Bioinformatics and biodegradation are two primary scientific fields in applied microbiology and biotechnology. The present review describes development of various bioinformatics tools that may be applied in the field of biodegradation. Several databases, including the University of Minnesota Biocatalysis/Biodegradation database (UM-BBD), a database of biodegradative oxygenases (OxDBase), Biodegradation Network-Molecular Biology Database (Bionemo) MetaCyc, and BioCyc have been developed to enable access to information related to biochemistry and genetics of microbial degradation. In addition, several bioinformatics tools for predicting toxicity and biodegradation of chemicals have been developed. Furthermore, the whole genomes of several potential degrading bacteria have been sequenced and annotated using bioinformatics tools. Millions of toxic chemicals have been produced for use in a variety of industries . These cBiodegradation is the break-down of chemicals or xenobiotic compounds by microbes and plants . BiodegrMicrobial remediation is an emerging technology for the removal of toxic chemicals from the environment -6. A larBioinformatics, which has been incorporated into each branch of life sciences, provides a platform for researchers to develop valuable computational tools for human and environmental welfare ,10. In tIn recent years, an increasing number of databases have been developed to provide information regarding chemicals and their biodegradation. These databases may be characterized into two categories: chemical databases and biodegradative databases. Table Biodegradative databases store information related to biodegradation of chemicals including xenobiotics-degrading bacteria, metabolic degradation pathways of toxic chemicals, enzymes and genes involved in the biodegradation. These databases include the University of Minnesota Biocatalysis/Biodegradation database (UM-BBD), a database of biodegradative oxygenases (OxDBase), Biodegradation Network-Molecular Biology database (Bionemo), MetaCyc, and BioCyc.http://umbbd.ethz.ch/. This database provides information pertaining to multiple fields of interest including microbes, biotransformation rules, enzymes, genes and reactions involved in microbial degradation [The UM-BBD is a well-known database in the field of biodegradation that is freely available at radation . This daradation .http://www.imtech.res.in/raghava/oxdbase/), which was developed by the CSIR-Institute of Microbial Technology, Chandigarh, India, stores information regarding oxygenases derived from published literature and databases [http://www.genome.jp/kegg/), UM-BBD, BRENDA, and ENZYME. This database is user-friendly and increases our understanding of aerobic degradation of aromatic compounds [Another database, OxDBase (atabases . Oxygenaatabases . There aatabases . Dioxygeatabases . ARCDs aatabases . OxDBaseompounds .http://bionemo.bioinfo.cnio.es) was developed by the structural Computational Biology Group at the Spanish National Cancer Research Center [The Bionemo database  provide reference data for computational prediction of the metabolic pathways of organisms from their sequenced genomes, (ii) support metabolic engineering, (iii) facilitate comparison of biochemical networks, and (iv) serve as an encyclopedia of metabolism [MetaCyc is a database of metabolic pathways derived from the scientific experimental literature that comprises more than 2097 experimentally determined metabolic pathways from more than 2460 different organisms. This is the largest curated database of metabolic pathways of all domains of life . This databolism . This dahttp://biocyc.org/) is a collection of more than 2988 organism-specific Pathway/Genome Databases (PGDBs). Each PGDB contains the full genome and predicted metabolic pathway of a single organism [BioCyc  is a part of UM-BBD that may be accessed at ompounds . Predictompounds . Users compounds . Users compounds . For exahttp://www.genome.jp/tools/pathpred/), is a knowledge based prediction system that uses data derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) in the form of the KEGG REACTION database and KEGG repair database [http://www.genome.jp/tools/e-zyme/) to assign a possible EC number (up to the EC sub-subclass). After assigning EC numbers, it is also possible to search the putative genes in the genome based on sequence similarity of known genes with the same EC sub-subclass [Another pathway prediction system, PathPred  is computational approach for development of novel pathways based on the reaction rules of the Enzyme Commission classification system . BNICE ghttp://FMM.mbc.nctu.edu.tw/ that is able to search all possible pathways between known input and output compounds among various species based on the KEGG database and other integrated biological databases [From Metabolite to Metabolite (FMM) is a web server freely available at atabases . FMM canatabases . This seatabases . For biohttp://osslab.ex.ac.uk/tinker.aspx) can be used to design synthetic metabolic pathways between user-defined target and source compounds [A recently developed web tool, Metabolic Tinker  of drugs, chemicals and natural products [•products .Toxicity Estimation Software Tool (TEST) for prediction of the acute toxicity of organic chemicals based on their molecular structures [•ructures .TOPKAT for prediction of the ecotoxicity, mutagenicity, and reproductive/developmental toxicity of chemicals [•hemicals .Ecological Structure Activity Relationships (ECOSAR) for estimation of the aquatic toxicity (acute short-term), toxicity and chronic (long-term or delayed) toxicity of industrial chemicals to aquatic organisms such as fish, aquatic invertebrates, green algae and aquatic plants by using computerized structure activity relationships [•ionships Estimation Programs Interface (EPI) suite for prediction of physical/chemical properties and environmental fate (eco-toxicity). The software calculates chemical property data using programs including KOWWIN, AOPWIN, HENRYWIN, MPBPWIN, BIOWIN, KOCWIN, WSKOWWIN, WATERNT, BCFBAF, HYDROWIN and ECOSAR [•d ECOSAR .CAESAR for assessment of chemical toxicity under the REACH [•he REACH .ToxiPred: A server for prediction of aqueous toxicity of small chemical molecules in Tetrahymena pyriformis[•yriformis.The automated Sanger method for sequencing is known as first generation sequencing, whereas newer methods developed for sequencing are considered next generation sequencing (NGS) . CommercThe initial steps of NGS involve generation of short reads and their subsequent alignment to a reference genome. The latter step is crucial for NGS technologies, and a variety of computational tools have been applied for genome sequence assembly including SSAKE , SOAPdenIn silico analysis of the bacterial genome leads to prediction of metabolic pathways for the biodegradation of xenobiotics and gives a holistic view of the metabolic network of particular bacteria [upriavidus necator JMP134 , which utilizes a variety of aromatic and chloroaromatic compounds as the sole carbon and energy sources, was sequenced and several genes coding the enzymes involved in the degradation of various xenobiotic compounds were identified [C. necator JMP134 genome contains 300 genes putatively involved in central ring-cleavage pathways of various aromatic compounds [NGS ignited a revolution in biodegradation and bioremediation with the concept of “from genomics to metabolomics.” Bacterial genomics is the study of the whole genomes of bacteria in which genes involved in biodegradation and other metabolic processes can be predicted. The whole genomes of several xenobiotic degrading bacteria have been sequenced using NGS technology, and several xenobiotic-degrading genes have been identified through gene predictions and annotation of the bacterial genomes -97. In sbacteria . Severalbacteria ,100. Forentified . The genompounds .silico analysis of the genome of Pseudomonas putida KT2440 showed that the presence of the following pathways for degradation of aromatic compounds: (i) the ortho pathway for the catabolism of protocatechuate (pca genes) and catechol (cat genes), (ii) the phenylacetate pathway (pha genes), and (iii) the homogentisate pathway (hmg genes) [nic cluster) and in a central meta-cleavage pathway (pcm genes) were also identified in the genome of this microorganism [In g genes) . Additioorganism .Mycobacterium vanbaalenii PYR-1 and identified 27 enzymes that were used to construct a complete pathway for pyrene degradation based on genomic and proteomic data [Whole-genome sequences are not only useful for prediction of genes and their functions, but also for identification of novel biocatalysts . Combinimic data .Several databases have been developed for providing the information on chemicals and their biodegradation. Users can use these databases to retrieve the information according to their research interests. For example, users can retrieve the information on toxicity, risk assessment, and environmental properties of the chemicals using chemical databases. Furthermore several bioinformatics tools have been developed for the prediction of the toxicity of chemicals. Users can use these tools for prediction of the toxicity of the chemicals. In addition, several pathway prediction systems are available for predicting the degradation pathways for those chemicals whose degradation pathways are not known in literature. The UM-BBD and PathPred are well known pathway prediction systems for biodegradation purpose. Using these pathway prediction systems, users can predict not only the degradation pathways, but also identify enzymes involved in the degradation pathways. This approach would be very useful for metabolic engineering and also to develop the strategy for bioremediation. The major problem related to the pathway predictions is that the predicted pathways are yet not experimentally verified. In the future, experimental studies should be carried out to verify the predicted pathways. Furthermore, the genomes of the several xenobiotics-degrading bacteria have been sequenced using NGS and the genes and enzymes involved in the biodegradation have been identified using gene-annotation. In future, molecular techniques along with bioinformatics tools may provide new insights into the genetics of the biodegradation.The authors declare that they have no competing interests.PKA collected all the relevant publications, arranged the general structure of the review, drafted the text and produced figures. HHB revised and formatted the review and also help to draft the manuscript. All authors read and approved the final manuscript.


