The next generation sequencing technologies substantially increased the throughput of microbial genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and computational methods are used. Integration of information from different sources is a powerful approach to enhance such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially depends on this annotation but it is hampered by the current lack of suitable information integration and exploration systems for microbial genomes.Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments. We developed a data warehouse system (INDIGO) that enables the integration of annotations for exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information from http://www.cbrc.kaust.edu.sa/indigo.We developed a data warehouse system, INDIGO, which enables comprehensive integration of information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline. The INDIGO web server is freely available at  The Next Generation Sequencing (NGS) technologies substantially increased the throughput of genome sequencing -3. AnnotIntegrated Data Warehouse of Microbial Genomes), a data warehouse for microbial genomes we developed, which allows integration of annotations for exploration and analysis of microbial genomes. Currently, INDIGO contains information from three species: two bacterial species, Salinisphaera shabanensis [Haloplasma contractile [Halorhabdus tiamatea [Here, we introduce INDIGO . Automation of data warehouse development in a high throughput manner that minimizes the intermediate steps for processing of annotation results. Provision to public annotations of microbial genomes being sequenced at KAUST from studies of the Red Sea environment. The number of genomes will gradually increase.www.ensemblgenomes.org) and MicroScope [Generally, newly sequenced microbial genomes are submitted to archival databases such as GenBank  or EMBL croScope . These pWe developed INDIGO, a data warehouse for microbial genomes using the InterMine framework Smith et al.  that allwww.454.com) with scaffolding option turned on in addition to using SOAPdenovo [AFNU00000000, AFNV00000000 and AFNT00000000 for HLPCO, SSPSH and HLRTI strains, respectively.In our case, we reassembled previously reported -13, threAPdenovo  and VelvAPdenovo . FurtherAPdenovo  to obtaiAPdenovo , GapFillAPdenovo  and GapCAPdenovo . ApplyinAPdenovo  is now rIn our study, we performed genome annotation through a series of steps described in a workflow depicted in Recently, Triplet et al.  thorough1Development of an automatic high throughput data warehousing pipeline to process customized annotation and their validation from newly sequenced microbial genomes. As an example, we annotated and processed annotations from three extremophile genomes from Red Sea and added to INDIGO for public data mining. 2Addition of Genome Browser functionality.3Addition of BLAST interface to allow comparison of external user specified sequence data to INDIGO dataset and integration of BLAST results to either explore hit genes annotations in the INDIGO data warehouse or the auxiliary genome browser.4We made available special hyperlinks for KEGG assigned INDIGO pathway gene sets to be shown on publication quality pathway diagrams at KEGG website.5and more importantly, we made available Automatic Annotation of Microbial Genomes (AAMG) pipeline for public use through the INDIGO server. We compared INDIGO system to InterMine and few other microbial genome data warehouses such as Integrated Microbial Genomes (IMG) , MicrobeEscherichia coli (E. coli) K12 strain and E. coli TY2482 strain, were recently considered in benchmarking two different annotation pipelines [Candidatus Carsonella ruddii DC[To assess the quality and volume of annotations produced using our AAMG pipeline, we compare AAMG annotation results based on three publicly available datasets. Two of these datasets, namely ipelines . The thiruddii DC.E. coli in Germany triggered the sequencing of E. coli O104 [E. coli TY2482 is compared with annotations available from BG7 [http://www.broadinstitute.org/annotation/genome/Ecoli_O104_H4/assets/Ecoli_TY_2482_BGI.gbk. Results depicted in Recent outbreak of oli O104 , the caufrom BG7 . BG7 pipE. coli O104 (TY2482) annotation results to RAST-based annotations considering Broad Institute annotation as a gold standard. It was reported for E. coli TY2482 assembly version 4 [E. coli TY2482  showing about the same number of genes predicted as BG7, but with higher numbers of functional  products and smaller number of orphan  genes when compared to the Broad Institute reference annotations. BG7 compared its ersion 4  that BG7E. coli O104 (TY2482), we also compared our results in comparison to existing annotations in NCBI for E. coli K12 and another much smaller genome, Candidatus Carsonella ruddii DC. Results show that our annotation pipeline is able to minimize hypothetical genes names through scanning multiple full protein and domain databases. Our gene calls are also very close to the existing annotations. In addition to http://www.cbrc.kaust.edu.sa/indigo_data/. Data files and results are visualized using interactive graphs based on modified Krona package [Our annotations for these three genomes are available as a material at  package .InterMine provides a core genomic data model defined with several genome entities, their attributes, syntax and relationships. We extend this core genomic model to fit our needs so as to cater to all types of annotations we receive from our annotation process. This includes data types and relationships between entities to be stored, such as attributes for organism, contigs, genes, CDS, protein domains, pathways and cross references. An example of genomic data model is provided in the materials at the website.InterMine provides a built-in setup for complex data integration, post-processing and web-application development. Data integration is heavily dependent on genomic model defined with data types and relationships between entities to be stored. Once a genomic model is defined, one can perform a check for the annotation that is to be loaded into the database. Our system first validates the annotation in reference to the defined genomic model using InterMine’s Model and Document Perl Modules. It then prepares an xml schema filled with data that is ready to be loaded to the backend Postgres database. InterMine loads data into the database using pre-defined ‘sources’ for different types of data packed in different formats. For example, to load genes data packed in the gff format, a Java-based data converter is available, but it assumes specific tags and fields. For customized data loading we developed prokaryotic-annots-xml, available as Supplementary material here, which allows loading of our validated annotations packed in xml format. InterMine’s build-db setup reads the generated annotation using prokaryotic-annots-xml source and loads the data by defining and populating different annotation tables automatically. Data integration in the InterMine’s framework is a crucial step. It integrates data from sources provided in the project xml file and performs multiple checks . We only provide database identifiers in the annotation xml, for example, for GO or Interpro protein domains (IprD), and InterMine system integrates corresponding detailed annotations from complete GO or IprD source files defined in the project xml. There are several built-in post-processing steps available in the InterMine framework such as create-search-index, transfer-sequences, etc., that allow for quick indexing of the data. For INDIGO, in order to have all the functionality available, we run all post-processing steps. http://tomcat.apache.org). Web-application templates are available in the InterMine framework and we customized them to fit our requirements. For example, report pages for genome features such as genes, proteins, domains, and pathways are customized according to the data available including hyperlinks to external databases. One of the interesting external links allows for displaying KEGG pathway diagrams showing presence of the KEGG Ortholog ids to which the explored genome is mapped. Such a display shows which elements of the reference pathways are present or missing from the genome being examined. InterMine allows packaging of the web-application as a Web Application Archive or WAR-file that is then deployed on the Tomcat Apache server  or limit (constrain option) for different feature types and their attributes. ‘Regions’ tab provides access to all features present in a given genomic coordinate range. ‘Data’ tab provides general information about the genomic data sets included in the data warehouse, e.g. genome assembly statistics, counts and links to contigs, ORF sequences, archaeal/bacterial genome completeness statistics based on counts of archaeal/bacterial core COGs , minpathWhen analyzing a new genome, majority of questions can be summarized in ‘What’, ‘Where’ and ‘How’ context. For example, to see whether a gene, protein, protein domain, GO term or a pathway of interest can be found in INDIGO, a search mechanism can help. For ‘Where’ context questions, the ‘Region’ search option in INDIGO can list all the genomic features in a given range of genomic coordinates. For complex questions of the type e.g. ‘What is a list of genes involved in pathway X and what are their protein domains and associated GO terms’ more control on what is being searched is needed and here it is provided through Query Builder. More details on features of INDIGO, such as a quick and easy keyword search, query builder search, analysis of genomic feature  lists, genomic region search, and enrichment analysis for GO, protein domains and pathways, are shown in few examples in what follows.In the INDIGO system, a keyword search, as well as a more extensive query builder search option, are provided. The keyword search option provides a very simple interface to the underlying annotation data. It is very fast since all the keywords in the database are indexed. Query builder, however, provides more control over annotation classes and attributes to be searched, constrained or viewed. It is possible to combine several queries through constraint logic. In order to find out characteristics of a particular genomic region, one can use region search. When coordinates for the specific genomic region are provided, the region search allows for selection of additional upstream and downstream regions, as well as features like gene or intergenic region, etc. . ResultsINDIGO makes use of different types of lists. For example, a list could be the list of genes/proteins, or protein domains, etc. Results from keyword search or query builder, can be saved as a list. A click on the saved list link automatically shows GO, protein domain and pathway enrichment, as shown in an example in https://intermine.readthedocs.org/en/1.1/embedding/list-widgets/enrichment-widgets/). The user is also able to save all enriched genes, make sub-lists, view individual gene report pages, or export results. Enrichment analysis provided for a list includes p-values based on hypergeometric distribution with several multiple testing correction options  has in its focus areas the biodiversity and microorganisms of the Red Sea. INDIGO is populated with information from three extremophiles from the Red Sea, whose genomes have been previously reported by our team -13. The The Red Sea is one of the warmest, most saline and most nutrient-poor oceanic water bodies in the world ,50. It aThree