time. We note that, although genearally in distribution free case one cannot base statistical inference on 5 data points, hypothesis testing is valid under distribution assumptions. Given that we observed a significant gap in performance between ODLP and other methods over all 5 TF’s studied, we believe that the similar performance patterns  will be observed on a larger dataset. We note that, although genearally in distribution free case one cannot base statistical inference on 5 data points, hypothesis testing is valid under distribution assumptions. Given that we observed a significant gap in performance between ODLP and other methods over all 5 TF’s studied, we believe that the similar performance patterns  will be observed on a larger dataset. Further, evaluating active learning methods on real-world data from different domains could provide more insight into the efficacy of these algorithms on data of different characteristics. Another interesting extension of the current work is studying the dynamic structure of the underlying gene regulatory network. In the current study, both the gold-standard network and the data used for The current study, as the first attempt to systematically evaluate the performance of various active learning algorithms for discovery of local causal pathways from real-world (non-simulated) data, demonstrates promising results. It is beneficial to extend this work to other domains  in order to gain insights of the behavior of these algorithms on datasets with different characteristics and facilitate further improvements of these methods.S. cerevisiae gene regulatory network, capturing the direct regulatory relations among 114 transcription factors and 5,395 genes. In our previous workThe gold-standard network reflecting direct gene regulatory interactions is constructed as described inlocal causal pathway discovery, 5 targets were selected randomly from the set of transcription factors (which play key role in the gene regulatory network) such that they represent local causal pathways of varying sizes. More details of these targets are given in Since the focus of this study is S. cerevisiae over time, resulting in 585 observations per gene . We choose this dataset as the observational data for regulatory network reconstruction since Rapamycin was demonstrated to induce widespread transcriptional changes in yeast. We used experimental data obtained from 1,484 gene deletion experiments conducted in a co-author’s lab (P.K)A previously published gene expression datasetWe evaluated 54 active learning methods/variants for local causal pathway discovery, including 24 variants of ALCBN18Originally, ALCBN and HE-GENG were designed to learn the entire network spanning all measured variables. In this study, we used the original ALCBN/HE-GENG algorithm to discover the entire network and evaluated their performance for the specific local causal pathways of interest. To improve the methods’ efficiency in local causal pathway discovery, we also modified ALCBN and HE-GENG algorithms to better suit local causal pathway discovery. Specifically, when selecting variables for manipulation to orient edges, instead of attempting to orient all edges in the entire network, the modified methods only orient edges among the target variable and its neighbors. The performance of these variants were evaluated and compared against the original methods and other methods. For all ALCBN and HE-GENG variants, PC algorithmUnlike the ALCBN and HE-GENG methods, ODLP was designed for local causal pathway discovery18To set up a baseline for the quality of adjacency discovery, 12 variants of univariate association methods were applied to the observational dataset. Variables that are considered to have statistically significant association with the target variable are considered as members of the local causal pathway and are output. The 12 variants (3 × 2 × 2) consists of the combination of 3 association tests , two alpha levels (0.05 or 0.01), and two methods for correcting for multiple statistical tests  was computed as the following: To evaluate the accuracy of edge orientation in the local causal neighborhood, i.e. whether the variables in the local causal neighborhood are correctly identified as direct causes or direct effects of the target variable, a proportion of correctly oriented edges were calculated with respect to the number of edges that are correctly identified. The proportion of correctly oriented edges ranges from 0 to 1, with 0 indicating the worst edge orientation accuracy and 1 indicating the best edge orientation accuracy.To evaluate the quality of complete pathway discovery, sensitivity and specificity of complete pathway discovery was computed. A discovered gene is considered a true positive if it is a member of the true local causal neighborhood, and the edge between this gene and the target transcription factor is oriented correctly. Therefore, the sensitivity and specificity capture the quality of both adjacency discovery and edge orientation accuracy, i.e. quality of complete pathway discovery. Sensitivity and specificity of complete pathway discovery were also combined into a single metric, the Euclidean distance from the optimal algorithm with (with sensitivity of one and specificity of one): The experimental cost, i.e. the ratio of the number of experiments conducted for edge orientation over the total number of genes in the dataset was calculated and reported as percentage. This metric ranges from 0 to 100%, with 0 indicating no experimental cost (no variables/genes manipulated) and 100% indicating maximum experimental cost . Since experimentation (or edge orientation) is the most costly step in local causal pathway reconstruction, we consider the experimental cost as a good approximation to the overall cost of local causal pathway discovery.All the above metrics were calculated for individual local pathways and algorithms, and then averaged across five local pathways yielding one set of metrics per algorithm see , 3, 4.How to cite this article: Ma, S. et al. An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation. Sci. Rep.6, 22558; doi: 10.1038/srep22558 (2016).