KEGG, an iPad interface for KEGG data, which is empowered with multiple browsing and visualization capabilities.Kyoto Encyclopedia of Genes and Genomes (KEGG) is an online and integrated molecular database for several organisms. KEGG has been a highly useful site, helping domain scientists understand, research, study, and teach metabolisms by linking sequenced genomes to higher level systematic functions. KEGG databases are accessible through the web pages of the system, but the capabilities of the web interface are limited. Third party systems have been built over the KEGG data to provide extensive functionalities. However, there have been no attempts towards providing a tablet interface for KEGG data. Recognizing the rise of mobile technologies and the importance of tablets in education, this paper presents the design and implementation of iPathCaseKEGG has been implemented and is available, free of charge, in the Apple App Store (locatable by searching for “Pathcase” in the app store). The application provides browsing and interactive visualization functionalities on the KEGG data. Users can pick pathways, visualize them, and see detail pages of reactions and molecules using the multi-touch interface of iPad.iPathCaseKEGG provides a mobile interface to access KEGG data. Interactive visualization and browsing functionalities let users to interact with the data in multiple ways. As the importance of tablets and their usage in research education continue to rise, we think iPathCaseKEGG will be a useful tool for life science instructors and researchers.iPathCase Kyoto Encyclopedia of Genes and Genomes (KEGG) is one of the well-known biological knowledge bases, that links genomic information to high-level functions of the cell, for several organisms , 2. KEGGKEGG system is one of the PathCase family of systems, with a PathCaseKEGG database, populated from KEGG data. PathCaseKEGG system provides, over the web, additional browsing, querying, visualization, and analysis functionalities for KEGG data, such as interactive visualization [PathCaselization . And, Palization , 5 such lization , 7. BothKEGG which is an iPad-based system with a mobile interface that accesses PathCaseKEGG, KEGG Pathways, and ENZYME databases, and provides browsing and interactive visualization functionalities on the accessed data. To the best of our knowledge, as of August 2012, there have been no attempts, with the exception of SimGene [In this paper, we describe iPathCase SimGene , towardsiPad has been a revolutionizing mobile device with the multi-touch based interface. Apple has sold 9.25 million iPads only in the fiscal 2011 third quarter [KEGG, an iPad application to interface with KEGG data. The system comes with the following functionalities:Tablets also have started to attract attention in education. For instance, Apple has made an effort to revolutionize text books , and couInteractive visualization of metabolic pathways,Access to the KEGG web page regarding a pathway within the app,Highlighting a node and close proximity,Highlighting organism-specific subsets of reactions,Enzyme details pane from ENZYME Database for a selected reaction through EC (Enzyme Commission) Numbers .KEGG has been implemented, extensively tested for iPad 2, and is freely available in Apple App Store.iPathCaseAs of July 23, 2012, KEGG consists of seventeen databases: KEGG ORTHOLOGY, GENOME , GENES, DGENES and SSDB in genomic information, KEGG COMPOUND, GLYCAN, REACTION, RPAIR, RCLASS and ENZYME in chemical information, KEGG PATHWAY, BRITE, MODULE, DISEASE, DRUG and ENVIRON in systems information. The most unique data object in KEGG is the molecular networks -- molecular interaction, reaction and relation networks representing systemic functions of the cell and the organism. Systemic functions capture experimental knowledge from literature, which are organized in three forms: Pathway maps - in KEGG PATHWAY, Hierarchical list (ontology) - in KEGG BRITE, Membership list - in KEGG MODULE and KEGG DISEASE. These databases constitute the reference knowledge base for biological interpretation of genomes and high-throughput molecular datasets through the process of KEGG mapping, which is a process to map elementary datasets  to network datasets . Currently there are four types of mapping operations available in KEGG: pathway mapping, brite mapping, module mapping and taxonomy mapping, which may involve molecular or non-molecular datasets  and the network dataset (taxonomic tree). While the main focus of KEGG has been the molecular network, different types of data and knowledge, such as disease genes and drug targets, are also integrated as part of the KEGG molecular networks to support translational bioinformatics.PathCaseKEGG is a web-based system to store, browse, query, visualize and analyze KEGG metabolic pathways at different levels of genetic, molecular, biochemical and organismal detail. It integrates (i) browsing interface to access different pathway information and pathway-related information , (ii) an interactive client-side visualization tool for metabolic pathways, with powerful visualization capabilities, and with integrated gene and organism viewers, (iii) three distinct querying capabilities: keyword search function for pathways, processes, organisms, basic molecules, proteins and genes, an advanced querying interface for computer savvy users, and built-in queries for ease of use, that can be issued directly from pathway visualizations, (iv) pathway functionality analysis tools, such as the gene ontology viewer, and (v) pathway and process export and import capabilities.KEGG uses.iPad 2 (hereafter simply “iPad”) is a device about 7.3x9.5x0.3 inches with a 1024x768 pixel touch screen . The touUnlike traditional computer displays, the iPad is designed to be held in any orientation, with the display’s contents rotating and resizing to fit the users perspective. There are four possible orientations, but the shortcuts “landscape” and “portrait“ suffice for describing and implementing most interfaces .The most basic component of an iPad interface is the “view”. A view is a rectangular region that is capable of displaying content, receiving events such as gestures and device orientation changes, and delegating these tasks to “subviews”. Views can contain buttons, toolbars, lists, other views, and arbitrary graphical content. “Nested views” consist of a view hierarchy.A “button” is a view that renders itself in enabled, disabled, and pressed states, and invokes some action when a touch is released inside its bounds.“Toolbars” are views that can be inserted at the top or bottom of another view. They typically contain buttons, text labels, progress indicators, and more.A “scroll view” contains another, larger view that the user can move around with one or two fingers. For example, one might be used to display a photo that is larger than the screen. Scroll views can be dragged around with one finger, or zoomed out by “pinching two fingers” together.“Tapping twice” in the same location typically zooms in on the tapped location.One common use for the scroll view is in a table view, used to display lists of content. The user can scroll up and down the list and tap a row to invoke some action. Some types of views exist only to contain other views. One view of this type is the “popover”. A popover appears temporarily on the screen, allowing the user to perform some quick interaction before dismissing it by tapping outside its bounds. The popover is a key component in the master-detail interface, a common arrangement of views in iPad applications.A diagram of the master-detail interface is shown in Figure KEGG interface to provide context to the implementation details that follow. iPathCaseKEGG divides the iPad screen into (i) a master interface  that provides navigation functionality and metadata for the currently selected object, and (ii) a details interface (detail view) that displays content chosen from the sidebar, such as documentation or a graph view. When the device is in the landscape  orientation, the master view is shown to the left of the detail view. In portrait  orientation, it is accessed via a button in the upper left corner of the screen. This interface is described visually in Figure KEGG displays a list of metabolisms  and their documentation links in the sidebar as seen in Figure KEGG database, and pathway layouts have come from GraphViz [Here, we give a brief overview of iPathCaseGraphViz . TCA cycKEGG, iPathCaseKEGG uses data from the ENZYME enzyme nomenclature database [In addition to displaying a node’s long description from PathCasedatabase  to show database . An examKEGG’s architecture consists of a few well-defined components:iPathCaseUser interface objects present things like the master-detail interface, lists of pathways, organism selection, etc.Web service request classes make HTTP requests to the PathCaseKEGG server, process the response, and notify other objects of the results.Encapsulated components provide specific functionalities such as reading the ENZYME database [database  and genedatabase .KEGG. Given the event-driven and nonlinear nature of GUI programming, it is more appropriate to describe events and notifications rather than straight line execution. The user begins at the pathway list view, which allows the selection of a pathway to view. Selecting a pathway initiates a call to the PathCaseKEGG web service , the result of which is stored as an in-memory data structure. An event is then triggered to render the graph view from the stored data structure, and then display the rendered view itself. The graph view sends event notifications when nodes are tapped, which trigger the display of new information in the sidebar. This information comes from both the pathway data structure and a stored copy of the ENZYME database. There are pieces missing from the diagram in Figure Figure KEGG. There are two kinds of edges in the diagram. Present edges are drawn when one view controller causes another to be displayed in its content area. For example, UISplitViewController shows the PCMasterViewController (via UINavigationController) in its sidebar area or popover, and the PCDetailViewController in the rest of its content area. Notify edges represent notifications that are sent between objects. A notification is an abstraction of message passing. An object may register for a notification of a certain name. When any object posts a notification with that name, the object that registered for the notification is passed a message that was specified at registration time. This message includes a notification object, which may contain additional data depending on the specific notification that was sent. This system allows objects to send and receive events without storing unnecessary references to other objects. A good example of notifications can be found in iPathCaseKEGG. PCMasterViewController, a view controller included in Figure PCPageViewRequested that contains the URL of the corresponding web page, in this case http://www.kegg.com/.Figure PCDetailViewController responds to five different notifications, but only two are shown in Figure PCPathwayTableViewController and PCNodeInfoViewController, are displayed in the master content area. The master view makes use of a navigation stack. The top item on this stack is the PCMasterViewController, which pushes a PCPathwayTableController onto the stack when a pathway category is selected. When one of the pathways in that list is selected, a “show pathway” notification is posted, and a PCNodeInfoViewController is pushed onto the stack. This is the view controller that displays information for the current selection, so it responds to notifications sent by PCGraphViewController about changes in the user’s selection.KEGG does not download the entire pathways data in PathCaseKEGG database at once. HTTP requests are made as new data is needed, and the result is cached in the device’s internal storage. It has a separate class for each web service, named PC < WebService > Fetcher. iPathCaseKEGG’s web service classes fire events to a central notification system when they have completed, and controller objects check to see if any new web services should be invoked due to dependencies being made available.iPathCaseAfter the XML response has been parsed into one or more KEGG internal objects, these objects are not serialized. In other words, all objects generated by web services are built from the XML each time they are needed.KEGG web service GetPathwayData. Rather than calling the web service when the user requests to view a pathway, the web service responses for all pathways are included with the application itself in the form of text files. Each text file contains the HTTP response to a web request for the graph data for one pathway. Figure GetPathwayData’s response format to the graph model in GML (Graph Markup Language) format. The GML is generated by the yFiles library [KEGG graph representation has a