extremophilic microbes, previously isolated from the deep-sea anoxic brine lakes, were selected as part of a genome-sequencing project due to their phylogenetic position, peculiar features and unique biotope. Analysis of their draft genomes provides us with a first glimpse on some of their unusual characteristics and the ways they cope with living in such a harsh environment -13. Salinisphaera shabanensis was isolated from the brine-seawater interface of Shaban Deep [Gammaproteobacteria, and displayed a remarkable physiological versatility. Indeed, Salinisphaera shabanensis had quite broad growth ranges for oxygen, temperature, NaCl, pressure, and, to a smaller degree, pH [ban Deep . It reprgree, pH .Haloplasma contractile was isolated from the brine-sediment interface of Shaban Deep. Phylogenetically it represented a novel lineage within the Bacteria with branching position between the Firmicutes and Tenericutes (Mollicutes), with no close relatives [Haloplasma is its unusual morphology and unique cellular contractility cycle.elatives . The mosHalorhabdus tiamatea was isolated from the brine-sediment interface of the Shaban Deep [in situ hybridization coupled with the “optical tweezers” technique [ban Deep  using flechnique ,56. It wIn INDIGO provides easy and quick access to genomic annotations of microbial species at the levels of chromosomes, genes, and proteins, as well as to the associated GO and pathways. The top 10 pathways based on the number of genes assigned to each of these extremophiles as found by INDIGO are shown in Haloplasma contractile include its unusual morphology and contraction cycle and these provided clear targets for genomic-based exploration. While some aspects of the genetic control of cellular morphology remain unclear, the dcw gene cluster seems to play a central role. Gene context is particularly relevant, as morphology is impacted by presence or absence of specific genes, together with relative position and distance within this gene cluster [murD - one of the central genes of the dcw gene cluster. Furthermore, we analyzed the genomic context of murD (upstream and downstream regions) and successfully demonstrated multiple gene insertions and disruption of the murD-ftsW-murGgene order, see H. contractile as they have been previously implicated in all non-rod morphologies currently known [The most remarkable features of  cluster ,58.Usinly known .Aromatic compounds are abundant, widely distributed and known to constitute some of the most prevalent and persistent pollutants in the environment . Some miSalinisphaera shabanensis. Such valuable information obtained through the simple use of INDIGO will aid in search for target missing genes and/or design downstream laboratorial experiments to confirm the functionality of this pathway, and explore possible future applications. Based on previous detection of a variety of complex hydrocarbons in Shaban Deep , we lookThe new data warehouse system, INDIGO, enables users to combine information from different sources of annotation for further specific or general analysis. This data warehouse of Red Sea microorganisms currently contains information about three genomes . Considering the unique biodiversity present in the Red Sea, KAUST has undertaken a large sequencing effort starting from metagenomes to cultured and uncultured single cell amplified genomes. The plethora of sequencing data produced requires a high throughput assembly, annotation and data warehousing pipelines. This work shows the basic framework through which these pipelines can be used in a high throughput manner to properly warehouse the increasing amount of data for targeted studies. Additional genomes will include both, genomes of pelagic bacteria and archaea, as well as more extremophiles from the brine pools of the Red Sea.


BioCyc.org website have made extensive use of Groups, and an informal survey of Groups users suggests that Groups has achieved the goal of allowing biologists themselves to perform some data manipulations that previously would have required the assistance of a programmer.Knowledge spreadsheets (KSs) are a visual tool for interactive data analysis and exploration. They differ from traditional spreadsheets in that rather than being oriented toward numeric data, they work with symbolic knowledge representation structures and provide operations that take into account the semantics of the application domain. ‘Groups’ is an implementation of KSs within the Pathway Tools system. Groups allows Pathway Tools users to define a group of objects (e.g. groups of genes or metabolites) from a Pathway/Genome Database. Groups can be transformed (e.g. by transforming a metabolite group to the group of pathways in which those metabolites are substrates); combined through set operations; analysed ; and visualized (e.g. by painting onto a metabolic map diagram). Users of the Pathway Tools-based Database URL: BioCyc.org. A long-standing problem in computing is that of providing non-programmers with intuitive, yet powerful tools for manipulating and analysing sets of entities. For example, a number of bioinformatics database websites provide users with powerful tools for composing database queries, but once a user obtains the query results, they are largely on their own. What if a user wants to store the query results for future reference, or combine them with other query results, or transform the results, or share them with a colleague? Sets of entities of interest arise in other contexts for life scientists, such as the entities that are identified as significantly perturbed in a high-throughput experiment , or a set of genes of interest that emerge from an experimental investigation.We observe that spreadsheets have become a dominant form of end-user programming and data analysis for scientists. Although traditional spreadsheets provide a compelling interaction model, and are excellent tools for the manipulation of the tables of numbers that are typical of accounting and data analysis problems, they are less easily used with the complex symbolic computations typical of symbolic biocomputing. For example, they cannot perform semantic transformations such as converting a gene list to the list of pathways the genes act in.We coined the term knowledge spreadsheet (KS) to describe spreadsheets that are characterized by their ability to manipulate semantic objects and relationships instead of just numbers and strings. Both traditional spreadsheets and KSs represent data in tabular structures, but in a KS the contents of a cell will typically be an object from a knowledge base (KB) .Those features of Groups that respondents mentioned as being the most useful included its transformation and set operations, its over-representation analysis, its ability to share and download groups for further analysis, the ability to conduct metabolic pathway comparisons and the ability to view properties of different genes ‘all at once’.Although learning to use Groups was not trivial for some users, others found the online learning resources to be effective. For example, whereas one user reported: ‘The interface is not that intuitive. It took a bit to find out how to upload data’, another reported: ‘I watched the Groups tutorial video on the SRI website, and I was good to go.’ Possibly different sorts of training materials should be offered for different kinds of users.Respondents reported many areas where Groups could be improved. Most of these were simple suggestions or bug reports that, although well worth implementation or repair, do not merit discussion here. One user requested the ability to create groups that cross organisms. That would permit users to perform what would amount to between-species metabolic or genomic ‘joins’. This would be an interesting computation, and raises the question of what common keys make sense between organisms; such keys would be likely to vary from case to case, but homologous genes at least, or those whose product proteins have the same biochemical function, would make sensible join keys.BioCyc.org by 748 users, and 54 users created >10 groups. An informal survey of Groups users suggests that Groups has succeeded in its goal of allowing biologists to perform analyses that previously would have required the assistance of a programmer.Groups is an implementation of knowledge spreadsheets for Pathway Tools. Groups operates over objects and relationships from Pathway/Genome Databases. The Pathway Tools web-mode implementation of Groups is highly interactive, and provides a variety of operations for creating, modifying, exporting and sharing groups among colleagues. Groups transformations enable users to convert groups from one type of object to another, such as converting a pathway list to a list of all genes or all metabolites within the pathway. Enrichment analysis on Groups detects statistically over-represented sets of entities within a group. At least one group was created at Groups is available as part of the Pathway Tools software, and is subject to the licensing terms of Pathway Tools: the software is freely available to academic users including source code, and is available for a fee to commercial users. The software is downloadable from . The Gro


The current version 46 describes 7088 human proteins (34% of the predicted human proteome), participating in 6744 reactions based on data extracted from 15 107 research publications with PubMed links. The Reactome Web site and analysis tool set have been completely redesigned to increase speed, flexibility and user friendliness. The data model has been extended to support annotation of disease processes due to infectious agents and to mutation.Reactome ( At the cellular level, life is a network of molecular reactions that can be organized into higher order interconnected pathways. Molecules are synthesized, degraded, transported from one location to another and assembled into complexes and higher order structures with other molecules. Intensive studies of cellular signaling, motility, vesicular trafficking and other aspects of cell biology, coupled with the development of comprehensive catalogs of human genes and their protein products, have enabled the description of many cellular processes in the same molecular detail that has been a standard for metabolic processes for a generation. By annotating all of these processes in a single, consistent reaction-pathway format, the Reactome Knowledgebase systematically links human proteins to their molecular functions, providing a resource that functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data from gene expression pattern surveys or somatic mutation catalogues from tumor cells e.g. .Since its inception 10 years ago, Reactome has grown to include (version 46–September 2013) annotations for 7088 of the 20 774 protein-coding genes in the current Ensembl human genome assembly (34% coverage), 15 107 literature references and 1421 small molecules organized into 6744 reactions collected in 1481pathways. Notable recent additions include extensive annotations of phospholipid and eicosanoid metabolism, protein glycosylation and SCF-KIT, IGF1R, NOTCH and HIPPO signaling, as well as annotations of regulatory processes mediated by non-coding RNAs.Here, we will focus on three new features of Reactome: the development of a strategy to annotate the disease counterparts of normal human processes, the deployment of a redesigned Web site and the extension of tools for data analysis.et al.   host_celTo denote molecular properties of a protein modified by a somatic