Muscle invasive bladder carcinoma is a complex, multifactorial disease caused by disruptions and alterations of several molecular pathways that result in heterogeneous phenotypes and variable disease outcome. Combining this disparate knowledge may offer insights for deciphering relevant molecular processes regarding targeted therapeutic approaches guided by molecular signatures allowing improved phenotype profiling. The aim of the study is to characterize muscle invasive bladder carcinoma on a molecular level by incorporating scientific literature screening and signatures from omics profiling. Public domain omics signatures together with molecular features associated with muscle invasive bladder cancer were derived from literature mining to provide 286 unique protein-coding genes. These were integrated in a protein-interaction network to obtain a molecular functional map of the phenotype. This feature map educated on three novel disease-associated pathways with plausible involvement in bladder cancer, namely Regulation of actin cytoskeleton, Neurotrophin signalling pathway and Endocytosis. Systematic integration approaches allow to study the molecular context of individual features reported as associated with a clinical phenotype and could potentially help to improve the molecular mechanistic description of the disorder. Bladder cancer (BC) presents with an estimate of 72,570 new cases diagnosed and 15,210 deaths across the United States . Though Cystoscopy is the gold standard with a reported sensitivity and specificity in the range of 62–84% and 43–98%, respectively . TreatmeThe comparison of the genetic characteristics of muscle-invasive and non-invasive tumours revealed that non-invasive tumours over-express HRAS and FGFR3 or produce highly activated forms of these proteins. As a result, the Ras/MAPK pathways are up-regulated in non-invasive tumours . Muscle-http://cancergenome.nih.gov/), Oncomine [High-throughput experimental platform technologies ranging from genomic sequencing to proteomic and metabolomic profiling are now being used for molecular characterization of clinical phenotypes –19. A vaOncomine , and OMIOncomine . Though Oncomine –25. DespOncomine .http://www.pathguide.org/), all displaying well-defined human molecular metabolic and signalling pathways together with disease-specific pathways (e.g. pathways in cancer). Molecular features being identified as associated with bladder cancer can be interpreted on the level of such pathways, adding to a functional interpretation of molecular feature sets characterizing the phenotype.On the effector level, proteins interact and co-operatively form specific molecular processes and pathways. Intermolecular interactions include various types being represented as networks (graphs) with molecular features denoted as nodes (vertices) together with their interactions (edges). A large number of biological pathway resources has become available, including KEGG , PANTHERTo add to our understanding of muscle-invasive bladder carcinoma (MIBC), we derived a phenotype-specific network model (interactome) by integrating omics signatures characterizing MIBC, reported in scientific literature and databases. Our procedure incorporated scientific literature screening and signatures from omics profiling, resulting in 1,054 protein-coding genes being associated with MIBC, further consolidating to 286 genes on the interactome level. The results display deriving a systems-level model for molecular phenotyping of bladder cancer muscle invasion, presented as multiple affected pathways.ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2pubmed.gz). Profiling experiments were further screened for adequacy in sample size (at least 50 samples included in study design), magnitude of differential abundance (>2-fold change) and the specific phenotypic conditions; T1, T2a/b, T3a/b, T4a/b   and ArraProtein interaction information was obtained by querying IntAct , BioGRIDCytoscape’s plug-ins ClueGO and CluePedia was used to identify pathways that are being over-represented in the set of features located in the induced subgraph , 36. KEGFrom the set of MIBC-associated protein-coding genes, each gene symbol was evaluated for being a member of the MIBC pathway set. The evidence of identified pathways and extracted genes involved in MIBC was assessed based on the level of annotation depth, defined as the number of individual studies identifying such protein-coding genes as involved in MIBC. Specifically, such evidence was derived from metadata available in PubMed. Gene-2-pubmed was used for linking the molecules contained in the induced subgraph to publications relevant to bladder cancer muscle invasion. The quality of publications obtained for each molecule was assessed based on manual reviewing. Only papers where a direct link of the molecule to bladder cancer muscle invasion was proven were retained. For the entire pathway set, the ratio between the number of molecules being linked to at least one urinary bladder neoplasm publication and the number of