PCGraphNode class to represent a graphical node and a PCGraphEdge class to represent a graphical edge. A PCGraphModel contains sets of these node and edge objects to represent single pathway visualization. In iPathCaseKEGG, the node and edge objects contain no parsing code, but instead expose all graphical attributes as writeable properties. The responsibility for parsing a graph definition and building a graph model with the correct graphical attributes is moved to an external object that is not part of the graph rendering system. After data has been parsed, the graph is ready for rendering by a PCGraphView, which uses a Core Animation layer hierarchy to draw the graph efficiently. There are three layers in a PCGraphView. From top to bottom:Text layer for drawing node labels. This layer contains one sub-layer for each node that renders the text for its label.Node layer for drawing node shapes, andEdge layer for drawing edges.The pathway visualization data (graph data) is provided by the PathCase library . iPathCaOnce these layers are rendered, they are drawn to the screen by a scroll view, which takes care of translating and scaling them in response to user interaction.KEGG. These interactions are illustrated in Figure PCPathwayTableViewController. In response to the notification posted by this action, PCDetailViewController sets its content area to the “Loading…” screen, and invokes the PCGraphDataFetcher web service class to load the pathway’s information from the PathCaseKEGG web service GetGraphData. This class produces a set of PCNodeInfo objects that represent all aspects of the graph except node and edge positions. This set is given to a PCLayoutFetcher web service class, which attempts to download a layout for the graph from the PathCaseKEGG web service RetrieveLayout. If successful, it builds a PCGraphModel including a pre-drawn and manually beautified  layout information using the information in the response. If not, it builds a PCGraphModel from the PCNodeInfo objects, and generates a dynamic layout. There are two advantages to using frozen layouts provided by RetrieveLayout. The first is that they are curated manually, and tend to look nicer as a result. The second is that, when a molecule is used in multiple reactions, each duplicate occurrence is drawn for each reaction, thereby eliminating many incoming and outgoing edges for a single common molecule such as water, or energy molecules ATP or ADP, and, hence, simplifying the visualization drastically. When a frozen layout is not available, iPathCaseKEGG generates dynamic layouts where a metabolite that is used in multiple reactions will appear only once, often resulting in scrambled layouts with one metabolite node having many incoming edges from many reactions. When the layout step is complete, PCLayoutFetcher posts a notification containing the generated PCGraphModel object. This notification is received by PCDetailViewController, which creates a PCGraphViewController and puts it in the detail view content area. It also pushes a PCNodeInfoController onto the master view navigation stack, which receives events from the PCGraphViewController about what it should display.The loading and display of a pathway visualization requires interactions between several components of iPathCaseKEGG pathway visualization includes the ability to select only a subset of organisms to display information from. If reaction R is present in organisms A and B but not C, and organisms A and B are disabled, then reaction R will be dimmed in the visualization. Next we briefly describe the process of displaying the organism hierarchy, shown in Figure PCGraphViewController to display a popover with a navigation stack. The first item on this stack is a PCOrganismHierarchyTableViewController, which presents a table view containing hierarchical categories of organisms. Tapping a row in the table view either pushes the subcategories of that category onto the navigation stack if the row represents a subcategory, or it toggles the enabled/disabled state of an organism if the row represents an organism. The set of enabled organisms is then posted as a notification that is received by the PCGraphViewController, which updates the PCGraphModel with the enabled/disabled state of each individual node based on the enabled/disabled state of the organisms.The iPathCaseKEGG contains human-curated frozen layouts for some pathways, but not all. In order to display the pathways without frozen layouts, iPathCaseKEGG uses the Graphviz library. Graphviz is a system which, among other things, provides graph layout and visualization functions [KEGG encapsulates the layout functionality of the Graphviz library in a class called GVGraph. An instance of this class represents a graph with nodes, a label for each node, and one directional edge that connect nodes.PathCaseunctions . It provKEGG uses the description, alternate name, catalytic activity, cofactor, and comments, ignoring the remaining fields.The ENZYME database is provided in a flat text file format , 15. ThiKEGG, users can interact with KEGG pathways using a multi-touch interface and gestures, which is a major improvement over the static visualizations provided by KEGG. We believe that iPathCaseKEGG is useful for (i) students who use KEGG to learn metabolic networks, (ii) instructors that teach material using a visual tool and (iii) researchers who would like to employ metabolic pathways in their research.Tablets have revolutionized the mouse and keyboard based interface with a mobile and a multi touch based interface. Many gestures such zoom-by-pinch or swipe to turn the page are very user-friendly. Not to mention the new retina display, iPad interface has been a breakthrough so far. As stated before, tablets have already been adopted as an educational tool. We foresee that tablets will keep attracting more attention and will be more widely used as prices drop; universities that distribute laptops to students will start to give tablets; classes will be held using tablets such as iPads; heavy hardcopy textbooks will be replaced by e-books, and students will be able to carry thousands of them in a single tablet device. For our application, KEGG is an important source for life sciences researchers, and for biology/biochemistry education as it maintains extensive biochemical pathways data. With the interactive visualization interface of iPathCaseKEGG system and the iPathCaseKEGG app. Most of the functionalities listed are self-explanatory, and, next, we discuss only a few of the functionalities listed in Table Table Greying out reactions that are not present in the selected organism set” is available in both PathCaseKEGG and iPathCaseKEGG, which allows the user to focus on only those reactions of the pathway available in the chosen organism set. The functionality “Moving nodes to rearrange the layout” of the pathway at hand is also available in both interfaces. As it turns out, biochemists always would like to have the same layouts for pathways, and automated layouts may need to be manually edited by some users; hence this functionality is considered essential for life sciences researchers. Also, this functionality gives the user the flexibility of changing the layouts based on their specific needs, i.e., emphasizing a certain part of a given pathway.The functionality “Identifying the location of the gene producing the catalyzing enzyme" allows pathway analysis in terms of the locations of genes producing catalyzing enzymes of different reactions. This functionality can be viewed as a higher level pathway analysis functionality, and is not provided in iPathCaseKEGG.The functionality "Common molecules are those molecules that participate in many reactions, i.e., CO2, ATP, ADP, NAD+, NADP+, NH4+, NADPH, NH3, NADH, Oxygen, CoA, H+, H2O, H2O2. Common molecules can also be regarded as social network “hubs” of pathways. PathCaseKEGG allows users to “hide” common molecules when multiple pathways are drawn together as it reduces the visualization complexity of pathways. This feature is also not provided in iPathCaseKEGG since, due to its small screen size, we have opted not to allow users of iPathCaseKEGG to visualize multiple pathways together as one sub-network of the whole metabolic network.KEGG server in a much more piecemeal way. The data for each pathway is encapsulated entirely in a single request; so there is no need for complex dependency management or even data storage above and beyond simple HTTP request caching. The data models, HTTP requests, and parsing are cleanly separated.The biggest challenge of the project was controlling the passage of information from the PathCase servers to the iPad application, both in how the HTTP requests were made and how the data was translated into a form usable by the rest of the application. The data is requested from the PathCaseKEGG (as well as other iPathCase systems). In terms of software architecture, in the future, the system components that download the data, parse it, build model objects, as well as various controls should be re-factored to separate each of these tasks explicitly. Additionally, there should be a more formal data dependency tracking system to control the bulk download of the web service data. It is common to have HTTP requests for web services A, B, and C rely on data from request X, so there should be a formalized dependency graph that tracks this information.When the project started, we underestimated the software system complexity involved in iPathCaseKEGG’s biggest technical issue is a noticeable hang when selecting a large pathway to view, due to the HTTP requests and responses that involve creation and transfer of large (2–5 megabytes) XML documents, sometime with large frozen layout information, as well as dynamic layout creation delays due to the use of Graphviz. Caching pathways a priori has mostly, but not fully, eliminated this problem. However, when a new or revised pathway in the PathCaseKEGG system is encountered and it needs a dynamic layout generation, the system takes significant amounts of time (ranging from 30 to100 seconds) to request and receive the new/revised pathway, parse and produce a new layout, and display it. A delay of this magnitude sometimes leads users into thinking that the app has crashed, which is not acceptable in an online visualization environment. More effort should be spent for both (a) lazy processing and downloads of new pathways , and (b) moving processing to a separate thread to allow the main user interface to remain responsive.iPathCaseYet another technical challenge was rendering large graphs efficiently on a device with limited memory and processing power. Fortunately, the iOS application frameworks made pathway rendering possible, though various low-level optimizations were required at different stages of development.Finally, similar to graph rendering, dynamic graph layouts accounted for a considerable amount of development time, despite the convenience of the Graphviz library to provide layouts on an as-needed basis.KEGG has been a successful exercise in extending the PathCase architecture and philosophy to new devices and new users. It is one thing to design a web interface and server backend in tandem, and another to apply the server’s web service interface to an entirely new set of applications. iPathCaseKEGG shows that PathCase’s architecture serves the purpose for which it was built: to provide a platform for analysis and reference tools on top of multiple data sources for pathways. iPathCaseKEGG :iPathCaseAllows browsing of all KEGG pathways,Links reactions with the external ENZYME database, andAllows access to the KEGG web page corresponding to any pathway.SMDA[The application adapts the basic PathCase pathway visualization interface concepts for a touch screen, specifically making heavy use of iOS idioms. This adaptation and iPathCaseSMDA, anotherKEGG opens up many exciting possibilities for the use of tablets in educational and research environments. The best thing that can be done to make these possibilities become realities is to perform studies and have discussions with practicing life scientists and students. Formal and informal feedback should be gathered from possible users to determine the most effective future improvements. Even their simple presence in the iOS online store should provide helpful feedback (positive or negative) that will affect the development of both applications.iPathCaseKEGG brings interactive pathway visualizations, reference materials, and even research tools to desks, meetings, and couches.Interactive visualizations of complex biological data are no longer limited to immobile desktop workstations or cumbersome laptops; the iPad is even less intrusive than a physical textbook and can display information in new and interesting ways. iPathCaseProject name: iPathCaseKEGGProject download source: Apple App StoreOperating system: iOS 5.Programming language: Objective-C.Other requirements: iPad2 or newer.


