Biological databases and pathway knowledgebases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. But before we can reliably use a pathway knowledgebase as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference.We design a series of logical tests to detect potential problems we might encounter using a particular knowledgebase, the Reactome database, with a particular computer-aided hypothesis evaluation tool, HyBrow. We develop an explicit formal language from the language implicit in the Reactome data format and specify a logic to evaluate models expressed using this language. We use the formalism of finite model theory in this work. We then use this logic to formulate tests for desirable properties  for pathways stored in Reactome.We apply these tests to the publicly available Reactome releases (releases 10 through 14) and compare the results, which highlight Reactome's steady improvement in terms of decreasing inconsistencies. We also investigate and discuss Reactome's potential for supporting computer-aided inference tools.The case study described in this work demonstrates that it is possible to use our model theory based approach to identify problems one might encounter using a knowledgebase to support hypothesis evaluation tools. The methodology we use is general and is in no way restricted to the specific knowledgebase employed in this case study. Future application of this methodology will enable us to compare pathway resources with respect to the generic properties such resources will need to possess if they are to support automated reasoning. There has been a tremendous increase in the volume and variety of data about biological systems and many biological databases have been developed for storing and querying the rapidly accumulating data. There aIn particular, knowledgebases which represent biological pathways in an abstracted, structured manner are being developed on a large scale -4. Such Knowledgebase developers realize the importance of providing reliable, complete, and consistent knowledgebases. For example, the pathway hole filler algorithm developed by Green et al attempts to find missing enzymes in automatically generated metabolic pathway databases and fillLocating, retrieving and integrating biological data have become increasingly burdensome tasks, leading to a growing need for tools that offer an organizing framework to facilitate the interpretation of biological data. We are The Reactome project is a collaborative effort involving the Cold Spring Harbor Laboratory, the European Bioinformatics Institute, and the Gene Ontology Consortium. Reactome is a knowledgebase containing representations of the core pathways and reactions in human biology, authored by expert biological researchers and maintained by the Reactome editorial staff. The basic unit of Reactome is the reaction, which Reactome defines as any biological event that converts inputs to outputs where thThe existing HyBrow prototype evaluates hypotheses on the event scale by checking the individual events and the logical combinations of events in each hypothesis for agreement with stored information. In the case of Reactome, we cannot check individual reactions, as they are provided "as is" by domain experts and the corresponding primary data are not stored. However, if we assume that the individual reactions are valid, we can check higher order properties for pathways. This exercise has dual benefits: we not only obtain a quality check for an important knowledgebase, but also develop methods that we can use in the future for principled evaluation of higher-order hypotheses. We propose logical proofreading as a pre-processing step to precede the use of a pathway database for other automated inference procedures. This would bring several advantages. For example, if a knowledgebase contains inconsistencies or gaps, automated pathway tracing can encounter ambiguities or dead-ends, causing problems for programs attempting to investigate possibilities for metabolic engineering. Logical proofreading facilitates human reasoning as well as automated reasoning. Human curation often encounters complications if multiple curators are involved. Logical proofreading can help by, for instance, identifying instances where two differing opinions exist, or where one curator assumed that a particular relationship was recorded elsewhere and thereby chose to record neither this assumption nor the original relationship.Biologists will increasingly need tools for thought, and so Using the event-level relationships  described in the methods section, we defined a set of testable desirable properties that we believe should hold for pathway-models stored in Reactome. These properties were formulated to identify errors that can cause problems for computer-aided information integration and inference tools such as HyBrow.well-formed if the direct precedence relation holds whenever the direct supply relation holds over the events in the sequence.1. We say that a sequence of events in a pathway representation is complete if every event in the pathway is either supplied by a preceding event or supplied and enabled by the axioms.2. We say a pathway representation is inconsistent if an event comes before another event in the pathway order, but the opposite is true in the precedence order.3. We say that an acyclic pathway representation is verbose if there exist events a and c such that (a ≺ c) in the precedence order, and there exist "extra" events ei, i ≥ 1, such that (a ≺ e1), (e1 ≺ e2) ... (ei ≺ c) in the pathway ordering.4. We say that a pathway representation is terse if there exist events a and c such that (a ≺ c) in the pathway ordering, and there exist "extra" events ei, i ≥ 1, such that (a ≺ e1), (e1 ≺ e2) ... (ei ≺ c) in the precedence order.5. We say that a representation pathway is gapped if there exist events in the pathway for which supply is violated, and there exist preceding events in the database which are capable of supplying those pathway events.6. We say that a pathway representation is unfounded if there exist cycles in the precedence order that are not also defined to be cycles by the pathway annotation.7. We say that a sequence of events in a pathway representation is In order for Reactome to become a reliable resource of core pathways and reactions for automated inference about human biology, it is greatly desirable that its representations satisfy completeness, consistency and gap-free properties:• Each pathway in the Reactome database should be complete. If a pathway is incomplete, a reasoner is unable to "backtrace" reactants to find their requisite precursors.