or germline mutation in the gene that encodes it, as opposed to post translational modification, we created a new class. This class of genetic modifications has subclasses to accommodate substitution of a canonical residue by a different one, the insertion or deletion of multiple contiguous residues into a canonical sequence and the generation of a fusion protein containing fragments of two canonical ones . The undReactions involving mutated proteins as catalysts, inputs, outputs and regulators are annotated exactly as wild-type reactions are. To link reactions involving a mutated protein to those involving its normal counterpart, an optional ‘normal reaction’ attribute is used. A reaction in which a receptor constitutively activated by a mutation transmits a signal is thereby paired with the wild-type reaction in which a normal receptor is activated by ligand binding. A reaction catalyzed by the normal form of an enzyme is paired with the different one catalyzed by its gain-of-function mutant counterpart or with a dead-end reaction  associated with its loss-of-function mutant counterpart. A limitation of this strategy at present is that, as Reactome does not capture quantitative data such as reaction rates or binding affinities, quantitative effects of mutations are not readily annotated.http://www.sbgn.org/Documents/Specifications) and that is displayed on our Web site. The software that generates these displays has been extended to create disease displays in which the variant forms of the events responsible for a disease process are superimposed and highlighted on the normal process diagram  or updated (U), and identify ones that are parts of disease processes (+). Navigation controls in the upper left corner enable zooming and panning across the pathway panel. A diagram thumbnail in the lower left shows the part of the pathway currently displayed in the visualization panel. A widget icon in the upper right corner of the panel links to tools for searching within the displayed pathway, overlaying the pathway with functional interactors (see later in text) and for downloading the diagram as a snapshot or PNG file.A new tabbed ‘Details’ panel, below the pathway diagram, provides additional graphical and textual information. The ‘Overview’ tab provides summary information relating to the pathway, reaction or entity selected. The ‘Molecule’ tab displays external annotation and linking-out to other bioinformatic resources describing the selected molecule. The ‘Structures’ tab B displayBased on usability testing and feedback from our user community, we modified the context sensitive menus. In this new version, the mouse cursor can be positioned on the pathway background and right-clicked to display a new set of pathway diagram options.The molecular interaction overlay that provides the display of proteins or small molecules that interact with Reactome pathway proteins D is reviWe have merged pathway identifier mapping, over-representation and expression analysis tools into a single tabbed data analysis portal with integrated visualization and summary features . The patThe results of an analysis are displayed in three tabs that provide three different views of the same data. Pathway-oriented expression analysis provides a table of top-level pathways plus information relevant to the type of data submitted by the user. If presented with a list of identifiers, an enrichment analysis will be shown; numerical data will be summarized in a graphical way, using color to indicate average expression values. The identifier mapping view lists each of the identifiers supplied by the user with the corresponding UniProt identifier and a list of pathways in Reactome in which it functions. The overview is a completely new feature. It provides a canvas-based aggregated summary of the entire set of Reactome pathways, using visual cues to inform the user about the number of participating entities, relations between the pathways, average expression levels and other details.The pathway names in the pathway-oriented view are clickable links, which take the user to the relevant pathway diagram. In it, the sub-pathway nodes, complexes and entities are colored according to expression level. Entities that lack gene expression information are colored white. Pathway and complex nodes are colored in vertical segments with each segment representing a protein in the pathway or a component of the complex. The segments are stacked in order from lowest to highest expression level. A new icon at the base of the diagram allows the user to single step through the individual experiments or time points.http://protege.stanford.edu), SBML-ML  includes 131 rice pathways.The Reactome data model has been adopted by the Gramene group for manual annotation of plant pathways, especially metabolic processes specific to plants. The current release of Plant Reactome (Since 2011, Reactome has participated in the Google Summer of Code program as part of the Genome Informatics group, helping to create the software components for the new pathway browser, RESTful API and the pathway overview. The Reactome data and source code continues to be publically accessible under the terms of a Creative Commons Attribution 3.0 Unported License.


Metabolomics as analog to genomics, transcriptomics and proteomics is more and more integrated into biological studies and often transcriptomic and metabolomic experiments are combined in one setup. At a first glance both data types seem to be completely different, but both produce information on biological entities, either transcripts or metabolites. Both types can be overlaid on metabolic pathways to obtain biological information on the studied system. For the joint analysis of both data types the MassTRIX webserver was updated. MassTRIX is freely available at  In Systems Biology, Metabolomics is an interesting field as end-point of the paradigm that biological information flows from the genome over the transcriptome and proteome to the metabolome; it is the sum of all biological processes, including post translational modification and regulations. To generate hypotheses about regulatory processes between different levels, “omics”-approaches are combined in one experimental setup, whereas the combination of transcriptomics and metabolomics is often preferred. Gene expression data can nowadays be obtained by the use of gene expression chips or more recently with Next-Generation-Sequencing of transcripts. Since usually gene expression correlates well with translation, it reflects the protein levels to certain degree. Additionally, modern mass spectrometry (MS) technology is able to detect thousands of metabolites with high accuracy and precision. In particular, Ion Cyclotron Resonance Fourier Transform MS (ICR-FT/MS) and the newest generation of Time of Flight MS (ToF/MS) provide mass errors <0.1 ppm or <2 ppm, respectively. This accuracy, together with isotopic information allows the calculation of chemical formulas following several chemical rules The MassTRIX webserver is written in Perl using CGI for dynamic content representation and runs on an Apache2 web server (version 2.2.11).A calibrated mass list, consisting of tab separated masses, intensity values and an additional unique identifier, like an ID or a retention time, serves as main input for MassTRIX. These masses are compared within a certain error range against the theoretical adducts of compounds from different metabolic databases. The default database is a combination of KEGG +) and for negative ionization chloride ([M+Cl]−) and bromine ([M+Br]−) adducts, including the most relevant isotopes, are added. Isotopic peaks are filtered according to the natural abundance, meaning that masses corresponding to isotopes are only kept if the main adduct peak is found. For example an [M+37Cl]− adduct is only kept if also the corresponding [M+35Cl]− is also found. An exception from this is bromine, having a natural distribution of both isotopes of roughly 50%. Here peaks are kept only if both isotopes are found. By default only [M+H]+ or [M-H]− in the respective ionization mode are used for the search. The web interface allows the specification of either a relative ppm or absolute Da error. The absolute error scale enables the use with MS instruments of lower resolution, like Time-of-Flight instruments. Instead of a mass list, a file containing KEGG compound numbers can be uploaded, bypassing the whole annotation procedure for already identified metabolites. As several metabolites and molecules from different organisms are not listed in certain databases, we included the possibility to upload a separate database in flat file format containing a list of molecules  which will be also searched. This also allows to create a list of adducts not covered by MassTRIX, e.g. [M+H-H2O]+ or [M-2H]2−. A comprehensive overview on commonly observed ESI adducts can be found in Huang et al. The full basic functionality of MassTRIX explained in Suhre and Schmitt-Kopplin As new feature transcriptomic data can be uploaded. MassTRIX accepts the.cel format of Affymetrix, a self-annotated file or gene expression data pasted into the data field for data input. For gene expression with cel-files a reference file as control and a sample file are uploaded and are analyzed via GC robust multi array averaging using an R script. A self-annotated file or data to paste contains either a EC-numbers, KEGG identifiers or KO’s and tab separated from them a fold change or UP or DOWN as key word for the gene expression analysis. All processed data is visualized on the corresponding KEGG pathways of a chosen organism via the KEGG API, whereas by default only the “Glycolysis and Gluconeogenesis” pathway is used, saving time, since most users use MassTRIX for annotation of MS data. If other pathways should be included, it needs to be pointed out explicitly by uploading a list of pathways to be colored. It should be noted that even if only the “Glycolysis and Gluconeogenesis” pathway is chosen, information on the metabolite-pathway relationship of all pathways is included in the downloadable files.All results are downloadable as tab separated text files. The masses.annotated file includes all annotated raw masses with the respective information, including theoretical adduct mass, ppm or absolute error, number of possible isomers/isobars, KEGG ID’s, molecular formula and pathway information. A second compressed format is available containing all possible annotations for a raw mass together in one line with the additional metabolite-pathway relationship. Moreover files with the used input and option settings or fold changes together with png images of the colored pathways can be downloaded.For further analysis, results from the metabolomics part can be compared between jobs on either the compound or the pathway level. This feature enables the comparison of different experimental conditions or multiple time points in an experimental setup. The comparison of pathways can be used for deducing which pathways are affected by a treatment or sample condition compared to a control. It is carried out with the pathway number, also allowing comparison between different organisms. Pathways not present in one organism but in another will also be considered, whereby the number of annotated compounds is empty in the case of non-presence. Compound comparison uses information stored in the masses.annotated file and creates a matrix of all annotated compounds from all used jobs against the different jobs. For each job three columns, containing the submitted intensity, the calculated ppm error and the raw mass, are included. Metabolites not found in a certain job are left empty. An example can be found in Different combinations of the upload possibilities can be tested with the example jobs on the MassTRIX webserver.The updated version of MassTRIX allows the combined visualization of gene expression and metabolome data in one setup. Moreover the ability to compare different jobs on pathway or metabolite levels enables the analysis of time-series or experiments under different conditions.Current limitations are based on the insufficiency of the KEGG API in regards to multi-coloring of pathways and multiplexing of different samples on a shared representation. Despite this impediment, KEGG normally allows a good first overview on biology before using species specific databases, were available. The inclusion of MetaCyc gives the possibility to annotate metabolites stored in this database, being more specific for bacterial genomes and pathways This updated version fulfills the requirements of scientists working with both transcriptomic and metabolomic data. It allows for a quick overview of their data in the biological context of metabolic pathways and therefore enables the generation of first hypotheses for further in-depth analysis.Further improvements are ongoing with the integration of additional metabolic databases, e.g. KNApSAcK