Microorganisms are known to counteract salt stress through salt influx or by the accumulation of osmoprotectants . Understanding the pathways that synthesize and/or breakdown these osmoprotectants is of interest to studies of crops halotolerance and to biotechnology applications that use microbes as cell factories for production of biomass or commercial chemicals. To facilitate the exploration of osmoprotectants, we have developed the first online resource, ‘Dragon Explorer of Osmoprotection associated Pathways’ (DEOP) that gathers and presents curated information about osmoprotectants, complemented by information about reactions and pathways that use or affect them. A combined total of 141 compounds were confirmed osmoprotectants, which were matched to 1883 reactions and 834 pathways. DEOP can also be used to map genes or microbial genomes to potential osmoprotection-associated pathways, and thus link genes and genomes to other associated osmoprotection information. Moreover, DEOP provides a text-mining utility to search deeper into the scientific literature for supporting evidence or for new associations of osmoprotectants to pathways, reactions, enzymes, genes or organisms. Two case studies are provided to demonstrate the usefulness of DEOP. The system can be accessed at.Database URL:http://www.cbrc.kaust.edu.sa/deop/ Microorganisms need to maintain a stable and a constant cell volume and turgor pressure across their membrane to sustain their metabolic activities. Fluctuations in external salt concentrations would either force the water out of the cell affecting their metabolic activities  or causeThere are two known strategies used by microorganisms to cope with salt stress. Halophiles, which are microorganisms that thrive in extreme concentrations of salt, use the ‘salt-in-cytoplasm’ mechanism to counteract high salt concentration of the environment. The metabolic machinery is not hindered by the elevated salt concentrations. In fact, these high salt concentrations are required to protect their adapted biomolecules against denaturation. The salt-influx mechanism has so far been reported in a few microorganisms and is not widespread , 2.Supplementary Table S1 for a list of osmoprotectants.The majority of microorganisms cope with salt stress by accumulating uncharged, highly soluble organic compounds. These compounds do not interfere with the microbe’s metabolic machinery and hence can be accumulated in large quantities to maintain the equilibrium balance with the outer environment without allowing in large quantities of salt ions. Their solubility and non-interference characteristics are the reason they are named compatible solutes. They are also called osmoprotectants because they protect against salt and osmotic stress. Osmoprotectants include a variety of compound classes, such as sugars , amino acids , polyols  and heterosides (glucosylglycerol and mannosucrose) , 2. See de novo biosynthesis , whole cells, salt antagonists or salt protective agents. Ectoine and hydroxyectoine are two compatible solutes that were successfully synthesized through biotechnological procedures in a process called ‘bacteria milking’ . Culture tobacco , 18.http://www.cbrc.kaust.edu.sa/deop/. DEOP is free for academic and non-profit use.Online resources are increasingly available that compile genomic and proteomic information belonging to extremophiles such as HaloWeb , HaloBasosmo* OR organic solute* OR osmol* OR compatible solute* OR stress OR halo* OR salinity OR salt OR rhizosphere OR rhizob* OR brine OR regulatory pathway* OR metabolic pathway*. The search returned 903 563 articles as of the 25 March 2014.A comprehensive and accurate search of the scientific literature for osmoprotectants and related pathways is cumbersome and labour-intensive. We simplify the search and information gathering using text mining in the preprocessing stages followed by manual curation. We reduced the PubMed search space by first applying the following keyword combination: Compatible solute(s), osmoprotectant(s), osmol[iy]te(s), osmoregulation, salt stress and stress response. Co-occurrences in the PubMed database were recorded for terms in the chemical compound dictionary in relation to terms in the second dictionary. For each association in the literature, we calculate the number of articles in which it occurred and store the articles’ Pubmed IDs (PMIDs). We then ranked these associations in a descending order of the number of articles.We compiled a non-redundant dictionary file of all chemical compounds and their synonyms from the MetaCyc database. A second dictionary file was compiled that contained the following keywords (one per line): compound_X AND .The association report narrows down the search scope to those compounds that have been mentioned in the context of osmoprotection or salt stress response. Starting from the associations with the highest article count, we either pull and read the abstracts as per the association PMIDs or re-query PubMed using the following: Escherichia coli protects against osmotic stress by accumulating ubiquinone-8 (Q8) (E. coli) strongly suggests that Q8 is a true osmoprotectant at least in E. coli, and the abstract content confirmed the assumption. We minimize errors in the curation by finding more supporting literature, including full-text articles. Based on the evidence, we classified a compound as an osmoprotectant or a candidate osmoprotectant. We also labelled precursor compounds and compounds involved in osmoregulation according to the literature evidence. A compound is excluded if we fail to assign it any of the labels above.We initially skimmed through abstracts searching for clear reference to the compounds of interest in an osmoprotection context. For example, it was recently reported that e-8 (Q8) . For ourAfter we have compiled a curated list of osmoprotectants, we search databases such as BioSystems , MetaCychttp://www.uniprot.org/) or by searching the external links on the relevant pages from these databases. We manually checked the consistency of the accession numbers. For downloads from the NCBI databases, we used the Entrez programming utilities (E-utilities) to query and download the sequences of interest. For obsolete sequences, we downloaded the replacement sequences, but often the whole-genome sequence is used as a replacement. In many cases, the gene accession number refers to the full genome too. We dealt with such inconsistencies by first attempting to map to UniProt accession numbers. If a hit is found, we convert the UniProt ID to the NCBI gene ID. If we fail to find a UniProt ID, we search NCBI gene using the gene name or synonyms in combination with the organism name.One of the objectives of this work is to provide a mapping facility whereby a gene can be mapped onto an osmoprotection pathway or a genome can be investigated for potential osmoprotection pathways. This requires a comprehensive list of known genes and enzymes involved in the reactions and pathways collected. We used the accession numbers provided by pathway databases such as MetaCyc to download the genomic or protein sequences from sequence databases such as, NCBI Entrez , RefSeq The compiled list of genes is used as a BLAST-able backend database. Hits against the database are assigned to the respective pathways. Pathway completeness is estimated by comparing the detected genes against the total number of genes in that pathway. Pathway completeness is a difficult estimate, and therefore, should be used with caution especially when whole genomes are mapped. Genes required for a pathway may differ from one organism to another even for those that may be considered closely related.osmo* [title/abstract] OR compatible solute* OR stress [title/abstract] OR haloadapt* OR halophili* OR halotoler* OR salinity OR salt OR rhizosphere OR rhizob* OR brine OR regulatory pathway* OR metabolic pathway*. The query returned 728 162 articles that were downloaded in the XML format for further processing.The DEOP literature knowledgebase was compiled using the PubMed biomedical literature database as source. As of April 2014, PubMed lists >23 millions citations from a large scope of fields. We focus our knowledgebase by discarding irrelevant literature. To further narrow the search space, PubMed was queried using a stricter keyword combination: http://www.orioncell.org). For the purpose of this research, the DES pipeline was redesigned to operate on a high-performance computing cluster. The input for the pipeline is the XML literature corpus generated above and six dictionaries .3. The literature explorer modulesThe mapping modules are based on the NCBI BLAST program family: blastp (protein vs protein), blastx (translated nucleotide vs protein), blastn (nucleotide vs nucleotide) and tblastn (protein vs translated nucleotide). The default e-value for the BLAST queries is set to 10Supplementary Material).The literature explorer modules provide concept association reports in textual, spreadsheet or in a graphical format. The reports are assembled from the automatic extraction of the most relevant literature and from document clustering based on content similarity. Contents are evaluated and documents are clustered uzsing self-organizing maps , 39. Cluhypothesis/association explorer” that is based on Swanson’s ABC model  mining and association mining. The FP miner discovers concepts that frequently appear in the literature. The methodology is based on the FP-tree structure and the FP-growth method . The assData enrichment/integration modules include a local MRS-based database system , which hThe database is hosted on a PostgreSQL server v.9.3. The web front of the database was coded in PHP. Mapping of genes/genomes is handled using a combination of PHP and Perl scripts.To date, we are not aware of a resource that is dedicated to providing detailed and curated information about osmoprotectant compounds, complemented by relevant pathways, reactions, enzymes, genes and organisms. DEOP addresses this gap. DEOP is a hybrid online resource of human-based curation and computational-based compilation. The foundation of the resource is the manually curated list of osmoprotectants. We built on the list by adding reactions that produce or consume these compounds, and then added the pathways and involved genes and proteins. A rich text-mining and cross-referencing layer was then added to enrich the knowledge base. For the current statistics of the database, refer to Access to the database is facilitated by means of combined browsing and searching. The database has five entry points depending on the object of