• None of the pathways in Reactome should be inconsistent. If a pathway is inconsistent, it is possible for a reasoner to arrive at two conflicting event orders. In the absence of further information, the reasoner would not be able to tell which of two events could possibly be a cause for the other.• None of the pathways in Reactome should be gapped pathways. If a pathway contains gaps, a reasoner cannot verify the presence of all of the possible output entities that should be available at the end of the pathway.Of less crucial importance are directness, well-formedness and well-foundedness properties:• The directness criterion requires that as many pathways as possible should be adjusted so that they are neither verbose nor terse. This will enable reasoners that work with both the curated (pathway) and stored (precedence) data to get compatible information from both sources. This compatibility is essential if we want to "borrow" a pathway representation, based on precedence, from one organism and compare it to a curated pathway representation from another organism.• The well-formedness criterion states that every sequence of events in Reactome should be a well-formed sequence. If not, this is a cue that precedence data are missing.• The well-foundedness criterion mandates that there should not be any cyclical sequences in the database that are not also annotated to be cyclical pathways and that for no events should the precedence relation be reflexive. An unlabeled cycle in precedence or a reflexive precedence relation (a self-loop) can lead to non-terminating traces or require cutting the cycle at an arbitrary point.We developed scripts (available for download) to testDirect supply is often violated, due to missing events, the incomplete state of current experimental knowledge, and lapses in annotation.CDK-mediated phosphorylation and removal of Cdc6 (id 69017) has four events identified by ids: 69005, 69006, 69015, and 69016, of which the event, Cytoplasmic phosphorylated Cdc6 is ubiquitinated by the anaphase-promoting complex (id 69015), is unsupplied. This event requires Ubiquitin (id 113595) as an input, which is not supplied by any other reaction. Ubiquitin is also not identified as one of the concrete simple entities that are assumed to be present. To remedy this, Ubiquitin would have to be accounted for as a product of another reaction, or else it must be declared to be a concrete simple entity.For example, the pathway The concept of enabling is not yet tracked by Reactome, even though there are events that create physical entities which act as catalysts in other pathways. No events specifically modify or create catalysts for other events in a pathway.e, which precedes events that are instances of the generic event preceding e.Overall, Reactome adheres to its data format specification. However, generic entities are allowed to interact with concrete physical entities in generic pathways. This leads to peculiarities such as a generic event preceding a concrete event For example, the concrete reaction identified by id 111746 has the generic reaction 111742 as a preceding reaction. However, the concrete instances of this generic reaction  have reaction 111746 as their preceding event and thus we have 111742 ≺ 111746 ≺ .These reactions describe how the enzyme complex ribonucleotide reductase catalyzes the reduction of each of four nucleoside diphosphates, ADP, CDP, GDP, and UDP to the corresponding deoxynucleoside diphosphates, dADP, dCDP, dGDP, and dUDP. These reactions are represented by ids 111741, 111753, 111754, 111755. In the course of doing so, the enzyme complex itself is oxidized, and must be regenerated by reduction, before it can catalyze another round of ribonucleotide reduction. There are two ways of carrying out this reduction, one of which is in a reaction involving glutathione (reaction id 111746).Reduction of cytosolic ribonucleoside 5'-diphosphates to deoxyribonucleoside 5'-diphosphates (glutaredoxin) (id 111742) with four concrete instances  and linked the concrete regeneration event (id 111746) to this generic reduction reaction. This leads to the case where 111742 ≺ 111746 ≺ . We pointed this out to the Reactome team and they agreed that this was indeed a problem.The reactome curators created a generic reaction, named This situation should ideally have been annotated as four cycles involving the four concrete reduction events  and the concrete regeneration event for each case.To fix the problem, the curators decided to delete the "preceding event" link between the generic reduction reaction (id 111472) and the concrete regeneration reaction (id 111476), and replace it (as of version 14) with links directly from the concrete reduction to concrete regeneration events .Since we found that Reactome does not consider enabling, we decided to include an assumption of universal enablement.  Reactome also does not track the creation and use of concrete simple entities, so we had to assume the availability of such entities to avoid systemic judgments of incompleteness. We also assumed the presence of the input reactants to the first step in each pathway.After incorporating these assumptions, we were able to locate several pathways that were not complete. One such pathway is the inosine formation pathway.inosine formation pathway (id 74236) has two reactions: Adenosine + H2O → inosine + NH3 and inosine 5'-monophosphate + H2O → inosine + orthophosphate. (ids 74235 and 73822)The not part of the inosine formation pathway and the event that is listed in the pathway does not supply inputs to event 73822. Thus, the inosine formation pathway is not yet complete.The events that precede 73822 are 73797 and 76590. However, these events are Overall, we found that 8 of the 55 pathways in Reactome were incomplete, and that there were 14 events in those 8 pathways that were the cause of the judgments of incompleteness. Since there are 742 events in these 55 pathways, these 14 events constitute 2% of all events.Our tests identified a number of inconsistencies in Reactome. One example is the ornithine metabolism pathway.ornithine metabolism pathway (id 70693) has 9 events. Of these, event 70577 (ATP + aspartate + citrulline ↔ argininosuccinate + AMP + pyrophosphate) is listed directly before 70560 (carbamoyl phosphate + ornithine → citrulline + orthophosphate) in the pathway ordering. However, the preceding event of 70577 is 70560 according to the preceding event relationship specified in Reactome. This assertion is