features in the pathway was computed and used for relevance ranking. For individual protein-coding genes identified in literature the number of linked urinary bladder neoplasm publications was used as relevance ranking criterion.Mining of published articles and omics repositories led to a collection of 285 references after manual screening . This scThe largest number of features associated with MIBC resulted from transcriptomics with a total of 716 gene symbols. Metabolites were mapped to protein coding genes using the Human Metabolome Database (HMDB)  and provWith respect to the feature set overlap, 52 gene symbols were identified in both, literature mining and individual omics signatures . Of the To increase evidence in regard to the association of molecular features with MIBC we included protein-interaction information as a filter mechanism, i.e. combining evidence from statistical analysis with biological (interaction) data. For this analysis, the molecular feature set was reduced from 1,054 unique protein coding genes to 592 gene symbols. The reduction in the protein list was mainly caused because the protein coding genes indirectly linked from metabolomics and miRNA profiling were not incorporated in the pathway analysis due to low evidence linking to respective targets and enzymes. Further disregarding features not showing interactions to other members of the MIBC set resulted in 286 protein-coding genes represented on the muscle invasive bladder cancer-specific subgraph. The list of the initial 1,054 proteins, the 592 proteins disregarding metabolomics data and miRNA screens, and the list of 286 proteins that form protein-protein interactions in the MIBC subgraph is available in KEGG pathway enrichment analysis of the MIBC molecular feature set represented on the induced subgraph utilizing ClueGO and CluePedia resulted in 15 molecular pathways being significantly affected in the context of muscle invasive bladder carcinoma . In detaThe network in We performed an additional pathway enrichment analysis that involved an alternative set of gene symbols. From the full set of protein coding genes (707 molecules excluding metabolite and miRNA targetted gene symbols), we restricted to members being present in more than one study type . This restriction resulted in 72 gene symbols, again forwarded to pathway enrichment analysis. Understanding the molecular pathophysiology of muscle-invasive bladder carcinoma and revealing the network of pathways involved in muscle invasion could lead to targeted therapy. In addition, addressing specific dys-regulated pathways linked to progressive disease holds the promise of supporting an improved, biomarker-based risk assessment followed by stratified clinical intervention . High thHigh-throughput DNA sequencing can yield erroneous data . MS basehttp://www.pathguide.org/). Albeit there are several well curated and reliable pathway database resources as also described by our group [Biological pathway databases play an essential role in annotating protein-coding genes resulting from high-throughput profiling approaches. There are approximately 547 pathway database resources available as listed in PathGuide  family of receptors and down regulate tumour necrosis factor superfamily (p75NTR) through which PI3K/Akt, Ras/Raf/MAP kinase, NF-kappa B and Jun kinase signalling pathways are triggered. Trk-receptors with neurotrophin ligands have been identified as initiating tumour progression, and the signalling pathway neurotrophins-Trk has been reported as a target for therapeutic intervention in hormone-refractory prostate cancer (HRPC) and in human astrocytomas, and potentially could play a role in urothelial carcinoma –58. EndoThe bioinformatics approach reported here involved integrating available public domain data sets in context of bladder muscle-invasive carcinoma on an interaction network, and further mapping them to biological pathway sources to reveal 15 pathways as being affected in progressive disease. Eleven from these pathways were discussed previously in the context of MIBC. It should be taken into account that while using such computational techniques to integrate molecular signatures from varying resources, certain technical issues regarding the use of appropriate global identifier need to be considered. In our approach, we discarded metabolite and micro-RNA targets for the pathway enrichment analysis , resulting in 592 features from the total of 1,054 protein coding genes. This is mainly driven by hampered translation of metabolite and microRNA profiles to the level of involved protein coding genes, be it on the target or enzyme level. In regard to genomics and epigenetics, we only incorporated those gene symbols that contained epigenetic information on the protein/mRNA abundance levels for the interactome analysis.On the other hand, the two pathway terms GnHR receptor and T cell receptor signalling pathways found as enriched on the basis of the 72 gene symbols being multiply identified in, were not retrieved from the analysis resting on