interest. The database can be browsed for compounds, pathways, reactions, enzymes, their genes and organisms. In each browser page, data are represented in tabular format with Ajax case-insensitive search boxes on top of each column, which can be used to filter the content in their respective columns. Multiple search boxes can be populated simultaneously for combination searches. We also provide an all-columns search box that filters content in all of the columns, which can also be used in combination with the other column-specific filters.To facilitate browsing, the tables are paginated with only 10 rows showing on the first page. Columns can also be sorted alphabetically by clicking on the column title. A left click on the text in the cells activates context menus that are content-dependent. For example, a left-click on a pathway name on the pathways page provides three menu options: ‘MRS cross-reference’, ‘Literature References’ and ‘Pathway Details’.The MRS cross-reference context menu searches our large MRS-based databank  using thThe literature reference menu links the biological entity of interest, e.g. a compound or a gene, to the preprocessed PubMed abstracts. The report highlights, using colour, the current biological term and other relevant terms (if found) in the abstracts. This provides a comprehensive, current and content-specific literature support that is expected to save time and effort and even unearth new information.Further details are provided for each type of biological data through the context menu. The details page for a pathway lists its osmoprotection classification, affected compounds, participating reactions and host organisms. For a compound, the resource displays the formula, an interactive Jmol-based 3D structure , ChEBI sDespite the existence of curated and well-established metabolic pathway databases, none provide standardized osmoprotection labelling. With the mapping tool that we developed, the utility of our resource is extended beyond mere listing of biological terms. It takes genomic or protein sequences and maps them to the pathways using NCBI BLAST programs. The stringency of the mapping can be adjusted by tuning the minimum bit score and the minimum alignment length and/or the percentage identity. Pathway completeness is calculated based on the number of gene hits to a particular pathway divided by the total number of genes in that pathway. This completeness measure can assist the user to decide whether a pathway really exists in their data.Manual curation is labour-intensive and time-consuming especially considering the amount of publications and the rate of publications. A significant part of DEOP is the ‘Literature Explorer’ module based on osmoprotection-related literature available from the PubMed. The module consists of a number of sub-modules: literature statistics, literature relevance, document clustering and association reports. In addition, various download services are linked to the modules that allow the user to get data in spreadsheet forms and analyse them offline. The text-mining modules are supported by two data-mining modules aimed for association and FP discovery.The literature statistics report gives a broad overview of available literature. It summarizes the results of the text-mining process by giving information about the number of relevant abstracts and number of recognized biomedical concepts available in the system. In addition, charts showing two types of distribution are displayed: (i) distribution of dictionaries and (ii) frequency of concepts regardless of dictionary. This information can be used as a quick reference, for example, to see the number of pathways mentioned or described in the literature. Related to this overview is the literature relevance report that actualizes the statistical overview into annotated text available for reading. Abstracts are sorted by frequency of terms found in them, and can be viewed as annotated text and in original form. In the annotated text, concepts belonging to different dictionaries are highlighted using different colours to help the user to focus on certain biomedical concepts with the ability to switch on and off one or more dictionaries. This useful feature is available throughout the system. More sophisticated literature relevance overview is given in the document clustering report. Documents are evaluated using a self-organizing map. The algorithm uses the frequency of concepts identified in the documents. The weight of a feature equals its frequency in the cluster divided by the total number of documents in the cluster. The clustering report offers the top five clusters that have >400 documents.For a researcher, one of the most useful modules is perhaps the association report. Concepts found in the literature are grouped according to the dictionaries and then cross-referenced. This feature allows the user to browse or search through the list of concepts, discover associated concepts, generate and visually explore association networks and hypotheses and download customized reports in the form of spreadsheets. Each concept is enriched by a plethora of additional information hosted on our local MRS database that is a click away.The underlying DES hypothesis discovery module is based on Swanson’s work , 49 and We implemented two data-mining algorithms to text mine the database. The first is the FP miner, which discovers patterns of concepts that frequently appear together in the literature. For example, results show that the concept glutathione appears with the concept oxygen in 6375 documents . The number of documents in which one or more terms co-occur is called support. The user can adjust the support using a percentage slider to choose the minimum support number while searching for frequent patterns.The second data-mining module is the association miner, which discovers the probability of the co-occurrence of various concepts in the literature. For example, we noted that the combination of concepts alanine, rat and aspartate occurs together in 682 or in 0.14% of all documents. In other words, the supporting literature for this rule is 0.14%. In the remaining literature, those concepts may occur in different binary combinations, singles or not at all. We also observed that if a document contains the combination of concepts alanine and rat, there is a 65% chance of finding aspartate. This association can be formulated as follows: the occurrence of alanine and rat implies the occurrence of aspartate with 65% confidence. The association miner is an interactive tool that provides the user sliders to choose the number of top associations as well as the minimum confidence.Betaproteobacteria KB13 , Methylophilales bacterium HTCC2181 , ‘Candidatus Pelagibacter ubique HTCC1062’ , ‘Candidatus Pelagibacter sp. HTCC7211’ , ‘Candidatus Pelagibacter sp. IMCC9063’ , the alphabacterium IMCC14465 strain (BioProject: PRJNA174247) Haloplasma contractile SSD-17B (BioProject: PRJNA67925), Salinisphaera shabanensis E1L3A (BioProject: PRJNA67923) and Halorhabdus tiamatea str. SARL4B . SSD-17B, E1L3A and SARL4B were isolated and sequenced from the Shaban Deep  against our gene data set. Then, hits were assigned to the various pathways. We compared the genomes against each other for pathways presence/absence and completeness and enrichment. We only included pathways that were at least 50% complete. Two heatmaps were generated: (i) for the first heatmap, we used the completeness values that range from 0 to 1 as an approximate binary estimate of the presence or absence of pathways , was also solely found in these three bacteria. Unique to H. contractile SSD-17B are the pathways alanine degradation IV and lysine fermentation to acetate and butyrate that synthesize the osmoprotectants L-alanine and L-lysine as side products.In both panels of S. shabanensis E1L3A uniquely contained the following pathways: glycine betaine biosynthesis from glycine IV , 2,2’-dihydroxybiphenyl degradation , isopropylamine degradation  and dTDP-D-forosamine biosynthesis (synthesizes L-glutamate as an intermediate product). Interestingly, the archaeon H. tiamatea str. SARL4B lacked all of the bacterial pathways suggesting that this archaeon copes with the harsh conditions at the sediment-brine interface using different mechanisms; perhaps the salt-in-cytoplasm strategy. In summary, the purpose of this brief comparison is to demonstrate the utility of DEOP in study adaptation strategy at the genomic level.Avicennia marina rhizosphere (RSMgr1-4), whereas the other two are for neighbouring control sediment (CS1-2). Artificially duplicated reads were removed using CD-HIT-454 with default settings . Four of the metagenomic data sets are for the mangrove settings . We usedsettings  to discasettings .Figure The mangrove samples clustered together in both panels in K-252, rebeccamycin, candicidin and oleandomycin are all antibiotics produced from actinomycetes, whereas dTDP-D-desosamine is an essential component of macrolide antibiotics revealing the availability of the osmolytes used in these pathways, that provide osmoprotection, and using these osmolytes to produce antibiotics provides an additional advantage of eliminating competitors that do not have resistance to them.Putrescine is a mangrove exudate that has been reported to impact the rhizophere microbiome as increased uptake of putrescine causes decreased growth rates and consequently decreased ability for competitive colonisation ; but putDEOP is an online resource that gathers literature verified osmoprotectants and lists all available pathways and reactions that affect them. Through the genes associated with the pathways, we are able to predict the presence of these pathways in organisms with available genomic sequences. Although, the osmoprotectants listed on the resource were reported in the literature as such, the detection of a pathway in a genome by computational methods is not conclusive evidence for its activation or its role in osmoprotection, as biodiversity is incredibly big and many organisms can, under abiotic stress, use some pathways and not others despite the presence of all. The causes of this selectivity are unknown, although the environment is the prime suspect. Having said this, the resource will facilitate the assignment of genomic or protein sequences to the pathways that impact on osmoprotectant compounds. The application of such resource is evident in studies interested in abiotic stress responses and plant stress tolerance, as well as industries interested in microbes as cell factories.Supplementary data are available at Database Online.