therefore inconsistent with the pathway ordering.The Eight of the 55 pathways had consistency problems and two of these are incomplete pathways. There were 23 reactions that were the cause of these inconsistencies and these 23 reactions comprise about 3% of all reactions.There were very few gapped pathways in Reactome. Of the 55 concrete human pathways, only 4 were found to have gaps. There were 5 events in release 10  which could supply the necessary patches to seal the gaps.glycogen breakdown in liver cells pathway (id 71598), which has 7 events. Event 71593 in the pathway requires{-alpha-glucosyl}poly{-alpha-glucosyl}glycogenin-2 as input, which is not provided by any of the previous events in the pathway. However, it can be supplied by event 71594, which is listed as its preceding event but is not considered part of the pathway.Consider the Our test for terseness revealed that only 3 of the 55 pathways were too terse and in each case, there was only one occurrence of terseness.glycogen breakdown in liver cells pathway (id 71598). There are two events between events 71591 and 71593 according to the preceding event relationship.Again, consider the Upon testing for verbosity, 18 of the 55 pathways in Reactome release 10 were found to be verbose.xanthine formation pathway (id 74257), event 74249 and 75251 directly precedes event 74255 according to the preceding event relationship. However, the xanthine formation pathway lists four events 74248, 74249, 74250, 74251 grouped in the guanine formation pathway (id 74252) as occurring either in parallel or before event 74255.Consider the We assert that verbosity is not that serious a problem, because excessive verbosity indicates that descriptions of pathways by domain experts can include extra steps for which evidence of the exact sequence of events is not explicitly specified or known. Although not optimal, this situation is not surprising for a biological process still under investigation.On testing for well-formedness between consecutive events in pathways, we found that 21 of the 55 Reactome pathways were completely well-formed and 25 pathways presented 80% or more of their constituent events in well-formed sequences (80% well-formed). Table Pyrimidine biosynthesis pathway (id 73648). Event 73629 produces all the required inputs for event 75124 and hence supplies event 75124. However, event 73629 is not considered event 75124's preceding event. This violates well-formedness.Consider the There were no concrete reactions that violated this property.Reactome itself is constantly changing and evolving. This raises the possibility of studying how pathways evolve over time as curation progresses. Table We are developing computer-aided information integration and inference tools such as the Hypothesis Space Browser (HyBrow),11. In oWe believe that evaluating pathway knowledgebases by proofreading and assessing their compatibility as well as a level of support for tools such as HyBrow is an important step toward the overall goal of using knowledgebases as effective resources for tools that perform information integration and computer-aided inference about biological processes.The reliability of a knowledgebase for supporting information integration and computer-aided inference can be estimated by the kinds of tests outlined in the current work. A reliable pathway knowledgebase should be free of internal conflicts, represent as many steps as possible in each pathway, and provide the most complete set of pathway descriptions possible. Omissions, inconsistencies, errors in the order of steps in a pathway, missing steps, and extra steps all limit the performance of a knowledgebase. We believe that a reliable knowledgebase should minimally be complete, consistent, direct, gap-free, well-formed and well-founded. In this work, we provided precise mathematical definitions for each of these necessary properties and presented a logical framework for assessing a knowledgebase's reliability. We applied our tests to the latest releases of a deployed pathway knowledgebase and thereby demonstrated how these tests can be used to proofread and monitor the quality of a knowledgebase while it is being developed.Reliability is, of course, not the whole story. To allow comparison of alternative representations of biological processes across organisms and databases, it is essential to have formal mechanisms to compare the richness and differences among representations of biological processes across existing knowledgebases. Even a completely reliable knowledgebase may lack the ability to support queries such as type-checking of stored relationships or be unable to capture certain kinds of biological relationships such as context dependent changes in the function of proteins.In future work, we will evaluate a spectrum of knowledgebases for the purpose of comparing their expressive power. The situation we face when comparing different knowledgebases is complicated by the fact that the knowledgebase languages may not be in one-to-one correspondence. One approach is to consider only the subset of the language that many knowledgebases can express in a common interchange format, such as BioPAX. Alternately, we may choose to formulate a definition of expressiveness for comparing the expressiveness of knowledgebases given their (possibly implicit) ontologies and associated logics over their distinct languages. Our model theory formalism allows for rigorous comparisons of expressiveness. However, further work is required to examine which particular approach will yield the most meaningful comparisons.We used a model theoretic approach to check a deployed and maturing knowledgebase with respect to its suitability for machine-aided reasoning. We identified a desirable set of logical properties a pathway knowledgebase should satisfy to support machine-aided reasoning, including consistency, completeness and other desirable properties. Checking these properties during the design stage can predict and eliminate errors, as we demonstrated in this work.We tested pathways from the available releases of Reactome for the satisfaction of our criteria. We implemented "proofreading" scripts to make our tests available for future use in the curation process. After reviewing our results, the Reactome team not only decided to make specific changes in response to errors we found, but also chose to incorporate our proofreading methods and scripts into their pre-release quality control process.In previous work, we developed a model-theoretic framework for representing and reasoning about biological