the full set of 286 features being derived from the induced subgraph. Data evidence and selection biases clearly affect results of such integrated analysis demanding strict quality control of input data sets as followed in our study.Apparently, each individual functional context highlights specific aspects of bladder cancer pathophysiology, but only providing limited characterization of clinical outcome on the cohort level.In summary, automated data retrieval from the literature resulted in a first collection of molecular features associated with MIBC, and, complementing with omics profiling data, allowed augmenting a mechanistic (pathway) map linked to MIBC. From the cross-sectional nature of the underlying molecular feature collection no direct conclusion can be drawn regarding the prognostic relevance of individual pathways. However, deriving bladder cancer-associated protein coding genes on the basis of such pathway maps provides a systematic foundation for experimental analysis regarding association with development of muscle-invasive disease. We are confident that this approach can form the basis to rational selection of biomarkers for enabling targeted analysis of potentially relevant key molecules.Our results suggest that there is a complex interplay between interacting pathways that characterizes the muscle invasive phenotype of invasive bladder cancer. We developed an integrated molecular model of muscle invasive bladder cancer to allow selecting protein-coding genes on the pathway level aimed at capturing a set of pathways of potential relevance in tumour progression. Further experimental validation of Neurotrophin signalling pathway, Regulation of actin cytoskeleton and Endocytosis with respect to disease progression and treatment response in muscle-invasive bladder carcinoma is indicated.S1 TableSheet one lists protein coding genes retrieved from DNA mutation, methylation, transcriptomics, metabolomics and literature screening. Redundant entries were ranked based on the frequency of occurence. Combing all protein coding genes resulted in 1,054 unique protein coding genes.(XLS)Click here for additional data file.S2 TableProvided are gene symbols pertaining to the muscle-invasive phenotype (T2–T4) of bladder carcinoma identified in scientific literature, together with expression levels and PubMed identifiers.(XLS)Click here for additional data file.S3 TableProvided are KEGG pathways together with the number of MIBC-associated features, overlapping literature-mined gene symbols and Bonferonni corrected p-values. The supporting table is divided into two sheets namely Novel pathways and Literature-known pathways.(XLS)Click here for additional data file.S4 TableProvided are gene symbols pertaining to the muscle-invasive phenotype (T2–T4) of bladder carcinoma identified from methylation studies, together with expression and methylation levels in addition to their PubMed identifiers.(XLSX)Click here for additional data file.S5 TableProvided are gene symbols pertaining to the muscle-invasive phenotype (T2–T4) of bladder carcinoma resulting from transcriptomics studies (inculsion criteria of >50 molecules in the study), together with expression levels and PubMed identifiers. Protein coding genes that only hold differential expression information are provided with links from available studies.(XLS)Click here for additional data file.S6 TableProvided are miRNA targets pertaining to the muscle-invasive phenotype (T2–T4) of bladder carcinoma from transcriptomics studies (inculsion criteria of >50 molecules in the study), together with expression levels and PubMed identifiers.(XLS)Click here for additional data file.S7 TableProvided are underlying enzymes for metabolites pertaining to the muscle-invasive phenotype (T2–T4) of bladder carcinoma from metabolomics studies (inculsion criteria of >50 molecules in the study), together with expression levels and PubMed identifiers. Protein coding genes that only hold differential expression information are provided with links to the data retrieved from available studies.(XLS)Click here for additional data file.S8 TableProvided are protein coding genes with significant levels of mutations pertaining to muscle-invasive phenotype (T2–T4) of bladder carcinoma and PubMed identifiers.(XLS)Click here for additional data file.S9 TableProvided are the MIBC-asociated gene symbols in three different columns; a. 1,054 unique gene symbols from initial consolidation, b. 592 gene symbols excluding enzymes from metabolite signatures as well as miRNA targets c. 286 gene symbols that formed the induced subgraph, and d. interaction information among the 286 gene symbols.(XLS)Click here for additional data file.S10 Table7 pathway terms were enriched. The set of protein coding genes included is listed in sheet 2. Sheet 3 and 4 list known and novel pathways together with the number of protein coding genes assigned and Bonferroni corrected p-values. Sheet 5 and 6 list the overlapping gene symbols for all identified pathways.(XLS)Click here for additional data file.