Pathway analysis has been widely used to gain insight into essential mechanisms of the response to myocardial infarction (MI). Currently, there exist multiple pathway databases that organize molecular datasets and manually curate pathway maps for biological interpretation at varying forms of organization. However, inconsistencies among different databases in pathway descriptions, frequently due to conflicting results in the literature, can generate incorrect interpretations. Furthermore, although pathway analysis software provides detailed images of interactions among molecules, it does not exhibit how pathways interact with one another or with other biological processes under specific conditions.We propose a novel method to standardize descriptions of enriched pathways for a set of genes/proteins using Gene Ontology terms. We used this method to examine the relationships among pathways and biological processes for a set of condition-specific genes/proteins, represented as a functional biological pathway-process network. We applied this algorithm to a set of 613 MI-specific proteins we previously identified.A total of 96 pathways from Biocarta, KEGG, and Reactome, and 448 Gene Ontology Biological Processes were enriched with these 613 proteins. The pathways were represented as Boolean functions of biological processes, delivering an interactive scheme to organize enriched information with an emphasis on involvement of biological processes in pathways. We extracted a network focusing on MI to demonstrate that tyrosine phosphorylation of Signal Transducer and Activator of Transcription (STAT) protein, positive regulation of collagen metabolic process, coagulation, and positive/negative regulation of blood coagulation have immediate impacts on the MI response.Our method organized biological processes and pathways in an unbiased approach to provide an intuitive way to identify biological properties of pathways under specific conditions. Pathways from different databases have similar descriptions yet diverse biological processes, indicating variation in their ability to share similar functional characteristics. The coverages of pathways can be expanded with the incorporation of more biological processes, predicting involvement of protein members in pathways. Further, detailed analyses of the functional biological pathway-process network will allow researchers and scientists to explore critical routes in biological systems in the progression of disease. Knowledge of molecular-level interactions and reactions has been curated in many knowledge databases, forming biological pathways. These knowledge databases include BioCarta :Despite manual curation and careful revision, different knowledge databases could have different descriptions, participating molecules, interacting diagrams, and supporting literature for similar pathways. For example, considering the Transforming Growth Factors Beta (TGF-beta) signaling pathway in human, KEGG reported as KEGG - "Once phosphorylated, R-Smads associate with the co-mediator Smad, Smad4, and the heteromeric complex then translocates into the nucleus. In the nucleus, Smad complexes activate specific genes through cooperative interactions with other DNA-binding and coactivator (or co-repressor) proteins".http://www.genome.jp/kegg-bin/show_pathway?hsa04350). In the nucleus, the activated R-SMAD promotes transcription in complex with a closely related helper molecule termed Co-SMAD (SMAD4)".http://www.reactome.org/PathwayBrowser/#DIAGRAM = 170834&PATH = 162582). from the MI-specific protein-protein interaction network .In this study, we started with 613 MI-specific proteins to find enriched pathways and GOBPs . We perfWe previously identified 613 proteins specific to MI in an MI-specific protein-protein interaction network (MIPIN); the network and its protein members were used here to demonstrate the developed method .Count to be 2 and EASE to be 0.05, to obtain enriched GOBP terms, KEGG and Reactome pathways [Many tools are available to provide gene-annotation enrichment analysis and pathway mapping. We performed functional annotation analysis using DAVID Functional Annotation Tool, with the parameters pathways .G, two annotation terms i Tand j Tannotated by two set of genes i Gand j G, we denoted the number of proteins annotated by both terms as ija, the number of proteins annotated by i Tbut not j Tas ijb, the number of proteins annotated by j Tbut not i Tas ijc, and the number of proteins not annotated by neither terms among the union of proteins annotated by N annotation terms as ijd.We evaluated the pairwise similarity between different annotation terms, including GO terms and pathways using Kappa statistics because annotation terms sharing common members might be related to one another . ConsideThus, we have,ij κwas defined as,The Kappa score ijagree) was the observed percentage agreement and Pr(ijrandom) was the overall probability of random agreement for annotation terms i Tand jT. The observed percentage agreement Pr(ijagree) could be calculated as follows,where Pr(i Tannotates j Tannotates ijrandom) could be calculated as,Out of total number of associated proteins, A high Kappa score indicated that two annotation terms share many common proteins.GraphGOBPenriched was constructed to describe the relationships among enrichedGOBP Nenriched GOBP terms, i.e., GraphGOBPenriched = , |GOBP|= NenrichedGOBPV, and GOBP Edefines the set of edges in the graph. The relationships between GOBP terms, represented by edges connecting them, were evaluated based on the ancestor/offspring relationships in the complete directed acyclic graph of all GOBP terms from the Gene Ontology Consortium. We mapped enrichedGOBP Nenriched GOBP terms to the corresponding vertices of the complete directed acyclic graph of all GOBP terms from the Gene Ontology Consortium using the package "GO.db" from Bioconductor [GraphGOBPComplete =  be the complete directed acyclic graph of all GOBP terms. Then, GOBP Vis mapped to completeGOBP V(GOBP V⊂ V' and V' ⊂ completeGOBPV). Two GOBP terms would be connected if there existed a link between this pair of vertices in the complete graph of GOBP. All networks and graphs in this study were constructed and analyzed with the assistance of the package 'igraph' in R [An undirected GOBP graph onductor . Let Graph' in R .totalGOBP Nx totalPathwayN, where totalPathway Nis the total number of pathways including Biocarta, KEGG and Reactome pathways. Each row of the similarity matrix represents a GOBP term, and each column represents a pathway. Top 1% of the most similar pairs of pathway and biological process were selected and connected based on the Kappa similarity scores. Figure PathwayGOBPE. We then established the pathway and GOBP graph as an undirected bipartite graph BipartiteGraphPathwayGOBP = {Pathway, VGOBP, EPathwayGOBPV} where Pathway Vis the set of pathways and GOBP Vis the set of GOBP terms included in PathwayGOBP E. Thus, the graph BipartiteGraphPathwayGOBP would consist of pathways that could be well represented by GOBP terms.The relationships between pathways and GOBP terms were represented as an undirected graph where edges between pathways and GOBP terms were evaluated based on Kappa statistics. We computed the Kappa similarity matrix of size BipartiteGraphPathwayGOBP to represent pathways as Boolean functions of biological processes, assuming that connected biological processes have direct impacts on the pathways. Since a pathway contains dynamics and dependencies among participating molecules, which are annotated by biological processes, we assume that different combinations of biological process states can affect the state of the pathway, which is either 'active' (binary state 1) or 'inactive' (binary state 0). For every pathway Pathwayi Vin the graph BipartiteGraphPathwayGOBP, let PathwayGOBPi Vbe the set of GOBP terms connected to that pathway and GOBP V= ∪PathwayGOBPi-V, we performed Boolean mapping such that the pathway Pathwayi Vcould be described as a Boolean algebra functions of its connected GOBP terms, Pathwayi V= f(PathwayGOBPiV).We further introduced Boolean rules to 1, GOBP2 GOBPand 3GOBP, and a pathway P, where 1 GOBPand 2 GOBPwere connected, 3 GOPBwas not connected with 1 GOBPand 2GOBP, and all GOBP terms were connected to pathway P. Then, the Boolean function for P could be written as, P V= (GOBP1 V∪ GOBP2) V∩ GOBP3V.The Boolean rules were derived from the relationships between GOBP terms connected to the pathway. If two GOBP terms were connected, then the Boolean relationship between these GOBP terms would be "OR." Such assumption arose from the fact two connected GOBP terms would share a significant amount of protein; thus, if a biological process was active, then its connected process must be simultaneously active as well. The relationship between two unconnected GOBP terms would be "AND." For example, considering a small network with 3 GOBP terms, GraphGOBPenriched from section 2.4 and the bipartite graph BipartiteGraphPathwayGOBP from section 2.5 to have a complete functional biological pathway-process network, where there were connections among GOBPs, and pathways communicated with each other through biological processes. As the complete network had many vertices and edges, we presented the MI pathway, h_amiPathway, from Biocarta, to illustrate the result. We retained important GOBP terms which were crossed by the shortest paths among other pathways to the MI pathway. Shortest paths were calculated using the un-weighted breadth-first search method. The extracted network allowed us to identify how the MI pathway could lead to other pathways and vice versa, initiating cardiac remodeling post-MI.We combined the GOBP graph GraphGOBPenriched was constructed, resulting in a network of 993 vertices and 4284 edges. GraphGOBPenriched had 16 connected sub-graphs having more than 1 vertex and 46 isolated vertices. The largest connected sub-graph consisted of 885 vertices and 4199 edges.Using DAVID Functional Annotation Tool, we obtained 993 enriched GOBP terms from the list of 613 MI-specific proteins. From the ancestor/offspring relationships, the graph It is interesting to note that GOBP terms with the highest degree, measuring the number of direct links incident on a vertex in a graph, were related to phosphorylation, phosphate, phosphorus, and kinase activity Table . Since pIn addition, biological processes involved with phosphorylation accounted for 4 GOBP terms while there were 5 kinase-activity-related GOBPs in Table BipartiteGraphPathwayGOBP with 544 vertices, containing 96 pathways 448 associated GOBPs, and 973 edges. These edges represented the most significantly enriched pairs of pathways and GOBP in the context of MI. This graph consisted of 8 sub-graphs, with the largest connected component having 76 pathways and 396 GOBP terms.At selected parameters, we retrieved 98 pathways, including 37 KEGG, 13 Reactome, and 48 Biocarta pathways using DAVID Functional Annotation Tool. Analysing statistical measures of inter-annotator agreement between 98 pathways and 993 GOBP terms, we established a graph REACT_6844: Signaling by TGF beta were involved with 56 GOBP terms, hsa04350: TGF-beta Signaling Pathway was associated with 14 GOBP terms, and the h_tgfbPathway was linked to 27 GOBP terms. Nonetheless, the common biological processes among these pathways included phosphorylation of SMAD proteins, serine/threonine kinase signaling pathway, epithelial-mesenchymal transition, and response to cholesterol and cell morphogenesis involved in differentiation , hsa05410: Hypertrophic Cardiomyopathy (HCM), and hsa05414: Dilated Cardiomyopathy (DCM). These analyses provide additional examples to demonstrate how representing pathways in terms of biological processes helped us to quickly understand the characteristics of such conditions under specific circumstances  and h_fibrinolysisPathway (characterized by 22 GOBP terms). The center of this sub-network is the MI pathway from Biocarta, h_amiPathway. Altogether, 3 pathways were represented by 32 out of 34 GOBP terms in this sub-network, and there were 8 common GOBP terms, including coagulation, regulation of coagulation, negative regulation of coagulation, blood coagulation, regulation of blood coagulation, negative regulation of blood coagulation, homeostasis and regulation of body fluid levels  showed that by deleting plasminogen, a classical fibrinolytic factor that controls hematopoietic stress response, in mice, hematopoietic stem cells were prevented from entering the cell cycle and undergoing multilineage differentiation after myelosuppression, leading to the death of the mice [The complete network of pathways and GOBP contains a huge amount of information although it could be overwhelming. We extracted the MI network and only retained the backbone to explore additional features that might not have been covered. Figure the mice . In othethe mice . Incidenthe mice . Thus, iIn this study, we established a network by integrating GO biological processes and pathways from BioCarta, KEGG, and REACTOME enriched for MI-specific proteins using statistical measures and hierarchical structures. We examined the similarities between pathways and biological processes, and derived Boolean models of pathways in terms of standardized vocabulary with GOBP terms. This network can be used to explore critical routes that connect pathways and biological processes to the development of diseases or conditions. To demonstrate a functional interaction network, we started from the proteins