systems,11. A loThe Reactome data format implicitly generates a formal language. Elements from this formal language can be used to express formal models. If we can represent the information in the Reactome knowledgebase with a collection of formal models representing reactions, pathways and collections of pathways, we can then perform model checking to proofread it. Specifically, we can identify desirable logical properties for models representing pathways and develop tests to perform model-checking and query evaluation to verify these properties.This approach allows us to cast knowledgebase verification in a broader context while building a strong connection to our previous work on computer-aided hypothesis composition and evaluation done by HyBrow. On the Reactome contains representations of both concrete and generic reactions, and this investigation focuses on the former. A concrete reaction is a reaction in which all inputs, outputs, and catalysts are concrete physical entities , and in which the conversion of inputs to outputs occurs in a single step.In addition to inputs and outputs, a reaction can include information on the organism, sub-cellular location and the experimental evidence for the reaction in the form of one or more literature citations, as well as catalytic activity and regulatory information. Reactome's definition of reaction encompasses classical biochemical reactions , as well as events such as binding, dissociation, complex formation, translocation and conformational changes.The defining attributes of a reaction are its input, output, and catalyst activity. Reactions which have identical substrates and products but different catalysts are represented as distinct reactions. Similarly, chemically identical entities in different cellular compartments are represented as distinct entities. For example, extracellular and cytosolic glucose are stored as separate entities. Entities in different biochemical modification states are also represented as separate entities. The p53 protein, for example, is represented by three distinct entities: native p53, p53 phosphorylated at Ser15 and p53 phosphorylated at Ser20. Such multiple state representations are derived from a single base entity – what Reactome calls the Reference Entity – which contains information common to all the states.Reactome events can also contain generic physical entities such as 'any tRNA'. Reactioevent to concrete reactions and use the term pathway for a concrete pathway. In order to specify a formal language for a logic, we need a set of functions, a set of relations and a set of constants.In this paper we restrict the use of the term L is a triple L = <F, R, C> where:For this work, a formal language F is a set of function symbols, each with positive integer arity,• R is a set of relation symbols, each with non-negative integer arity, and• C is a set of constant symbols.• The concepts defined by Reactome can satisfy the requirements of a formal language if we make the following translations of Reactome concepts.• Reactome's representations of physical entities provide the constant symbols of the formal language.• Reactions that transform one version of a physical entity into another version, form or dissociate complexes, or transport entities into different contexts form the functions of the formal language.• Reactions not otherwise specified as functions form relations in the formal language. In particular, temporal relationships stored by Reactome are expressed using the 'precedes' relation.The base event-level relationship in Reactome is temporal precedence, which is directly stored by Reactome for each reaction. We supplement the 'precedes' relation by defining a set of additional event-level relations that allow us to define testable properties at the pathway level.n to be directly supplied by event en-1 if the inputs of en are included among the outputs of en-1. We consider it partially supplied by event en-1 if at least one of the inputs of en is present among the outputs of en-1. According to Reactome, partially supplying the following event is a requirement for an event to precede another event.• We consider an event en to be indirectly supplied by events e1 ... en-1 if all the inputs of en are contained in the union of the outputs of the other events• We consider an event eSupply is the union of direct, partial and indirect supply.n to be enabled by event ei (or events e1...en-1) if all of the catalysts of en are found among the outputs of ei (or events e1...en-1).• We consider an event emodel (or L-structure) is a tuple M = <M, FM, RM, CM>, where M is a set called the universe containing the objects from L. The universe is closed under function operation. FM is a set of functions, RM is a set of relations, and CM is a set of constants in M.The language we derived from the Reactome data format and the event relationships defined above allow us to specify pathway-level models in the fWe focus our attention on finite models. These cFor Reactome pathway-models, the constants are individual Reactome events. We define the 'enable' and 'supply' relationships as above, and also use the 'precedes' relation as defined by Reactome. The universe for such models is the set of all Reactome events, together with the "initial conditions" which provide all of the entities assumed to be axiomatically present.M: = {a ∈ M: M| = φ(a)}). Finding all pathway-models that exhibit a specific property, such as being complete, is a query-evaluation problem. .Model-checking requires two inputs: a model and a formula. Given a language L, a model M and an L-formula φ, solving the model checking problem answers the question of whether φ is true in M. Given a model M and a formula φ (x) with free variables x, solving the query evaluation problem computes the relation defined by φ on M  as initial conditions.0, φ1, ..., φn such that φi is obtained from an axiom, or is obtained from φ0, ...,φi-1 through the use of a verification rule. Establishing verification rules as described above allows us to check pathway-models to determine whether or not they satisfy the definitions of the pathway-level properties described in the next section. For example, it is desirable that all of the agents required in a certain pathway be supplied either as initial conditions to that pathway or by events upstream.We define a verification to be a finite sequence φSR conceived the theory and developed the tests, NS programmed the tests and compiled the results, and all authors were involved in the writing and editing of this manuscript.