Life science technologies generate a deluge of data that hold the keys to unlocking the secrets of important biological functions and disease mechanisms. We present DEAP, Differential Expression Analysis for Pathways, which capitalizes on information about biological pathways to identify important regulatory patterns from differential expression data. DEAP makes significant improvements over existing approaches by including information about pathway structure and discovering the most differentially expressed portion of the pathway. On simulated data, DEAP significantly outperformed traditional methods: with high differential expression, DEAP increased power by two orders of magnitude; with very low differential expression, DEAP doubled the power. DEAP performance was illustrated on two different gene and protein expression studies. DEAP discovered fourteen important pathways related to chronic obstructive pulmonary disease and interferon treatment that existing approaches omitted. On the interferon study, DEAP guided focus towards a four protein path within the 26 protein Notch signalling pathway. The data deluge represents a growing challenge for life sciences. Within this sea of data surely lie many secrets to understanding important biological and medical systems. To quantify important patterns in this data, we present DEAP . DEAP amalgamates information about biological pathway structure and differential expression to identify important patterns of regulation. On both simulated and biological data, we show that DEAP is able to identify key mechanisms while making significant improvements over existing methodologies. For example, on the interferon study, DEAP uniquely identified both the interferon gamma signalling pathway and the JAK STAT signalling pathway. High throughput technologies, such as next generation sequencing, microarrays, mass spectrometry proteomics, and metabolomics, are capable of evaluating the expression levels of thousands of genes, proteins, or metabolites in an individual run. As a result, the life sciences are experiencing a massive influx of data, exponentially increasing the size of databases 3A is a parent to child 4A with a catalytic edge, 1A is a parent to child 4A with an inhibitory edge, 3A4A7A is a path, 1A2A3A is not, Genes and proteins can be grouped into different categories on the basis of many traits: sequence, function, interactions, etc.. Grouping genes by biological pathway is often the most relevant approach to biologists. For this study, we represent biological pathways as directed graphs, where the nodes are biological compounds and the edges represent their regulatory relationships, either catalytic or inhibitory. A catalytic edge exists when expression of the parent node increases expression of the child node , where the identified genes are ranked based on expression values 1..A8A would prevent identification of significant differential expression by set analysis. Considering the additional information contained in the edges, it becomes clear that 3A4A7A represents a path with similar differential expression from reactants to products. Consequently, 3A4A7A represents a differentially expressed path and may possess biological significance, but is unlikely to be identified as such by set based approaches.The major limitation of set-based approaches in their application to pathway datasets is that they neglect the graph structure of the pathway. For example, in We define pathway analysis as any approach which identifies patterns of differential expression in a data set by considering pathway structure. In pathway analysis, researchers are generally interested in identifying pathways associated with a biological condition and determining the components of those pathways that explain the association. Thus, hypothesis testing can be viewed as a two-step procedure: first, test an entire pathway for differential expression; second, identify the path providing the greatest contribution to that differential expression. Recent approaches to pathway analysis test the generic hypothesis of a pathway differential expression without identifying specific paths 3A4A7A, 1A4A7A, 2A5A7A, 3A6A8A, and 3A6A7A and the set score for 1..A8A.One of the most popular methods for pathway analysis, signalling pathway impact analysis SPIA, Table 1 Zhao et al. introduced an approach that includes pathway structure in the analysis of genome wide association studies Protein interaction permutation analysis, designed for siRNA experiments, calculates the significance of the number of interactions in a network for which both genes are “hits” p>>n problems, where the number of genes or proteins, p, is considerably larger than the number of samples, n. Pathway and gene set analysis methods have the added complexity that gene expression within pathways is often highly correlated. Therefore, the statistical analysis approaches described above typically rely on random permutations of biological replicates in order to preserve expression correlation structure. However, the small sample size limits the number of possible permutations and, hence, the precision of p-value estimates. In addition, permutation tests are only applicable to simple experimental designs. Utilizing a random rotation approach circumvents these issues High-throughput data analysis typically falls into the category of In this study, we present a new pathway analysis method, Differential Expression Analysis for Pathways (DEAP). The primary assumption of DEAP is that patterns of differential expression in paths within a pathway are biologically meaningful. DEAP calculates the path within each pathway with the maximum absolute running sum score where catalytic/inhibitory edges are taken as positive/negative summands. To assess the statistical significance, we use a random rotation. Similar to other pathway analysis methods, DEAP tests a generic hypothesis of overall pathway differential expression. Contrary to current methods, DEAP identifies the most differentially expressed path to provide a refined focus for further biological exploration.As illustrated in  (left), where green = +1 and red = −1, is calculated as:: DEAP Algorithm.As an example, the score for the path containing all nodes in the inhibitory string in DEAP scores for different pathways are not directly comparable due to size and structure differences among pathways. Thus, we employ a self-contained approach which individually assesses the significance of each pathway. Generating a null distribution is complicated by the low number of samples relative to gene identifications and the correlation of gene expression within pathways. Most existing approaches use permutation tests to preserve the correlation between genes; however, small sample size limits their effectiveness. We use random rotation to circumvent these issues p-value, and the path with the maximum absolute score . The open source implementation  of this algorithm is available in Supplemental Materials (File S3).For each pathway in the analysis, DEAP outputs its score, the corresponding p<0.05), which is equivalent to one minus the type II error rate. The power of DEAP was compared to GSEA and SPIA, the two most popular gene set and pathway analysis methods, respectively. Comparative analysis of these methods included four key parameters: the overall effect , variation in individual gene effects (2gσ), sample size (n), and type I error rate.Data from the five pathways illustrated in μ values , the power of DEAP was approximately twice that of GSEA and SPIA, demonstrating improved sensitivity. For μ = 1 , DEAP had an increase in power over both GSEA and SPIA of two orders of magnitude. At μ = 1.25, the performance of SPIA improved substantially, approaching that of DEAP on all pathways except the long alternate route where SPIA was confounded by noise . Across the board, GSEA performed poorly because GSEA did not consider pathway structure and is dependent on comparisons to other pathways.Regardless of the level of differential expression, DEAP was consistently more powerful than were other approaches Figure 4n) grew, the power of DEAP relative to other approaches increased, particularly in pathways containing inhibitory edges (2gσ) increased, DEAP exhibited minor increases in power . The plots in Figure S4).To estimate the type I error rate, we simulated random data under the null hypothesis .In addition to comparing DEAP to GSEA and SPIA, we compared DEAP to several modifications of the DEAP algorithm, which were altered as follows: scores normalized by pathway length; all weights set to +1; and sum taken across the entire pathway. We also compared DEAP to a set-based implementation with rotation. DEAP had substantially higher power than all four approaches , variance (2gσ), and sample size, DEAP outperformed both GSEA and SPIA on the TGF-ß signaling pathway , variance (2gσ), and sample size . In addition to DEAP, GSEA, and SPIA, we applied the four alternative formulations of DEAP to both sets of biological pathways and noted the consistently strong performance of DEAP .In both sets of simulated data on real biological pathways, the type I error estimate was conservative for DEAP, GSEA, and SPIA .To verify that the simulated data effects are biologically relevant, we also applied DEAP to two sets of biological data on biological pathways. The experimental data are from a transcriptomic study of interferon We analyzed a microarray expression data of cells of radio-insensitive tumors that had been treated with interferon Additionally, DEAP analysis of the interferon transcriptomics data uses path identification to reduce the search space for future experimentation. Consider the Notch signalling pathway, which contains 26 proteins and is known to be activated by interferon treatment : Biological data, In order to illustrate DEAP on a different data type, we also analyzed a proteomics study which compared healthy smokers with patients diagnosed with COPD  and expression technologies . This broad applicability extends from the flexible design of DEAP: the only required inputs are expression levels