MicroRNAs (miRNAs) are about 22 nucleotides, non-coding RNAs that affect various cellular functions, and play a regulatory role in different organisms including human. Until now, more than 2500 mature miRNAs in human have been discovered and registered, but still lack of information or algorithms to reveal the relations among miRNAs, environmental chemicals and human health. Chemicals in environment affect our health and daily life, and some of them can lead to diseases by inferring biological pathways.We develop a creditable online web server, ChemiRs, for predicting interactions and relations among miRNAs, chemicals and pathways. The database not only compares gene lists affected by chemicals and miRNAs, but also incorporates curated pathways to identify possible interactions.http://omics.biol.ntnu.edu.tw/ChemiRs.Here, we manually retrieved associations of miRNAs and chemicals from biomedical literature. We developed an online system, ChemiRs, which contains miRNAs, diseases, Medical Subject Heading (MeSH) terms, chemicals, genes, pathways and PubMed IDs. We connected each miRNA to miRBase, and every current gene symbol to HUGO Gene Nomenclature Committee (HGNC) for genome annotation. Human pathway information is also provided from KEGG and REACTOME databases. Information about Gene Ontology (GO) is queried from GO Online SQL Environment (GOOSE). With a user-friendly interface, the web application is easy to use. Multiple query results can be easily integrated and exported as report documents in PDF format. Association analysis of miRNAs and chemicals can help us understand the pathogenesis of chemical components. ChemiRs is freely available for public use at  In recent years, a number of studies have reported that the dysfunctions on microRNA (miRNAs), environmental factors or their interactions have strong effects on phenotypes and even may result in abnormal phenotypes and diseases . Environibl.org) . Then, oibl.org) , 12, miRibl.org) , miRDB [ibl.org) , RNAhybribl.org) , PicTar ibl.org) , PITA [1ibl.org) , RNA22 [ibl.org) , TargetSibl.org) , miRWalkibl.org) , miRecoribl.org) , miR2Disibl.org) , and miRibl.org) . A set oibl.org)  servers ibl.org) , REACTOMibl.org) , and Genibl.org)  databaseThe workflow of ChemiRs server is illustrated in Fig. p-value.To access ChemiRs web server, a user has to choose a search function from main menu for one or more searches as query processing. In the ‘Search by miRNA’ module, the user directly selects a miRNA of interest from a dropdown list of human miRNAs. For the other search modules , the user can submit a query keyword of interest to search for related topics. A graphical control checkbox permits the user to make multiple choices of both the search databases and topics of interest. Detailed descriptions of the inputs are given by scrollable tabboxes, checkboxes, radio buttons or type text. Then, the ChemiRs server processes the user query, generates the intersection of search results, and calculates the statistical significance level with The search results of target genes and related associations with chemicals, diseases, pathways and GO terms are shown in the ChemiRs server. The output results are presented to the user via both an interactive viewer and downloadable files.Query results are shown in a tabbox and automatically made scrollable when the sum of their width exceeds the container width size. The listbox component can automatically generate checkboxes or radio buttons for selecting list items by user selected attributes. Checkboxes allow multiple selections to be made, unlike the radio buttons. It is easy to obtain results immediately with sorting functionalities built in the grid and listbox components.The results can also be downloaded as comma-separated value (CSV) files, which can be easily imported into Microsoft Excel. The CSV files include all features calculated by ChemiRs. In addition, a related reference represented by the Pubmed ID is also provided. Multiple query results can also be easily integrated and exported as report documents in PDF format.Schema of the client-server architecture of ChemiRs is shown in Fig. The data statistics of ChemiRs are described in Table The aim of ChemiRs web server is to provide integrated and comprehensive miRNA target prediction analysis via flexible search functions, including search by miRNAs, gene lists, chemicals, genes, diseases and pathways. Next, case study examples by six different search methods are described in the following sections.p-value). The p-value represents the probability of a random intersection of two different gene sets, and the p-value calculations are based on hypergeometric distribution. The probability to randomly obtain an intersection of certain size between user’s set and a network/pathway follows hypergeometric distribution. The lower the p-value, the higher is the non-randomness of finding such intersection. By taking log of p-value, the higher the -log, the higher is the non-randomness. Generally, when p-value is considered as 0.05, the -log greater than 2.995 denotes statistically significant. As shown in Fig. As an example, we applied ChemiRs to analyze the hsa-let-7a-5p miRNA. We selected the miRNA ‘hsa-let-7a-5p’ in ‘Search by miRNA’ module and chose ‘pictar(5way),’ ‘PITA,’ ‘RNA22,’ and ‘TargetScan’ as miRNA target prediction methods; ‘4 minimum predicted methods’ as restrictions; and ‘Targets,’ ‘Chemicals,’ ‘Diseases,’ ‘Pathways,’ and ‘GO terms’ as the output functions, respectively. This example can be referred by clicking ‘Tip#2 logical analysis’ on the start page of ChemiRs. As shown in Fig. We applied ChemiRs to analyze a gene list data reported by Naciff et al. , in whicp-value)). Interestingly, we found that these chemicals have already been well-known to be associated with estrogens or Endocrine Disrupting Chemicals (EDCs). In