Many important agricultural traits such as weight gain, milk fat content and intramuscular fat (marbling) in cattle are quantitative traits. Most of the information on these traits has not previously been integrated into a genomic context. Without such integration application of these data to agricultural enterprises will remain slow and inefficient. Our goal was to populate a genomic database with data mined from the bovine quantitative trait literature and to make these data available in a genomic context to researchers via a user friendly query interface.The QTL (Quantitative Trait Locus) data and related information for bovine QTL are gathered from published work and from existing databases. An integrated database schema was designed and the database (MySQL) populated with the gathered data. The bovine QTL Viewer was developed for the integration of QTL data available for cattle. The tool consists of an integrated database of bovine QTL and the QTL viewer to display QTL and their chromosomal position..We present a web accessible, integrated database of bovine (dairy and beef cattle) QTL for use by animal geneticists. The viewer and database are of general applicability to any livestock species for which there are public QTL data. The viewer can be accessed at  Many important agricultural traits such as weight gain, milk fat content and intramuscular fat (marbling) in cattle are quantitative traits. While significant information regarding the mode of inheritance of these traits is available, most of this information is not integrated into a genomic context. As large amounts of genomic sequence data become available (estimated completion time of the bovine genome sequence is early 2006), they require livestock genome researchers to integrate sequence data not only with existing gene maps, but more importantly with Quantitative Trait Locus (QTL) and phenotype data. Without integration, application of these data to agricultural enterprise productivity will remain slow and inefficient. In order to facilitate this overall integration, there is a requirement for an integrated database of QTL of cattle and an analytical tool for this database such as a visualization component.While there are livestock genetic map viewers ,2 and atThe QTL data and related information for bovine QTL are gathered from published work and from existing databases such as INRA BOVMAP  and USDAThe database is independent of the viewer, which dynamically displays the query results (using the USDA MARC map as a the reference map) as graphical representations of chromosomes highlighted in the QTL regions Fig. . BecauseThe user can then select a particular chromosome to view in detail by clicking on it. This chromosome view provides a detailed view of all QTL reported in the genome view generated previously. The user can go directly to the chromosome view by selecting a single chromosome at the time of the initial search. Individual QTL can then be viewed in detail Fig.  and at tAs can be seen from the Entity-Relationship (ER) diagram Fig. , links tThe ER diagram consists of these entities:• QTL information, including all the details of each QTL• Marker information, including all the details of each marker• References, including the complete list of references• Trait information, including the details of the traits of the cattle• Category information, which gives details about the categories into which traits are divided• Chromosome information, which has the length of each of chromosome measured in linkage units (Morgans).The database is modeled such that each trait can fall under multiple categories. In addition, each QTL and marker record can have more than one reference. Finally, the QTL information and marker information are not linked directly. These data are linked dynamically by the QTL Viewer based on the position of the marker and the position of the QTL on the chromosome. This provides flexibility when the database is updated, allowing marker information and QTL information to be updated independently of each other.Data can be added or edited using a set of web based tools that simplify database administration. Database administrators can add single QTL or easily modify QTL attributes without resorting to the command line. This makes updating the database possible by less skilled individuals such as students or technicians who may not have the training to mine the data out of the literature or have admin access to MySQL. Security settings are such that it is possible to assign users limited admin privileges over their own data, enabling them to submit and edit data but not delete QTL entries. User submitted data can be kept private and not accessible to other users until a user is ready to divulge the data for public access. This feature has been implemented to allow users to directly submit data prior to publication, allowing them to view their data in the context of public data in a genomic context.By ensuring the QTL records and the map information are not linked directly we make it easy to migrate the viewer to additional maps (integrated maps with arbitrary map units or RH maps) that also include map information for the QTL markers of interest. This can provide genomic visualization free from distortions caused by recombination hotspots (in the case of linkage maps) but more importantly allow QTL regions to be viewed on the best possible map, not just the map the QTL were identified from.The QTL literature is constantly being added to, so a strategy to keep the QTL database is a necessity. At present we have settled on manual, periodic literature searches to identify new publications that we can mine for QTL data. It would be advantageous if journals that publish significant numbers of bovine QTL studies could be persuaded to require submission to our database a requirement as is done for GenBank. We hope that as our site becomes more widely