of biomolecules and corresponding pathways. Appropriate scaling of the expression levels is defined by the user. For instance, RNAseq data is very similar to spectral count proteomics data in that they are both count-based. Thus, RNAseq read counts can be used as input for DEAP in the same manner as peptide spectral counts. Further, RNA transcripts can be used in place of proteins.http://proteinspire.org) and expression database MOPED (http://moped.proteinspire.org) To identify the most important pathways for further study, pathways can be ranked based on DEAP score significance. Specifically, future studies can be focused on the most differentially expressed paths within the pathways with the lowest false discovery rate, which can be especially beneficial when studying pathways that contain hundreds of biological compounds. Currently, DEAP is being integrated with our proteomics analysis pipeline SPIRE  for each gene in a pathway was simulated using a multivariate normal distribution defined in g is randomly generated  for each gene in the pathway, but the same value is used for replicate samples. The variable e is assumed to come from a normal distribution with mean 0 and variance 1 and represent random variation in gene expression. The value of e is randomly generated for each combination of gene and sample. The simulations varied the values μ, 2gσ, and the sample size (number of independent samples of pathway data). R scripts were used to generate the simulated data The value of μ values of +X and −X, respectively, where X was a positive number. Simulated data and pathways are available on Dryad: doi:10.5061/dryad.qh1pg.Five diverse pathways were specifically created to test the efficacy of identification by different scoring methods Figure 3: DEAP). When duplicate probes existed for the same gene, the expression value utilized for the gene was the arithmetic mean of these probes.Microarray data from a study of cells treated with interferon were acquired from the Gene Expression Omnibus (GDS3126) Table S3. On MOPED, data is stored under the experimental name “steffan_copd.” The tandem mass spectrometry data were analyzed through SPIRE with the parameters in Table S4The COPD proteomics data can be found at PeptideAtlas (raw data) Pathway data were downloaded from the PANTHER database X of standard normal random variables and then taking the rotation matrix to be the orthogonal matrix Q from the QR decomposition of X. Scripts to carry out rotation testing were written using the R programming language and are available in File S3, released under the GNU Lesser General Public License v3.0. The user is able to input a custom design matrix which accounts for complex experimental designs with multiple conditions and replicates.We used random rotation approach to estimate the null distribution of the test statistics and compute the p-values Given: a current edge, all other edges in graph, expression values for all proteins:E(x) to be the difference between the logarithm of the arithmetic mean of expression values associated with protein x in the two conditions.For single channel (unpaired) data, define E(x) to be the arithmetic mean of the log expression ratio(s) associated with protein x.For two channel (paired) data, define The recursive function operates as follows:Recursively examine all edges in the pathway set whose reactant node is the current edge's product node.recursivemax and recursivemin as y, contained in the edge's products.If there are no such edges, set recursivemax and recursivemin as the maximum and minimum scores, respectively, returned by the recursive function.Otherwise, define scoremax and scoremin as the maximum and minimum, respectively of: T(edge) is the multiplier associated with the edge type  and z, contained in the edge's reactants.Assign scoremax, 0} and the minimum of {scoremin, 0}.Return the maximum of {In DEAP, the maximum order  path is used to test the null hypothesis about the expression of the entire pathway. This claim, that the expression of one path answers questions about the expression of the pathway, is justified on two levels.3A4A7A represents a path with similar expression levels that proceeds all the way from reactants to products, a pattern that seems to be significant.On a biological level, significant fluctuations in activity do not require differential expression of an entire pathway. For example, in P, as the union of all paths of the pathway, 1P, 2P, …, KP. Each path is completely defined by its set of edges. Note that the k-paths are not entirely disjoint in the sense that some paths might share the nodes and the edges. However, we require each path to have a distinct set of edges. To test the hypothesis of a differentially expressed pathway requires testing whether any of the constituent paths is differentially expressed. This corresponds to testing the family of k-null hypothesis. To control the family wise error rate, we use a maximum order statistic, since the probability of making at least one incorrect decision under the null is equivalent to the probability of the maximum order statistic exceeding the threshold.From a logical perspective, consider a pathway, s*, we performed n rotations of the data. For each rotation sample, we recompute the DEAP score, is. The p-value is calculated as a proportion of scores that are at least as extreme as the observed score, the proportion of simulated DEAP scores whose value are greater than or equal to the observed DEAP score:To approximate a null distribution of the test statistic, The DEAP algorithm was implemented to allow for efficient computation.By maintaining global maximum and minimum values and updating their values as the recursive function proceeds, it is not necessary to examine all paths of the graph independently. Rather, we can initialize DEAP score calculations only at leaf edges, which have no upstream edges pointing to any proteins in their reactant set. To ensure that closed cycles are not missed, we track the edges which have been visited and examine additional edges until the difference of the complete edge set and the already visited edge set is empty. This greatly reduces the number of calculations per graph.Once the recursive function has returned a maximum and minimum score for a particular edge, that score will remain constant regardless of the preceding edge except in the case of cycles (see paragraph below). Therefore, we use a dictionary mapping edges to maximum and minimum scores to prevent duplicative score calculations. After this implementation, score calculations that took several hours on particularly complex pathway structures completed in seconds.In the case of cycles, scores may be dependent on the node of the cycle which is examined first. For these cycles, our current implementation represents a heuristic estimator rather than the exact optimal solution. Bidirectional edges are subject to this same limitation as they are equivalent to a two node cycle. Implementations that determined the exact optimal solution were prohibitively slow for practical application. Except in edge cases, the heuristic implementation will provide approximations of sufficient quality to identify significant patterns of differential expression.Every DEAP score calculation is independent of other DEAP score calculations, so we set up processing for multi-threading. For example, on a 64-bit Intel Core i7-2720QM CPU with 8GB RAM, speed improvements of approximately 4-fold were noted for the score calculation process. Specific running time is highly dependent on expression data set size, experimental design, pathway complexity, and number of rotation testing iterations. Running DEAP on 90 simulated data files each with 10 samples, 1000 proteins, 1000 pathways, and performing 100 data rotations took 72 minutes when multi-threaded and 260 minutes when performed on a single thread.The function tracks edges that have already been examined in a particular recursive cycle to prevent entrance into infinite loops in cyclical pathways. To control for duplicate protein identifiers, summations over the products and reactants were performed on the set of unique expression values rather than for every identifier. For example, if protein A and protein B both had expression levels of 1.743 and were both in the same protein set, then it was assumed they were the result of data duplication and 1.743 was only added to the score once. This duplication elimination was implemented primarily due to issues arising from redundant protein identifiers and potential mRNA translation into multiple proteins. For instance, the five UniProt identifiers for variants of Histone H3  are included in the same PANTHER pathway unit and share near identical protein sequences, so their proteomic and transcriptomic identification will be duplicated.File S3, released under the GNU Lesser General Public License v3.0.The algorithm was implemented in Python and is available in http://scholar.google.com) for a combination of the pathway name and details of the experimental condition. We continued searching Google Scholar until satisfied that the association was confirmed or felt reasonably certain that there was not yet a literature confirmed association. Once a literature association was confirmed, the most pertinent reference was retained and cited in this manuscript.Accuracy of pathway associations with experimental conditions were validated using a Google Scholar literature search. The literature search was performed by searching Google Scholar Click here for additional data file.Figure S2Power vs. sample variance for all 7 approaches for simulated data on simulated pathways.(TIFF)Click here for additional data file.Figure S3Power vs. sample size for all 7 approaches for simulated data on simulated pathways.(TIFF)Click here for additional data file.Figure S4Type I error for all 7 approaches for simulated data on simulated pathways.(TIFF)Click here for additional data file.Figure S5Type I error for simulated data on biological pathways.(TIFF)Click here for additional data file.Figure S6http://www.genome.jp/kegg-bin/show_pathway?map04350 with permission from KEGGPower vs. pathway effect, sample size, and variance for all 7 approaches for simulated data on the KEGG TGFβ signalling pathway. Figure adapted from (TIFF)Click here for additional data file.Figure S7Power vs. pathway effect, sample size, and variance for all 7 approaches for simulated data on the Reactome post-transcriptional silencing by small RNAs pathway.(TIFF)Click here for additional data file.File S1DEAP results on CF data.(TXT)Click here for additional data file.File S2DEAP results on COPD data.(TXT)Click here for additional data file.File S3Archive file of DEAP source code licensed under the GNU Lesser General Public License v3.0.(ZIP)Click here for additional data file.Table S1Provides a summary of approaches to pathway analysis, rationale for their inclusion, and summary of the results for simulated data.(DOC)Click here for additional data file.Table S2Decision justifications.(DOC)Click here for additional data file.Table S3PeptideAtlas accession numbers for COPD study.(DOC)Click here for additional data file.Table S4COPD search parameters using SPIRE.(DOC)Click here for additional data file.Table S5: Results from Interferon microarray data analysis using GSEA, SPIA, and DEAP.p-values for (XLS)Click here for additional data file.Table S6: Results from the COPD proteomics data analysis using GSEA, SPIA, and DEAP.p-values for (XLS)Click here for additional data file.


Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset.We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar.Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well. Biological pathways provide a powerful medium to explore and reduce the complexity of large datasets. Pathways organize genes, proteins, metabolites and their interactions into functional groups, often visualized as diagrams or networks. A commonly employed analysis technique using pathways is enrichment analysis, where pathways are represented as gene sets and where the aim is to find those sets that are enriched with entities of interest, such as differentially expressed genes . More reOne way to get insight in possible relationships between pathways is to look at their overlap in gene, protein or metabolite content. Pathways with a high overlap might be related by shared paths. Tools such as ClueGO  and EnriAnother more functionally based approach is to find possible pathway cross-talk by looking at protein interactions between pathways. Cross-talk allows multiple pathways to exchange signals and influence each other. For example, the P53 pathway can control the Cell Cycle pathway by regulating the expression of p21 and can itself be activated by several pathways, for example the MAPK pathway. Metabolic pathways may share enzymatic reactions and may influence each other by influencing the availability of a substrate. These forms of pathway cross-talk are highly context dependent, for example, interactions between the P53 pathway and Cell Cycle depend on several external stress factors such as DNA damage or oxidative stress. Previous studies have already explored this idea ,7 by buiIn this study, we investigate interactions between pathways by finding regulated paths between pathways for a given transcriptomics dataset. While the methods of Li et al. and Huang et al. only consider direct protein interactions between pathways, considering paths spanning multiple interactions may detect indirect interactions as well. Indirect interactions consist of paths including one or more proteins that are not annotated to a pathway, but do have known interaction or binding partners. In case the majority of the genes encoding these proteins and their interacting partners in two different pathways would be differentially expressed in a given condition, this indicates a potentially relevant path through which these pathways interact. An algorithm to detect pairs of pathways that contain such a path would allow researchers to directly explore and visualize possible paths along which pathways might interact in a given context. Furthermore, the inclusion of proteins that have not yet been annotated to a pathway in the analysis makes it possible to look beyond well studied processes, increasing the chance of generating novel hypotheses.We designed a method to detect both direct and indirect interactions between pathways and visualize the resulting paths and applied this method to a transcriptomics dataset from the European Nutrigenomics Organization (NuGO) PPS2 study  to invesTo find potentially regulated paths between different pathways, we designed a method based on non-redundant shortest paths in a weighted graph. The required input data are:1. A set of pathways, each pathway consisting of a collection of gene, protein and/or metabolite entities.2. An interaction network, providing interactions or functional associations between genes, proteins and/or metabolites, which can be compiled from different resources.3. A weight value for each edge in the interaction network, indicating how much an edge will contribute to the length of a path. This can be based on experimental data, for example the expression of the genes that the edge connects.We will now briefly introduce the approach. For a more complete description of the procedure we refer to the methods section. For each pathway pair, a score is calculated by finding the set of non-redundant weighted shortest paths that have a length which is smaller than a given threshold and do not cross any other pathway. Non-redundant paths are used to identify the different routes of information transfer between the pathways. The length of each path is defined by the sum of the edge weights in the path. By assigning a lower weight for edges between nodes for which the corresponding genes are more differentially expressed in the dataset, paths that include regulated genes will get a shorter length. The score for a pathway pair depends on the number of paths found and their length Figure . The morThis method was applied to pathways from the WikiPathways  and KEGGPathway interactions were investigated in the context of a transcriptomics dataset of mouse liver samples. This dataset contains gene expression measurements before (t = 0) and at 0.6 hour (t = 0.6), 2 hours (t = 2) and 48 hours (t = 48) after a glucose challenge in two groups of mice fed either a low fat (LF) or high fat (HF) diet for 12 weeks before the challenge. After these 12 weeks all mice fed with HF diet had become obese and had developed insulin resistance [Edge weights were derived from the computed gene-level T statistics of the comparisons to generate a weighted interaction network for each comparison (see methods). The number of edges in each interaction network that have a weight smaller than the maximum path length (see methods) and hence may contribute to a path between two pathways is shown in Table The results of the algorithm can be represented as two different types of networks Figure . First aThe number of identified pathway interactions ranges from 25 to 207 across the different networks  to over 1500 for the largest (comparison between diets at t = 0). Most paths consist of direct interactions between proteins in the interacting pathways  that are not annotated to a pathway, but do all bind to a few proteins that play a role in many different pathway interactions, such as Ywhab, Prkaca, Tubb5 and Hsp90ab1. One of the proteins that is not annotated to any pathway is Sgk3 which plays a role in several pathway interactions for the response to the glucose challenge in the LF diet group at t = 2 and t = 48 . Since Gsk3b is present in both the Axon guidance and Insulin signaling pathways, in addition to the possible interaction between the two pathways via Sgk3, it might also form an alternative route within the Insulin signaling pathway that has been differentially regulated in the HF diet group  with differentially expressed genes. Based on this filtered network, we study several potentially interesting interactions and subgraphs. A good starting point for this exploration might be the 3 apoptosis and stress response related pathways that have the highest in-degree in the network. Interestingly, 8 out of 14 pathways interacting with at least one of the three apoptosis related pathways also have an incoming interaction from the ESC pluripotency pathway, which is significantly enriched (p < 0.01). This pathway has a strong interaction (consisting of 38 paths) with the Proteasome pathway, which is also significantly enriched (p < 0.01). The remaining pathways interacting with the 3 apoptosis related pathways include 4 pathways with a relatively high out-degree of which 3 are significantly enriched (p < 0.05), and two versions of the Wnt signaling pathway, which both have a strong connection (25 and 27 paths) with the significantly enriched TNF-alpha NF-kB Signaling Pathway (p < 0.05). The subgraph containing these pathways and their interactions is shown in Figure Raf1, Kras, Nras and Prkcb are present in more than 10 of the source pathways and all form a path to the three target pathways via Map3k1, which participates in 53 paths between the pathways. Secondly, there is a distinct path through Prkar1a, which forms an interface to the Gap junction, Long term depression and Wnt signaling pathway via indirect binding interactions with Ppp2r1a, Tuba4a and Tubb5. Ppp2r1a may also play a role in the interaction with the TNF-alpha NF-kB Signaling pathway and two versions of the Wnt pathway via indirect binding interactions with Ywhab . The pathway diagram of the ESC pluripotency pathway . When looking at the protein interactions that form the interactions between these pathways, it turns out to be comprised of two binding interactions between Jun and Fos, for which the gene expression has both increased more than 5-fold compared to t = 0, and Il1b with a 2.5 fold increase  is reflected in the small size of the corresponding network. Studying the protein interactions for that network reveals that all identified pathway interactions include Hspa1a and Hspa1b, are differentially expressed (> 4 fold) and interact with Ap1s1, Ap1b1 and Cltc. These proteins are involved in transport of lysosomal enzymes and show moderate, but not significantly differential expression . Based on the pathway diagram, Mapk7 is a downstream target of the S1P receptor pathway, but because the other proteins in this pathway do not show significantly differential expression, it does not seem likely that the differential expression of Mapk7 is related to upstream activities in this pathway. However, since Mapk7 is present in the insulin signaling pathway as well, this might point to an alternative path within the insulin signaling cascade, which may be affected by HF diet in response to the glucose challenge.One of the components of the HF network at t = 0.6 that is not present in LF, for example, is composed of the Antigen processing and presentation and Spliceosome pathways, both interacting with the Lysosome pathway. These interactions have been identified because two genes encoding for proteins of the HSP70 family, Il1r1 and Il1r2, involved in a path to Apoptosis and oxidative stress via Casp8 and Casp9. All other receptors that contribute to the pathway interactions, including up-regulated Igf1r, Erbb3, Egfr and down-regulated Kdr and Fgfr4, are receptor tyrosine kinases and form a path to several specific interleukin signaling pathways, T-cell and Kit receptor pathways via the Cbl protein. Cbl is a ubiquitin-protein ligase that can target these receptors for endocytosis [Cbl and the receptor tyrosine kinases as well, shares all its interacting pathways with the Cytokine pathway. In addition to Cbl, the interactions between the Endocytosis pathway and its neighbors also include the up-regulated Cltc and Cltb, which encode for the two light chains of clathrin, as well as several other proteins that are involved in clathrin dependent endocytosis , which has the highest betweenness centrality. Several receptors listed in this pathway are differentially expressed, such as ocytosis . Its expfile Cbl . TogetheCdc27 and Ctnnb1 in the TGF-beta pathway show a similar pattern. The interaction in the LF network contains another protein, Axin1, which is significantly up-regulated in the LF group and slightly down-regulated in the HF group is not included in the latter interaction due to its low significance.A notable aspect of the late response to glucose challenge is that the LF group contains many more differentially expressed genes than at the early time points, while in the HF group the number peaks at t = 2 and is lowest at t = 48. This difference is not reflected in the pathway interaction networks for t = 48, which both contain almost the same number of nodes and edges. However, the overlap between these networks is small; only 4 edges are present in both networks, including the