fact, many industrially made estrogenic compounds and other EDCs are potential risk factors of cancer. Moreover, estrogen and progesterone receptor status have already been reported to be associated with breast cancer [We analyzed the top ten related chemicals returned by ChemiRs, which were sorted according to their significance of activity changes  by submitting ‘DEHP’ in ‘Search by chemical’ module. After pressing the ‘Refresh’ button, we clicked the Medical Subject Heading (MeSH) ID ‘D004051, Diethylhexyl Phthalate’ and chose ‘None’ as the filter; ‘miRNAs,’ ‘Genes,’ ‘Diseases,’ ‘Pathways,’ and ‘GO terms’ as the output functions; all ten methods as miRNA target prediction methods, and ‘10 minimum predicted methods’ as restrictions, respectively. As shown in Fig. p-value)). The 93 related human genes and their associated references are listed in Table We checked ‘Candidate miRNAs,’ the top ten ‘related genes,’ ‘related diseases,’ ‘related pathways,’ and ‘related GO terms’ returned by ChemiRs, which were sorted according to their significance of activity changes , ‘Tree Number’ , ‘Scope Note’ , and ‘MeSH Tree Structures’  will be returned by ChemiRs.We applied ChemiRs to analyze a cell cycle pathway using ‘Search by pathway’ module. We entered ‘cell cycle’ and pressed the ‘Refresh’ button, then five relevant pathways are listed. After we pressed the light blue line ‘KEGG: 04110 Cell cycle,’ all the hsa04110 pathway information will be returned.In the future, we will continuously develop and enhance the interactive analysis module and adjust the web service for better user-experience. An automatic update will also be carried out monthly to keep pace with the latest database versions. It is also planned to incorporate more applications for gene expression data and allow users to customize their own visualization.The ChemiRs web server integrates and compares ten miRNA target prediction methods of interest. The server provides comprehensive features to facilitate both experimental and computational target predictions. In addition, ChemiRs incorporates flexible search modules including (i) search by miRNA, (ii) search by gene, (iii) search by gene list, (iv) search by chemical, (v) search by disease and (vi) search by pathway. Moreover, ChemiRs can make predictions for Homo sapiens miRNAs of interest, and also allow fast search of query results for multiple miRNA selection and logical restriction, which can be easily integrated and exported as report documents in PDF format. The service is unique in that it integrates a large number of miRNA target prediction methods, experiment results, genes, chemicals, diseases and GO terms with instant and visualization functionalities.http://omics.biol.ntnu.edu.twHome page: http://omics.biol.ntnu.edu.tw: WelcomeTip: http://omics.biol.ntnu.edu.tw: VideoDemo: http://omics.biol.ntnu.edu.tw: HelpTutorial: Operating system(s): Both portal and clients are platform independent.Programming language: JAVA, JavaScriptAny restrictions to use by non-academics: None


Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F One of the central research goals of systems biology is modelling various biological processes. Elucidation of chemical reactions and pathways is one of the challenging problems in this field. A biological pathway is formed by a series of chemical reactions catalyzed by enzymes within a cell. Some of the most common biological pathways are those associated with metabolism, regulation of gene expression and transmission of molecular signals. A metabolic pathway involves the step-by-step modification of an initial molecule to form another product. The resulting product can be stored by the cell, secreted, used immediately, or used to initiate another metabolic pathway. An example of a metabolic pathway is the cellular respiration equation where glucose is oxidized by oxygen to produce ATP, adenosine triphosphate . PathwayThe widening gap between the amount of known proteins and knowledge of their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. Hence, the need for automated methods is more than urgent to help increase the annotation coverage, detect inconsistencies and provide seeds for manual curation. There are several approaches proposed in the literature for such a task. A quite promising approach is to apply knowledge discovery and data mining techniques to predict some protein features based on a set of known data. Such rule-based methods provide rich automatic functional annotations and aid in performing integrity checks. For instance, Kretschmann et al  applied Literature-based manual annotation of pathways cannot scale to timely process thousands of recently sequenced genomes. Therefore, computational methods are needed for the identification and mapping of pathways. We suggested that association rule mining could be used effectively as a computational method for pathway prediction. Association rule mining is a technique originating from the analysis of data on market baskets. The objective is to locate trends by means of association relationships and correlations within a dataset. Essentially, the aim of such analysis is to discover a set of useful rules that are shared among a percentage of the dataset.X ⇒ Y where X and Y are disjoint itemsets. Association rule mining was used in several applications of bioinformatics including mining gene expression data [An association rule is an implication expression of the form ion data  and idenion data . Moreoveion data . In the ion data .One of the very first pathway prediction systems was PathFinder  which aihttp://www.ebi.ac.uk/~rsaidi/arba/software.In this work, we are tackling the problem of pathway prediction in the context of metabolism. We introduce a pathway prediction system that can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The pathway prediction system