used, that journals will require or at least strongly encourage authors to submit their QTL data directly to us.While there are a number of livestock genomics web sites, there is only one other site that incorporates a dynamic, genome wide display of QTL data . We haveHardware requirement, any UNIX/LINUX/OSX server. Software required includes Apache, MySQL and PHP. The database and viewer code are available upon request.P. Polineni: designed the user interface, MySQL database and wrote the PHP code for the alpha and beta versions. Entered the original data set and contributed to the writing of the manuscript draft.P. Aragonda: implemented refinements to the database and designed and wrote the web based data entry interface for admin users. She also entered a significant amount of the data.S.R. Xavier: performed system administration duties, made sure database and viewer code was ported from the Linux development machine to the production machine and implemented refinements to the PHP code.R. Furuta: contributed significantly to user interface design.D.L. Adelson: Conceived the project, identified the data (literature/web sites), designed the data mining procedures, trained readers who mined data, contributed to interface design and database schema. Principal author of the manuscript.


Equally important and challenging as genome annotation, is the subsequent classification of predicted genes into their respective pathways. The Kyoto Encyclopedia of Genes and Genomes (KEGG) represents a database consisting of known genes and their respective biochemical functionalities. Although accessible online, analyses of multiple genes are time consuming and are not suitable for analyzing data sets that are proprietary.Presented here is a new software solution that utilizes the KEGG online database for pathway mapping of partial and whole prokaryotic genomes. PathwayVoyager retrieves user-defined subsets of the KEGG database and stores the data as local, blast-formatted databases. Previously selected datasets can be re-used, reducing run-time significantly. Whole or partial genomes can be automatically analyzed using NCBI's BlastP algorithm and ORFs with similarities below the user-defined threshold will be marked on pathway maps. Multiple gene hits are sorted by similarity. Since no sequence information is transmitted over the Internet, PathwayVoyager is an ideal solution for pathway mapping and reconstruction of confidential DNA sequence data.PathwayVoyager represents an alternative approach to many already existing, more complex pathway reconstructions software solutions. This software does not require any dedicated hardware or software and is flexible and straightforward to use. It is ideally suited for environments where analyses on variable datasets are desired. EC classification), these genes can be marked in corresponding KEGG pathways. However, analysis of larger numbers of genes using this manual approach is tedious, inflexible, and time consuming. Since un-encrypted data transmission is used for most public servers, remote Blast analyses with confidential sequence queries is not desirable. In addition to this web-browser based approach, the KEGG database can also be accessed directly via an application programming interface (API) and the underlying databases can be downloaded for local uses. Third party-software most commonly use the KEGG database for gene-classifications, often in combination with whole genome annotation efforts [ utilizes dedicated server and database backbones to realize a sophisticated environment. New genomes must be manually integrated into a Pathway/Genome Database (PGDB), which in turn sets the basis for more complex queries and analyses. However, the significant resources required to implement genome information for Pathway Tools might not be readily available. PathFinder [The ongoing sequencing of complete genomes of prokaryotes and eukaryotes reveals a tremendous amount of uncharted data. In prokaryotic genomes, roughly 25 to 30 percent of the predicted ORFeome remain functionally unknown with many Open Reading Frames (ORFs) only showing similarities to conserved hypothetical ORFs of other organisms. However, a significant number of the predicted ORFs do show similarities to functionally classified genes with defined roles in the complex network of metabolic pathways. Previously, the most common approach to determine the functionality of a gene and its gene product was to experimentally determine phenotypic changes upon inactivation or overexpression of the gene. This is the most effective approach for determining the roles of single genes, but it is unfeasible to investigate complete ORFeomes with over 2000 ORFs. The Kyoto Encyclopedia of Genes and Genomes (KEGG) represents an ambitious and successful attempt to assign known enzymes into known biochemical pathways and is updated on a regular basis -3. The d efforts , or util efforts  integratthFinder  and BioMthFinder  use a diClosely-related groups of organisms may differ in certain key elements that define specific strain/species related differences. Comparing these organisms or groups of organisms with each other should highlight these differences and reveal specific properties or lead to new genetic targets for pathway engineering. Therefore, it may be desirable to choose only subsets of organisms and pathways from the overall KEGG database content.PathwayVoyager was developed to overcome most of these obstacles. The software resembles a tool to analyze an unlimited number of protein sequences against a user-selected subset of the KEGG database using NCBI's BlastP algorithm and subsequently places them into their proper pathway positions. Results are displayed in colored pathway maps and hits can easily be analyzed using the graphical interface. This tool reflects a different approach to pathway mapping, in that it provides a simplistic and