in an MI-specific protein-protein interaction network we had previously constructed, acquired the enriched GO biological processes and pathways, constructed the GOBP graph and the functional pathway-process network, and determined the logical circuitry representing the involvement of GOBPs in pathways. The approach could be used with any set of genes or proteins, specific to any conditions or diseases, to develop additional features and visualizations.h_amiPathway. Because MI was the major theme of this study, this extracted network allowed us to quickly visualize the connection between pathways before and after MI and their involvement in the changes in the post-MI myocardium.This study presented three important results. First, we established a MI-specific functional biological pathway-process network, with demonstrated sub-networks shown in Figures Our results illustrated that using the functional biological pathway-process network is a promising method to identify biological properties of pathways under specific conditions. Pathways having similar descriptions encompassed both similar and diverse biological processes, indicating variation in their ability to share similar functional characteristics. The coverages of biological pathways can be increased with the incorporation of more biological processes and protein members, promoting more comprehensive pathways. As we discover and understand more about genes and proteins, the network helps to expand the participating genes or proteins in the pathways through the introduction of related genes in the GOBP. Pathways will be more comprehensive, leading to better knowledge of diseases. However, functional groups of GOBP terms based on hierarchical structures might need to be further evaluated for coherence. Moreover, GOBP functional groups might not have the same amount of contribution to the corresponding pathways; probabilistic Boolean models would allow more robustness in the face of uncertainty. In conclusion, we report here the establishment of the network of pathways and biological processes that can be used as a foundation to identify biological properties of pathways, providing interaction and visualization of biological systems at pathway level.AVRC: Arrhythmogenic Right Ventricular CardiomyopathyDCM: Dilated CardiomyopathyGO: Gene OntologyGOBP: Gene Ontology Biological ProcessHCM: Hypertrophic CardiomyopathyKEGG: Kyoto Encyclopaedia of Genes and GenomesMI: Myocardial InfarctionMIPIN: Myocardial Infarction-Specific Protein-Protein Interaction NetworkPANTHER: Protein Analysis Through Evolutionary RelationshipsSTAT: Signal Transducer and Activator of TranscriptionTGF-beta: Transforming Growth Factor BetaThe authors declare that they have no competing interests.Conceived and designed the experiments: NTN, MLL, and YFJ. Analyzed or reviewed the data: NTN, MLL, and YFJ. Contributed reagents/materials/analysis tools: NTN and YFJ. Wrote or edited the paper: NTN, MLL, and YFJ.Pathways and GOBPs of MI network. This file contains names of pathways and GOBPs in the extracted network of MI with labels as displayed in Figure Click here for file


The goal of this study was to identify cancer-associated differentially expressed genes (DEGs), analyze their biological functions and investigate the mechanism(s) of cancer occurrence and development, which may provide a theoretical foundation for bladder cancer (BCa) therapy. We downloaded the mRNA expression profiling dataset GSE13507 from the Gene Expression Omnibus database; the dataset includes 165 BCa and 68 control samples. T-tests were used to identify DEGs. To further study the biological functions of the identified DEGs, we performed a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Next, we built a network of potentially interacting pathways to study the synergistic relationships among DEGs. A total of 12,105 genes were identified as DEGs, of which 5,239 were upregulated and 6,866 were downregulated in BCa. The DEGs encoding activator protein 1 (AP-1), nuclear factor of activated T-cells (NFAT) proteins, nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) and interleukin (IL)-10 were revealed to participate in the significantly enriched immune pathways that were downregulated in BCa. KEGG enrichment analysis revealed 7 significantly upregulated and 47 significantly downregulated pathways enriched among the DEGs. We found a crosstalk interaction among a total of 44 pathways in the network of BCa-affected pathways. In conclusion, our results show that BCa involves dysfunctions in multiple systems. Our study is expected to pave ways for immune and inflammatory research and provide molecular insights for cancer therapy. Bladder cancer (BCa) is a heterogeneous disease with a variable disease history. At present, BCa is the ninth most common tumor worldwide , rankingBCa has a multifactorial etiology. Numerous studies have identified risk factors for the development of BCa and the affected cellular processes, including low-penetrance gene polymorphisms, smoking , occupatThe detection of BCa remains difficult due to the lack of specific tumor markers and the emergence of new symptoms. There is no standard therapy for patients with BCa; treatment options include surgery, chemo-, biological and radiation therapy. In addition, these methods show currently limited effectiveness, which is further reduced by the high recurrence of BCa and the high mortality deriving from increased rates of progression of the disease . There iEarlier studies have suggested that different mechanisms have evolved to respond to specific phenotype alterations in the molecular and cellular pathways involved in BCa. A number of genetic variations in major carcinogenesis-related pathways can induce BCa. For example, in healthy cells, the cell cycle is controlled by tightly linking the p53 and retinoblastoma (RB) pathways that influence regulation of apoptosis, signal transduction and gene expression. During cancer progression and metastasis, the mutated p53 protein exerts profound changes in regulatory networks involved in cell adhesion and angiogenesis, and becomes resistant to degradation in BCa of pT2–pT4 . HoweverIn the present study, we identified genes the expression of which is significantly changed between healthy and cancer cells and discuss the biological functions of these genes and the related pathways. In addition, we built a network of BCa-affected pathways in order to explore the mechanisms underlying the pathogenesis, occurrence and development of BCa. Our results may provide valuable molecular insights for BCa treatment.The mRNA expression profiling dataset for BCa was downloaded from the Gene Expression Omnibus (GEO) database (accession no. GSE13507) at the National Center of Biotechnology Information. The dataset comprises a total of 256 samples, including 10 healthy bladder mucosae, 58 normal-looking bladder mucosae surrounding cancer, 165 primary bladder cancer and 23 recurrent non-muscle invasive tumor tissues. Gene expression was previously studied in these samples on an Illumina Human-6 v2.0 expression beadchip platform ,12.The raw expression data were downloaded as a Series Matrix file. This expression matrix contained 43,148 probes. Among these, a total of 24,995 probes uniquely mapped to 24,340 genes. For different probes mapping to the same gene, we the mean probe value was calculated for the gene expression value. An expression matrix  was thus obtained, with each row representing gene expression value, and each column representing the sample. From the 256 samples, we selected 233, corresponding to 165 primary BCa and 68 control samples .A statistical t-test was used to identify DEGs between BCa and control samples and to calculate the associated p-values. Multiple testing correction on these p-values was performed with the Benjamini and Hochberg method , and proKEGG is a knowledge database for the systematic analysis of gene functions, linking genomic information with high-order functional information. To identify significantly enriched KEGG pathways among the DEGs, we first downloaded from the database 232 KEGG pathways on April 26th, 2011 . We nextIn this formula, N is the total number of gene samples involved in KEGG pathways; n is the number of differentially expressed gene samples; m is the number of gene samples in one pathway; and k is the frequency of overlapping genes in all pathways. FDR<0.01 was set as the threshold.In order to explore potential synergistic relationships between BCa-affected pathways, we constructed the pathway network in which nodes represent pathways and edges represent nonrandom synergistic relationships . The network was built using Cytoscape . The croIn this formula, #intersection  represents the overlapping number of genes between pathway A and B, and #union  represents the sum of genes in pathway A and B. All pathway pairs were grouped together to construct the network. Pathways that showed Ji≥0.01 and shared at least one DEG were selected.Based on t-tests and a threshold FDR from multiple testing correction <0.05, we identified 12,105 DEGs, 5,239 upregulated and 6,866 downregulated.In order to gain further insights into the biological functions of the identified DEGs, we used a hypergeometric distribution test to identify the significantly enriched pathways related to BCa. Using a threshold FDR<0.05, we found that upregulated DGEs are significantly enriched for 7 pathways , while dIn order to systematically analyze the DEG functions, we used a Jaccard model to construct and identify the interactions between the BCa-affected pathways. We selected the pathways that showed Ji ≥ 0.01 and shared at least one DGE, and thus identified, from 172 pathway pairs, a total of 44 pathways that were considered good candidates for a crosstalk interaction . Among tTo investigate the mechanism underlying the BCa disorder, we focused on the immune- and inflammation-related pathways identified within the network of interacting pathways. These included chemokine signaling, complement and coagulation cascades, antigen processing and presentation pathways, natural killer cell-mediated cytotoxicity, T- and B-cell receptor signaling pathways, and the intestinal immune network for IgA production. Furthermore, in order to assess the role of immune responses in the development of cancer, we focused on the B- and T-cell receptor signaling pathways. We found that the B-cell receptor signaling pathway is downregulated in BCa, involving 36 DGEs . The T-cBCa, which is pathologically diagnosed as transitional or urothelial carcinoma, is the only known solid tumor besides melanoma. Detection and treatment of BCa remain a challenge. Therefore, there is an urgent need to explore the mechanisms involved in BCa and develop an effective preventive strategy for this disorder. In the present study, we first identified DEGs by comparing mRNA expression profiling data (downlooaded from the GEO database) between cancer and control samples and then applied a hypergeometric distribution test to identify the significantly enriched pathways these DEGs are involved in. Furthermore, we constructed a network of the relevant pathways and assessed their potential interactions.The KEGG pathway enrichment analysis showed that the upregulated DEGs are related to 7 pathways. Among these, the most significantly enriched is the cell-cycle regulation pathway, which is a signal transduction cascade triggered in response to the detection of DNA damage , and whiThe downregulated DEGs were found as involved in 47 significantly enriched pathways, mainly including cell transfer-related pathways, such as: cell adhesion molecules and focal adhesion; cell growth-related pathways, such as WNT, transforming growth factor-β (TGF-β), mitogen-activated protein kinase (MAPK) and calcium signaling pathways; and cancer-related immune and inflammation pathways, such as the T-/B- cell receptor and the chemokine signaling pathways. Epidemiological studies have estimated that underlying infections and inflammatory responses are linked to 15–20% of cancer-related deaths worldwide . The intTo explore the potentially synergistic interactions among the BCa-affected pathways, we constructed a network based on significantly enriched pathways and Ji calculations. The crosstalk interactions revealed in this network indicated that cancer incidence relates to alterations in multiple processes, including environmental and genetic information processing, metabolism, organismal systems, and certain human diseases. These processes may synergistically contribute to the regulation and progression of BCa.in vitro  are recognized by infiltrating lymphocytes such as natural killer T-, natural killer or γδ T-cells, which are then stimulated to produce interferon-γ (IFN-γ) ,26. The in vitro .Activator protein 1 (AP-1), involved in cellular proliferation, transformation and death , is a diOverall, the present study presented a comprehensive bioinformatic analysis of genes and pathways that may be involved in the progression of BCa. Our results on immune- and inflammation-related pathways affected by BCa may prove helpful in the development of a new strategy to treat BCa in combination with medical therapy. Considering the increasing public availability of genomic data, we argue that our approach will constitute an attractive strategy for identifying disease-related pathways and interactions in numerous future studies. However, further experimental studies are needed to reveal the molecular mechanisms underlying BCa-associated processes.