interaction between the TGF-beta receptor signaling Pathway and Proteasome. Interestingly, the proteins in the paths between these two pathways with the TGF-beta and Proteasome pathways behave differently between the HF and LF group , was found to play a role in preventing insulin resistance and blocking inflammation in human muscle by preventing c-jun amino terminal kinase (JNK) phosphorylation [First, the expression of two proteins of the HSP70 family show a distinct peak of over 4-fold increased expression at t = 0.6 in the HF group, but not in the LF group. We identified a possible interaction of these proteins with the Lysosome pathway that may indeed play a role in this pathway by uncoating of clathrin-coated vesicles . Howeverrylation . Since JMapk7, Rps6kb and Sgk1 in the Insulin signaling pathway and the transcription factor Nr4a1 in the Nuclear receptors pathway. Both Nr4a1 and Sgk1 show an expression profile with a peak at t = 0.6 in both the HF and LF group, however Mapk7 and Rps6kb2 are only up-regulated in the HF group. This difference might indicate the presence of an alternative mechanism through which Nr4a1 can be activated. Nr4a1 is a known transcriptional regulator of genes involved in glucose metabolism in mouse liver [Second, we identified an interaction between se liver . BecauseClb protein. Several of the proteins participating in the interactions with the cytokine receptor and Endocytosis pathways are known to be related to insulin or even annotated to the Insulin signaling pathway. For example, Cbl plays an important role in the CAP/Cbl/TC10 dependent transport of GLUT4 vesicles [Cbl, Egfr and several other proteins in the Insulin Signaling pathway, including three subunits of PI3K, Crk and Igf1r. Although this interaction was not considered significant, it might be biologically relevant given the high differential expression of Cbl and Egfr.The most notable property of the pathway interaction networks for the glucose response at t = 2 is the high centrality of the Cytokine pathway. This pathway summarizes different cytokines and their receptors, and since several of these receptors are differentially expressed it makes sense that this pathway fulfills a central role in the network, by connecting to the different processes these receptors play a role in. We identified a regulated interaction between several cytokine receptor pathways and the Endocytosis pathway, which might point to increased removal of receptor tyrosine kinases from the cell membrane in the HF group after ubiquination with the up-regulated vesicles . This coGsk3b is involved in degradation of beta-catenin by proteolysis, a process which can be inhibited by Wnt stimulation to stabilize beta-catenin, and which subsequently activates transcription of target genes [Ctnnb1 and its associated proteasome related genes shows a different expression in the LF compared to the HF group at t = 48, so this mechanism could be affected by diet in the late response to the glucose challenge.The switch from glycogen storage to glycogen breakdown observed at t = 48 can be expected after 48 hour following the glucose challenge. However, the link with the Insulin and Il-6 signaling pathways is interesting, since the Il-6 signaling pathway has already been linked to insulin actions and glycogen metabolism in vitro . Anotheret genes . InspectIn summary, this analysis provided several new insights in the pathway interactions underlying insulin resistance in mouse liver and and the response to glucose. Initially the LF and HF group showed very distinct interactions with three apoptosis related pathways as most highly connected in the HF group. Despite the clear pathway and network differences in mouse livers after 12 weeks of LF or HF diet feeding, the initial response to a bolus of glucose is remarkably uniform and Il1a, Il1b as well as well as the transcription factors c-jun/fos (AP-1) appear to play an important role irrespectively of the degree of obesity and insulin resistance. This observation is in line with reports showing that the NLRP3 inflammasome senses glucose overload and triggers IL-1b activation in a caspase-3 dependent way  and suggAs illustrated in the analysis described here, the method we developed is especially useful for exploring large and complex datasets to find interesting aspects and generate hypotheses. It can be used to add an extra dimension to differential gene expression and pathway enrichment results. Instead of redefining pathway boundaries based on an interaction network, this approach directly utilizes predefined pathways and thereby complements the search for activated or regulated genes and pathways by taking into account how they may interact. These interactions between pathways are not static and fixed, but are specific to the studied context, providing a dynamic view on the pathway landscape. This approach allowed us to explore the NuGO PPS dataset and identify relevant gene and protein interactions. It also helped to hypothesize about underlying mechanisms and possible downstream effects of certain groups of differentially regulated genes, thereby providing starting points for more focused follow-up studies or experiments.During the analysis we mainly followed a fixed approach to explore the pathway interactions consisting of three main steps. First, we studied the pathway interaction network globally to identify highly connected or central pathways and proteins in the network. This, for example, pointed us to the apoptosis pathways in the comparison between the two diet groups at t = 0, which would not have been found by looking at pathway enrichment alone. In the second step, we interactively explored the interaction network using Cytoscape and zoomed in to specific pathway interactions or subgraphs by visualizing the protein interactions that form the paths between the pathways. This step is partly guided by the results of the previous step, since neighborhoods of the central pathways or proteins often point to interesting interactions. In addition, we looked for further interactions based on pathways that are known or expected to be involved in insulin resistance. For example, this lead us to the interactions with the insulin signaling pathways in the late HF response, which were not central in the network but appeared to be relevant because of enrichment of all involved pathways and the central role of insulin in this analysis. Finally, we often referred back to the original pathway diagrams to understand the context of the interacting proteins within the pathway and look for any upstream of downstream effects Figure . TogetheIn addition to the new perspective this analysis provides on the dataset, there are several advantages that make this method a useful complement to existing pathway analysis techniques. Firstly, compared to an analysis limited to pathway annotations, this combinatory analysis integrates additional information by using other sources of protein interactions and allowing indirect interactions including proteins that are not annotated to any pathway. This extends the coverage of the analysis with an additional 1660 genes that are in the interaction network but not annotated to any pathway, of which 929 are available in our transcriptomics dataset. This allowed us to move beyond well annotated knowledge in pathways, while still benefiting from the framework of canonical pathways and their intuitive visualizations. While the interaction network we used here is still relatively small compared to the number of interactions we expect to exist, current developments in measuring protein interactions  and idenBesides several advantages, we also identified several possible improvements that may be made to this approach. False positive pathway interactions were identified on occasion. One cause can be falsely annotated interactions between proteins in the input interaction network. This is the main reason we already excluded associations based on text-mining only from the STRING database, however remaining data may still contain false associations. For example, the component of metabolic pathways positioned around the Primary bile acid biosynthesis pathway turned out to be a false result after close inspection, due to falsely associated enzymes in the STRING database. Over time, the quality of interaction resources will probably improve, also helped by curation initiatives such as WikiPathways.Another cause of false or misleading results is the heterogeneity of many of the pathways. Pathway databases often include context specific pathways, which provide a summary of processes that are relevant to a specific disease or cell type or down-stream signaling of a specific messenger. However, the processes included in these pathways are often generic and play a role in other contexts as well, so finding an interaction with such a pathway does not automatically mean that its context is also relevant to the studied data. For example, we identified several interactions with the ESC Pluripotency pathway, which summarizes several signaling cascades regulating pluripotency in embryonic stem cells. It is very unlikely that embryonic stem cell functioning plays a role in our dataset, but still several interactions with this pathway were identified. These turned out to involve only specific parts of the signaling cascades, especially part of the Wnt signaling pathway. This is also a common problem in enrichment analysis, and our method does not provide a solution yet. Using a set of smallest generic core pathways with as little overlap as possible might lead to cleaner results. Possible alternative sources of pathway information that might provide better results than the databases used here could be Gene Ontology  or ReactThe main shortcoming of using only transcriptomics datasets in pathway-based approaches is that these provide insight in changes at the mRNA level only. In this study, each identified pathway interaction is based on the assumption that if a group of interacting genes is consistently differentially expressed it is likely that changes are reflected at the protein level as well. However, this may not always be the case and it is not possible to investigate beyond this assumption using such datasets. In addition, a large part of signaling and pathway cross-talk probably works at a different level, through protein activation by phosphorylation or other post-translational  regulatory mechanisms. If such interactions play a role, they cannot be identified with this dataset unless the interaction involves gene expression changes as well. The interactions that are most relevant to this dataset are transcriptional regulation, but unfortunately our interaction network contains relatively few transcription factor targets and we were unable to find a resource to increase this number in addition to the PAZAR database  used in Although in this particular study we used a transcriptomics dataset only, the method described here can directly be applied to other types of data or combinations of different data types as well. The method requires an edge weight for each interaction, which can be derived in different ways, depending on the type of interaction and available data. For example, the interaction network could be extended using the STITCH database , which dWe designed an analysis approach to identify interactions between biological pathways in a specific context. By applying this method to a transcriptomics