utilizes data from UniProtKB/Swiss-Prot , which iThe system is designed to solve the following problem: given a set of UniProtKB/Swiss-Prot entries, generate models for pathway prediction using rule mining techniques. As with any machine learning system, the system has two major phases, the learning phase, and the application phase. The learning phase involves the training step and the testing, or validation, step on UniProtKB/Swiss-Prot input data, whereas the application phase involves applying the generated pathway models on the respective UniProtKB/TrEMBL entries. It is important to note that UniProtKB/Swiss-Prot and UniProtKB/TrEMBL sets of entries are mutually exclusive and do not overlap and therefore are selected for training and application purposes respectively. It is also worth mentioning that the system evaluation and validation is essential for the development of a reliable machine learning system. We used cross-validation as a means for system evaluation which is described later in this section.The current status in UniProtKB for prokaryotes is summarized in Target: Metabolic Pathway CommentAnthranilate synthase component 1 is:Amino-acid biosynthesis; L-tryptophan biosynthesis; L-tryptophan from chorismate: step 1/5.Represented as a structured hierarchy of controlled vocabulary where each process is split up into super-pathway, pathway and/or sub-pathway. When known, the step number mediated by the protein within the pathway is also indicated. On the other hand, when the metabolic pathway is not fully known, only the super-pathway and pathway labels are indicated. Moreover, a protein can participate in different pathways or in different steps of the same pathway. An example of a fully known pathway representation in UniProtKB for the protein Attribute: InterPro Signature IDAnthranilate synthase component 1 is: IPR005801 (a domain), IPR019999 (a family), IPR006805 (a domain), IPR005256 (a family), and IPR015890 (a domain)The InterPro signature IDs are cross-referenced from the InterPro database, which is an integrated resource of protein families, domains and functional sites. InterPro provides a functional analysis of proteins by classifying them into families and domains. Protein signatures are combined from 11 member databases into a single searchable resource. A protein entry could be associated with one or more InterPro IDs. An example of InterPro IDs associated with the protein Attribute: Taxonomic LineageAnthranilate synthase component 1 is: Bacteria; Proteobacteria; Gammaproteobacteria; Pseudomonadales; Pseudomonadaceae; Pseudomonas.The taxonomic lineage is considered as an attribute. UniProtKB Taxonomy is based on the NCBI taxonomy database and is organized in a tree structure that represents the taxonomic lineage. It contains the taxonomic hierarchical classification lineage of the source organism. It lists the nodes as they appear top-down in the taxonomic tree, with the more general grouping listed first. An example of taxonomic lineage representation for protein The system extracts necessary information from the loaded UniProtKB/Swiss-Prot entries using metabolic pathways as targets, and InterPro signatures and organism taxonomic lineages as attributes. We ended up with a total of 96,280 entries. The attributes and target representation in UniProtKB are described as follows:The extracted list of attributes and targets for each loaded entry will form an itemset. SupportR = A AND B ⇒ C (noted supp(R)) is the support of the set S = A, B, C which is defined by the absolute or relative number of cases in which the rule is correct. In the previous example, it is the number of cases where the occurrence of item C follows from the occurrences of items A and B. However, this definition may cause some problems if multiple evaluation measures are used [According to , the supare used . Hence, are used , 31, 32 are used .ConfidenceR = X ⇒ Y (noted conf(R)), where X, and Y are itemsets, is calculated as the support of the set of all items that appear in the rule, divided by the support of the antecedent set. More formally,The confidence metric is used to measure the quality of a particular association rule. More intuitively, it measures the reliability of the inference made by a rule. Introduced in , the conplicable . A high Lift ValueR = X ⇒ Y is:supp(∅), is the number of transactions in the database. Lift measures how far from independence the antecedent and consequent are. A lift value equals to one, implies that the antecedent and the consequent are independent and that the support of a rule is expected, considering the supports of its components which renders such a rule not uninteresting. If the resulting lift value is greater than one, this implies that the presence of the antecedent items raises the confidence. Likewise, if the lift value is less than one, then the presence of the antecedent items lowers the confidence.The lift value, or confidence quotient is basically the quotient of the posterior and the prior confidence of an association rule . Mathemap-Valueχ2-distribution. The chi-squared statistic can be used to calculate a p-value by comparing the value of the statistic to a χ2-distribution. That is, the p-value is computed as the probability that the χ2-value of an association rule can be observed by chance, assuming that the antecedent and the consequent of the rule are independent [In statistics, the p-value is used to measure the statistical significance of a result. Several statistical tests have been used to calculate the p-values of association rules , 34. Herependent . The p-vThe prepared itemsets form the input of Apriori algorithm proposed by Agarwal and Srikant . AprioriGiven our selected dataset and parameters, Apriori successfully generated 568,006 rules in total. Some examples of rule representation along with it quality metrics are