flexible approach with few prerequisites. No dedicated hardware (i.e. background server) or software  are necessary to analyze given datasets. A standard PC with the Windows operating system is sufficient to operate PathwayVoyager. In contrast to more complex tools, no underlying protein annotation is necessary and plain protein sequences in FASTA format can be used as query templates. This approach is ideal for draft phase genomes and ongoing annotation efforts in completed genomes where the emphasis lies on the establishment and verification of gene annotation and an initial assessment of metabolic capabilities. The resulting main advantage of PathwayVoyager is its speed and economy for initial pathway mapping. Also, the resulting data can easily be accessed on different locations by transferring the generated flatfile database to the respective computers. Once the research objective shifts to comparative and predictive pathway analyses, other tools like DAVID or PathwayTools become more advantageous. PathwayVoyager fills a niche for environments with limited hardware and software resources that still require a significant and meaningful way to perform small and large scale pathway mapping projects from varying data sources.. No further Perl modules are required. However, two external distributions are required, namely the NCBI Blast distribution , and the SOAP::Lite client  to utilize the KEGG API. Perl/Tk provides the interpreter for PathwayVoyager, and the SOAP::Lite client facilitates interaction with the KEGG API. The standalone Blast distribution is used to generate Blast compatible databases and to perform the local Blast analyses. For data analysis and browsing, the Perl/Tk interpreter is the only pre-requisite for PathwayVoyager. The standalone Blast distribution, and the SOAP::Lite client can be omitted and no internet connection is necessary.PathwayVoyager is written completely in Perl/Tk and requires the Perl interpreter .The software was developed to optimally complement the GAMOLA annotation suite  but acceThe general procedure used by PathwayVoyager to map genes is illustrated in Figure The graphical user interface was designed to be self-explanatory and easy to use. After the initial pathway setup, no further installation steps are necessary. Although PathwayVoyager requires an internet connection in order to retrieve data from the KEGG database, all analyzes involving the provided gene sets are performed locally and no sensitive data are transmitted. This eliminates one of the major security concerns when working with confidential data and permits the real-time use of the KEGG database system. PathwayVoyager does not require any dedicated hardware and has been tested on a standard PC and the Windows platform. Linux versions of Perl/Tk, the SOAP::Lite client, and the standalone Blast distribution are freely available and would allow PathwayVoyager to operate under a Linux environment, as well. For certain selectable pathways  KEGG does not yet support organism independent marking. For practical reasons, no hits will be displayed for these pathways.Upon start, the main window displays two list-boxes and the currently active buttons Figure  commenceThe user-selected organism and pathway combination is shown in a separate pop-up window (not shown). The current status of the KEGG pathway mapping is also shown in a separate log-window (not shown). In general, the right panel Figure  harbors The possibility to re-use previous selections dramatically reduces the time needed to complete KEGG analyses, as retrieval of individual protein sequences from KEGG is omitted. In addition, Blast results obtained with the given query set can also be re-used. This shortens the run time further, enabling rapid mappings and analyses of pathways with varying relaxed or stringent threshold values.The provided gene set will then be compared to the local database generated from the selected organism-pathway protein sequence combination using the BlastP algorithm. Blast hits featuring an e-value below the user-selectable threshold will be used to generate the marked KEGG pathway requests. Pathway maps are saved as GIF files and the URL for the respective KEGG pathway map including the corresponding BlastP results are stored separately in text files.L. acidophilus NCFM [Results are displayed in a separate window. Figure lus NCFM  as queryIn general, previously selected pathways are displayed by either their KEGG pathway code or full name. Alternative analyses can be displayed by changing the default mapping directory, using the "Directory" function Figure . The selL. acidophilus below the defined threshold . Selecting this entry from the EC entry list Figure , highligd Figure . Two entPathwayVoyager does not evaluate or extrapolate the displayed hits and the quality and significance of the results depend on the current content of the KEGG database. As with every predictive software, results should be carefully analyzed and seen in their genetic context to evaluate activities and potential substrate specifity-variances carried out by homologous enzymes. Results from previous analyses can be displayed by selecting the "View existing KEGG pathways" option in the PathwayVoyager main window Figure .Run times for PathwayVoyager may vary, depending on the number of selected pathways and organisms. Analysis of a complete genome of ~2,000 open reading frames (ORFs) using the complete KEGG database can be carried out in less than 36 h.PathwayVoyager differs significantly in its approach from other software solutions for pathway reconstructions that already exist. In contrast to the often highly complex and specific algorithms, PathwayVoyager represents a more straight-forward approach, and doesn't require substantial resources on the users' side. Relying on the Blast algorithm and the ambitious KEGG database, PathwayVoyager utilizes widely accepted resources to analyze and map data. Despite the uncomplicated approach, evidential data can rapidly be obtained and easily analyzed during genome analyses. PathwayVoyager represents an effective pathway mapping tool for large or confidential data sets.Project name: Biological Pathway Mapping• Project home page: none• Operating system(s): Platform independent• Programming language: PERL• Other requirements: Active Perl 5.8, SOAP::Lite client, NCBI's Blast distribution• License: The software is distributed for free under the NC State University copyright and can be obtained upon request to the authors.