dataset, we identified relevant pathway interactions and possible key proteins involved in pathway cross-talk in the context of insulin resistant mouse liver, and at specific time points following a glucose challenge response. In addition, the analysis approach presented here can be applied to different types of high-throughput data and will be of more general use to facilitate interpretation of other complex datasets.http://www.ebi.ac.uk/arrayexpress, accession number: E-MTAB-601).The gene expression microarray data from the NuGO Proof of Principle Study 2  was usedlimma R package [The different groups were statistically compared using the  package . To studHF vs LF, t = 0: The moderated t-statistic, representing the significance and direction of differential expression between the HF and LF group at t = 0.TLF, tx vs t0: The moderated t-statistic, representing the significance and direction of differential expression between each time point after the challenge  and the time point just before the challenge (t = 0) in the LF group.THF, tx vs t0: The moderated t-statistic, representing the significance and direction of differential expression between each time point after the challenge  and the time point just before the challenge (t = 0) in the HF group.Tlimma package, q-values were calculated using the qvalue package [Based on the corresponding p-values as provided by the  package  to correPathways from WikiPathways   were usePathways with high overlap in protein content were merged into a single pathway using a step-wise procedure:1. Calculate overlap between each pathway pair.2. For each pathway, find other pathways that have > = 75% overlap and merge it with the most overlapping pathway.3. Repeat step 1-2 until no pathway pair with > = 75% overlap is left.This merging step was performed because pathways were combined from two different resources that partly overlap, so different pathways that actually represent the same underlying biological mechanism may occur.geneSetTest from the limma package was used. This test calculates a test statistic for each pathway by taking the mean of the absolute T-statistic of the genes in the pathway and calculates P-values by comparing the test statistic to an empirical null distribution based on 10,000 permutations with random gene sets. The resulting p-value for each pathway represents the significance of its enrichment with differentially expressed genes, regardless of their direction.To find pathways that are enriched with genes that score high in the differential expression test, the The interaction network used in this study combines the following databases:- Protein functional associations from the STRING database (v8.3) . This da- Transcription factor targets from the PAZAR database (v2010-08-15) .- Manually curated reactions and interactions from the pathways described in the section 'pathways'.These interactions were merged into a single directed network. For interactions without a defined direction (e.g. binding), two directed edges in opposite direction were added.xref in the following description of the algorithm.Genes and proteins were mapped to a common database using the BridgeDb library  and accoTo find possible interactions between pathways, we started by generating a unified graph, integrating the pathway and interaction information:PX Gcontaining both the xref interactions and pathway-xref associations was created and reused for each group comparison. This graph contains two types of nodes, either representing an xref or a pathway. Edges were added between the xref nodes based on the interaction network. In addition, each pathway node was connected to each of the xref nodes that are associated with that pathway.1. A graph Then the following steps were followed for each group comparison separately:PXG, based on the gene-level statistics described in the section 'Microarray gene-level statistics'. First, a transformation using a sigmoid function was applied to the T statistic:2. Edge weights were calculated for This function ensures that the transformed values will range from 0 to 1 and results in a soft threshold  of the target xref nodes they connect to, or to the maximum value of 1 if no data was available for the target xref. If the target of an edge was a pathway node, the weight was set to 0. This way, for each edge, the weight value inversely depends on the value of the T statistic of the target gene, receiving a low weight value if the gene is differentially expressed. Thereby, paths that include genes (or the proteins they encode for) that are differentially expressed will have a shorter length.Secondly, edge weights were assigned based on the transformed T statistic Pn in PX Ga subgraph Pn Gwas created containing only nodes that could be reached within nb steps (visited edges) from Pn. In this study, parameter nb was set to 5, limiting the number of edges between xref nodes in a path to maximal three. This limit was introduced mainly to reduce calculation time. Based on the distribution of resulting path lengths, increasing nb is unlikely to have a large effect, as only a single path with three edges between xref nodes had a weighted length <maxl.3. For each pathway node Pm in Pn Ganother subgraph was created containing nodes Pn, Pm, the direct neighbors of Pn and Pm (the xrefs in these pathways) and all xref nodes that are not direct neighbors of a pathway node (xrefs that are not in any pathway). Excluding xref nodes that are present in pathways other than Pm and Pn is necessary to prevent detection of false interactions between Pn and Pm that cross other pathways. Such an interaction should not be represented as direct interaction between Pn and Pm, but rather as two interactions with the other pathway as intermediate between Pn and Pm. For xref nodes that were connected to both Pn and Pm, the edge to Pm was removed to prevent paths to go directly through an overlapping xref . The path length was stored and the edges of the path, except those that connect Pn or Pm, were deleted from Pn G interactions, while a smaller lmax will have the reverse effect and only include interactions with highly significantly changed genes. In this study, max lwas set to 0.9, ensuring that paths between xrefs with low significance or no measured data  do not contribute to the interaction score.6. Step 5 was repeated until a preset maximum path length d Figure . The parnPm Pa list of path lengths i lis now available. These were converted to a single interaction score PnPm Sfor each pathway pair by calculating the sum of the inverted lengths:7. For each pathway pair Pn and Pr (a randomized version of Pm), resulting in a collection of randomized interaction scores PnPrS. For each permutation, Pr was generated by reconnecting the endpoints of its adjacent edges (which are the nodes representing the xrefs associated to the pathway) to different xref nodes from the interaction network that have a similar strength  as the R [igraph package [This algorithm was implemented in the statistical language R  and uses package  for handTo reduce the contribution of overlapping proteins between pathways to the interactions between pathways, the edge from an overlapping protein to one of the pathways in a pair is removed in step 4. While pathways with more than 75% overlap were merged, some pathways might still have much overlap that may outweigh the influence of pathway interactions via protein interactions. However, to completely discard overlapping genes might be too conservative, since an overlapping protein may be a key regulator that might connect two distinct parts of the different pathways. For example, in Figure igraph library in R. Betweenness centrality as displayed in Additional file igraph function betweenness by the maximum centrality, which is the total number of node pairs in the network, excluding the node for which the betweenness centrality is calculated.All networks were visualized using Cytoscape . NetworkHF: high fat ; LF: low fat ; NuGO: European Nutrigenomics Organization; PPS2: Proof of Principle Study 2.Designed and performed computational analysis: TK. Designed and performed transcriptomics experiment: MvE RK TeK. Normalization and quality control of transcriptomics data: MvE. Extensive reviewing and editing of the manuscript: CE, LE. Drafted the manuscript: TK. All authors read and approved the final manuscript.Supplementary figures and tables. Contains the supplementary figures and tables.Click here for fileCytoscape visualization of pathway interaction networks. Contains the cytoscape session files with interactive visualizations of the pathway interaction networks. These can be viewed with the freely available Cytoscape software http://www.cytoscape.org/.Click here for fileScripts and input data. Contains instructions on how to obtain the scripts, utilities and formatted input data that was used in this analysis.Click here for file


Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013.Positional MEDLINE (PosMed;  Mouse bioresources contribute to the study of human genes and diseases ,2. To elhttp://www.w3.org/RDF/) and query language SPARQL (http://www.w3.org/TR/rdf-sparql-query/) do not adequately support statistical evaluation of semantic links, we developed the GRASE for the implementation of PosMed . At least once a month we update PosMed data and its search index over the 10 million biological documents.For Semantic Web compliant data preparation, we used RIKENBASE or the RIKEN Scientists’ Networking System (SciNetS) , and pubPosMed is implemented as a web application that users can access freely via their web browsers without log in. Although users can use a conventional web browser and a web browser plug-in is not needed, for Windows we recommend Microsoft Internet Explorer 9 or later, Firefox 18 or later and Google Chrome 24 or later. For Macintosh we recommend Safari 5 or later and Firefox 18 or later.The web server is developed in Java and contains 11 Linux servers, including 10 distributed servers using GRASE engines  that perAlthough since the first launch of the PosMed service we have often been requested by users to implement a system to support batch queries, we do not support this yet because of machine resource limitations . Batch queries will be supported by securing additional machine resources in the future.Since 2005, PosMed has been widely used to prioritize candidate genes after Quantitative Trait Locus (QTL) analysis in mice and successfully identify responsible genes . This tiSeveral eminent databases have released RDF files, but not so many scientists use Semantic Web technology actively. This may be partially because bioinformaticians like to calculate the statistical significance of associations of the RDF connections rather than a simple Boolean retrieval of the connections. To solve this problem, we propose our original methodology SWAS for statistical searching of the biological Semantic Web data. PosMed executes SWAS to rank significantly enriched groups of biological resource data items that can be associated with a user-specified query through the big data of Medline, omics data sets, other semantic data and so on. Our results confirm that such enrichment analysis using our SWAS methodology is effective  and provNational Bioscience Database Center (NBDC) of the Japan Science and Technology Agency (JST). Funding for open access charge: Grant from the NBDC of the JST. Conflict of interest statement. None declared.