shown in Apriori generates a large number of rules especially for large databases . The expert is unable to determine the most interesting association rules and therefore make decisions based on these rules. Hence, we need an efficient evaluation of the rules to select those that are actually relevant.x = (A ⇒ B) is said to be comparable to a rule x′ = (C ⇒ D) if B = D AND A ∩ C ≠ ∅. Comparability defines a kind of semantic relationship between the rules and restricts the use of the other notion i.e., dominance. Specifically, the dominance between two rules must be applied only if a semantic relationship exists between them . The dominance relationship, which is the corner stone of the SkyRule operator can be presented as follows: a rule r is said to be dominated by another rule r′, if for all used measures, r′ scores better than r, and therefore is more relevant.The generated list of rules will be analyzed by the SkyRule software , 27 to s1.0 for all four quality metrics we have). After that, SkyRule sorts the set of rules in a descending order by their associated distances. The first representative rule to be selected by SkyRule will be the rule which has metrics closest to the normalized ideal metrics. This rule has proven to be undominated by any other rule of the set of candidate rules [At first, for each rule, SkyRule computes the Euclidean distance to the normalized ideal metrics  means that the pathway, “One-carbon metabolism; methanogenesis from acetate” has been predicted for the protein of ID “Q8TRZ4”.In our case, a prediction is defined as an association between a protein and a pathway annotation i.e., according to the system, the protein is not involved in the pathway under consideration. Furthermore, if a prediction is generated for a protein with known pathway annotations, then this prediction can be qualified as true if it is correct or false otherwise. So, a prediction can be a true positive (TP), true negative (TN), false positive (FP) or false negative (FN). For example, if a protein has an annotation with two pathways and the system predicted only one of the two pathways, then we will count one true positive and one false negative. A more general example is: if we have x number of pathways in the reference set of pathways, and y proteins where each protein is annotated with a unique pathway from the set of x, assuming we predicted them all correctly, then we will have y TP and y(x − 1) TN.This association is created when a prediction model, generated by our system, is applied on a protein. When the protein satisfies the model conditions, the generated prediction is called a positive prediction or simply a prediction. Otherwise, it is called a negative prediction For this experiment, we used the set of UniProtKB/Swiss-Prot prokaryotic entries containing pathway annotations with manual assertion evidences . We define as a pathway reference set the set of pathways present in at least 20 protein entries.In order to evaluate the robustness of our system, we used a five-fold cross-validation procedure with two runs (or rounds). In each round, data is split into five complementary folds and five iterations of learning are performed. At each iteration, both rule mining and model building are performed using four folds independently of the validation set (remaining fold). The five obtained validation results are then averaged over the number of folds to summarize the performance for one run of the evaluation process. We perform a second round of five-fold-cross-validation with a different splitting in order to avoid any bias that would result from the splitting process. The global validation result is then obtained by averaging the results over the two runs. 1-measure and area under the ROC curve (AUC), defined as:PrecisionRecall1-measureFAUC = Area under the ROC curve. A receiver operating characteristic (ROC) curve is a plot of the true positive rate as a function of the false positive rate and is often used to illustrate the performance of a binary score-based classifier. The area under the ROC curve is a summary measure that essentially averages diagnostic accuracy across the spectrum of threshold values.We consider four different evaluation metrics, precision, recall, F1-measure of 0.982, a precision of 0.999, a recall of 0.966 and an AUC of 0.987.reference proteome set of prokaryotic entries of UniProtKB/TrEMBL for the purpose of annotation using our prediction models. Reference proteomes are a subset of proteomes that have been selected either manually or algorithmically according to a number of criteria, to provide a broad coverage of the tree of life and a representative cross-section of the taxonomic diversity found within UniProtKB. It also covers the proteomes of well-studied model organisms and other species of interest for biomedical research [http://www.ebi.ac.uk/~rsaidi/arba/prokaryotapathway/organisms/comparison contains predictions applied on some popular prokaryotic organisms present in UniProtKB/TrEMBL along with a graphical report, illustrating our system’s predictions compared to those made by other systems present in UniProtKB/TrEMBL.In order to capture the performance of our system, we considered the research . These rThe system achieved a very high coverage of prokaryotic pathway prediction. That is, considering the parameters defined earlier in Here, we provide a comparison of the annotation coverage of our system over UniProtKB/TrEMBL with reference to all other automatic annotation systems run on UniProtKB/TrEMBL such as Rule-base  and HAMAIn In In order to better quantify the proportion of identical or similar predictions shared between our system and the other three main automatic annotation systems, Rule-base, HAMAP-Rule and SAAS, we compare the predictions that correspond to entries annotated by our system and the three other systems.