A systems-based investigation of the effect of perturbations on metabolic machinery is crucial to elucidate the mechanism behind perturbations. One way to investigate the perturbation-induced changes within the cell metabolism is to focus on pathway-level effects. In this study, three different perturbation types  are analyzed to compute a list of reporter pathways, metabolic pathways which are significantly affected from a perturbation. The most common omics data type, transcriptome, is used as an input to the bioinformatic analysis. The pathways are scored by two alternative approaches: by averaging the changes in the expression levels of the genes controlling the associated reactions (reaction-centric), and by averaging the changes in the associated metabolites which were scored based on the associated genes (metabolite-centric). The analysis reveals the superiority of the novel metabolite-centric approach over the commonly used reaction-centric approach since it is based on metabolites which better represent the cross-talk among different pathways, enabling a more global and realistic cataloguing of network-wide perturbation effects. Pathways are functional units which perform certain cellular tasks. A practical way to grasp the effect of a perturbation on molecular mechanisms, whether it is environmental, genetic or disease-based, is to check how it affects hundreds of cellular pathways. The availability of omics data facilitates the documentation of the perturbation-induced changes in the cell via the pathways constituting its cellular networks. Metabolic pathways are one of the most appealing pathway types in this sense since metabolism is the engine that runs the cellular factory. Several computational methods emerged, for example, to integrate transcriptome data and metabolic networks to catalogue how the pathways respond to a perturbation123The most common pathway-oriented approach to bridge mRNA-level changes and observed phenotypes is pathway enrichment, which statistically analyze the enrichment of associated pathways for a set of genes found to be significantly affected from the perturbation, without considering the magnitudes of individual changes observed for the gene transcripts7m) is shown to outperform the reaction-centric scoring of pathways (RPAr) in terms of reporting perturbation-associated underlying mechanisms.Here I present a new approach to catalogue metabolic pathway-level perturbation effects based on transcriptome data. Approaches in the literature for the determination of perturbed metabolic pathways are reaction-centric, i.e. pathways are scored based on the expression levels of the genes controlling the associated reactions. A metabolite-centric approach which takes into account all the reactions which consume or produce the metabolites of the pathway is needed to better represent the effects of a perturbation on network-level. A novel metabolite-centric scoring algorithm is employed to integrate organism-specific metabolic networks with the statistical changes on the genes which control the reactions functioning in the network, and it is compared to the more straightforward reaction-centric algorithm. The algorithm builds on the reporter metabolite approach commonly used in the analysis of transcriptome-associated regulations in metabolic networksS. cerevisiae. A deletion was introduced to this gene to perturb the cofactor balance within the yeast cellsGDH1 is known to encode NADPH-dependent conversion of 2-ketoglutarate to glutamate in the yeast r and RPAm, and also by PADOG. In addition to the NADPH imbalance, the deletion also results in an NADH imbalance in the cell since the deleted reaction is replaced by an NADH-dependent route by the cell. Therefore, pathways linked to NADPH and NADH are expected to be perturbed. The RPAm results revealed 35 significant pathways affected from the perturbation , one of the major related pathways directly related to the source of perturbation . Other related pathways such as glycolysis, respiration and TCA cycle were predicted to be not affected by PADOG from the oxygen availability.Several fatty-acid degradation pathways appear in 232525Finally the two alternative approaches were applied to Alzheimer’s disease datasetm include a number of lipid related pathways: triacylglycerol, CDB-diacylglycerol, phosphatidylcholine, 3-phosphoinositide, sphingosine and myo-inositol metabolisms. Significantly less phosphoinositides and significantly elevated myo-inositol levels were reported in Alzheimer’s disease31m, but RPAr fails to capture the perturbation of this pathway in Alzheimer’s disease. It was reported that a number of mitochondrial enzymes that function in TCA cycle exhibits changes during the disease3334m. It is known that melatonin levels are reduced in Alzheimer’s disease patients, and melatonin can be used as a biomarker for the early stages of the disease36m analysis. Another pathway identified by RPAm, heparan sulphate biosynthesis, is in agreement with the literature since heparan sulphate proteoglycans are known to be linked to the pathogenesis of the disease3940m, in accordance with the role of retinoids in late onset alzheimer’s disease43Results for RPAr gives only two pathways with p-value lower than 0.01 whereas it is 20 pathways for RPAm. PADOG, on the other hand, did not give any significantly perturbed pathway for the selected significance cut-off of 0.01. However, it identified glutathione redox reactions and reactive oxygen species degradation in the top five significant pathways . These pathways did not appear in the reporter pathway analyses. It is known that reactive oxygen species are indicative of oxidative stress in the cell, and neurodegenerative diseases are associated with oxidative stress46In general, RPAm analysis such that significantly upregulated and downregulated pathways are also identified. Such an analysis can be combined with the identified significant pathways to reveal the capacity of RPAm to predict the directionality of regulation. Due to the nature of the algorithm, in the up (/down) regulation analysis only up (/down) regulated genes linked to a metabolite are used to calculate a reporter metabolite scoreIn the reporter pathway analysis, the up/down regulation of the associated gene transcripts are not distinguished. I have modified RPAm results for only up-regulated genes and for only down-regulated genes reveal that a high majority of the identified pathways for the genetic perturbation . A more stringent cut-off of 0.005 can still capture pathways which cannot be captured by RPAr for a significance level of 0.01. This includes myo-inositol, retinol and TCA cycle pathways for Alzheimer’s disease and pentose-phosphate, 2-ketoglutarate dehydrogenase complex and glycolysis pathways for the GDH1-based genetic perturbation. One should note that RPAm and RPAr results have different rankings for different pathways. That is, what RPAm does is more than merely decreasing the cut-off. For example, mevalonate pathway, ranked as 17th most significant pathway for Alzheimer’s disease based on RPAm, ranks as 41th pathway for RPAr. One reason behind the change in rankings in addition to the inclusion of inter-pathway reactions is the implicit assignment of an increased weight on branch points by RPAm. The score of a gene is accounted multiple times in the metabolite-centered scoring especially if the metabolites of the pathway are on branch points. Although this may seem a disadvantage of the approach at first sight, the increased weight is in agreement with the fact that the branch points act as important regulatory spots in metabolism49m better reflects this phenomenon.There are, on the other hand, a few issues to be considered for the metabolite-centric scoring approach. The tendency of the approach to report noticeably more significant pathways compared to RPASaccharomyces cerevisiae were downloaded from BioCyc databaseMetabolic networks for human and the yeast Three different transcriptomic datasets were analyzed: the genetic perturbation in the yeast via the deletion of NADPH-dependent GDH1 gene grown in anaerobic chemostat culturesReporter Features tool under the online tools were used. The three input files, p-value data file, interaction network file, and network nodes-data association file, were created in MATLAB in the requested format for each specific simulation. In the metabolite-centric reporter pathway analysis (RPAm), a metabolite-score must be computed first. This scoring method, termed reporter metabolite analysis, was used in a number of research covering microorganisms7535448ttest2 function. The metabolic networks downloaded from BioCyc include the information on reaction-gene association. In the analyses, the genes are linked to the corresponding controlled reactions at first as follows: when a reaction is associated with multiple genes, the one with the minimum p-value was consideredReporter pathway analyses were performed using the online BIOMET Toolbox serveri is the Z-score of gene i obtained from the corresponding p-value by using the inverse normal cumulative distribution, and k is the number of neighbor genes to the metabolite. The averaging of the mRNA-level changes in the form of Z-scores shows the average effect of perturbation on metabolites7μk) and standard deviation (σk) of the aggregated Z scores of many sets of randomly selected k genes from the metabolic network.Here, Zm) follows a similar equation to Metabolites with significant Z-scores are called reporter metabolites, hence the analysis is termed reporter metabolite analysisn is the number of metabolites associated with the pathway. The output is a set of significantly affected metabolic pathways. The alternative reaction-centric reporter pathway analysis (RPAr) is directly based on the involved reactions in a pathway, which are linked to transcriptome data via the genes controlling the reactions. Pathways are scored via the p-values (Z-scores) of the genes associated with each involved reaction in this case, following Here, i is the score of gene i, and p is the number of reactions associated with the pathway. Note that both m. The metabolite-centric approach allows the elucidation of the global effect of the perturbation on cellular parts since this scoring considers the fact that a metabolite is consumed or produced by a number of reactions which are traditionally listed under different pathways. Such an analysis bridges different cellular processes and also reflects cross-talks between pathways.Here, Zm and RPAr analyses. PADOG was chosen since it was shown to be one of the best performers among more than 15 alternative gene-set based algorithmsPADOGHow to cite this article: Çakır, T. Reporter pathway analysis from transcriptome data: Metabolite-centric versus Reaction-centric approach. Sci. Rep.5, 14563; doi: 10.1038/srep14563 (2015).