• Any restrictions to use by non-academics: none• Lactobacillus acidophilus NCFM. TRK read and approved the manuscript and provided financial support for EA and the project.EA developed and tested the complete PathwayVoyager software and performed the described pathway reconstruction for


Structural and functional research often requires the computation of sets of protein structures based on certain properties of the proteins, such as sequence features, fold classification, or functional annotation. Compiling such sets using current web resources is tedious because the necessary data are spread over many different databases. To facilitate this task, we have created COLUMBA, an integrated database of annotations of protein structures.COLUMBA currently integrates twelve different databases, including PDB, KEGG, Swiss-Prot, CATH, SCOP, the Gene Ontology, and ENZYME. The database can be searched using either keyword search or data source-specific web forms. Users can thus quickly select and download PDB entries that, for instance, participate in a particular pathway, are classified as containing a certain CATH architecture, are annotated as having a certain molecular function in the Gene Ontology, and whose structures have a resolution under a defined threshold. The results of queries are provided in both machine-readable extensible markup language and human-readable format. The structures themselves can be viewed interactively on the web..The COLUMBA database facilitates the creation of protein structure data sets for many structure-based studies. It allows to combine queries on a number of structure-related databases not covered by other projects at present. Thus, information on both many and few protein structures can be used efficiently. The web interface for COLUMBA is available at  Biological databases have become a major resource for researchers in life science. With the constantly increasing number of data deposited and the computational tools evolving, the focus of research has shifted from the study of a single gene towards an intra- and inter-species comparison of genes and gene products. This trend can also be seen in the field of structural biology, where the number of protein structures deposited in the Protein Data Bank, PDB  is increOberg and colleagues  comparedResearchers have several possibilities to collect information on protein structures. First, entries in the PDB itself contain a set of full text information and often are annotated with links to external data sources. However, PDB entries are not curated, only archived by the PDB team. This has two consequences. First, the data are not constantly updated and therefore quickly become out-of-date. Second, the annotation provided by different submitters is highly heterogeneous and does not follow a standardized nomenclature. As a consequence, searching the PDB for annotations is an error-prone task. Annotations may be incomplete or inconsistent with standard nomenclature, spelling errors and uncontrolled usage of abbreviations prevent an efficient textual search, and literature references or links to functional and structural databases may be outdated or missing. Examples of such problems are described in . This laTo overcome this problem, we created COLUMBA, a database of information on protein structures that physically integrates information from twelve protein structure related data sources into a single data warehouse. Besides the protein structures themselves, COLUMBA covers structural and sequence-based classification schemes, functional annotation, secondary structure elements, and participation in metabolic pathways. Links between these data and the protein structures, both on the chain, compound, and entry level, are either taken from the second-party databases or are computed inside COLUMBA, leading to more accurate and more current information than available in the PDB itself and as current as possible, we compute links between chains and Swiss-Prot entries based on sequence similarity, thus cross-referencing 68% of the PDB entries to a Swiss-Prot sequence.COLUMBA is centered around PDB entries . For eacTo gain information about protein domains, entries from the protein-fold classification databases SCOP  and CATHAll data sources integrated into COLUMBA describe specific aspects of either PDB entries itself, their compounds, or their chains. We never mix data from different data sources with each other. This partitioning is directly reflected in the database schema  to retrieve information.A research question concerning the participation of enzymes in metabolic pathways arose from an article from Martin et al.  that invEach KEGG pathway consists of a number of enzymes related to a PDB compound. Those compounds are linked to the respective chains, which in turn are cross-referenced to CATH classes, architectures, and topologies. We computed the number of occurrences of CATH classes for the set of enzymes in a pathway containing more than 10 enzymes and having a coverage of at least 50% with PDB structures that are also annotated in CATH. For all qualifying pathways the figures are given in Table The first striking fact is that only 26% of the enzymes participating in KEGG pathways do have annotated chains in CATH. This is because just 34% of the enzymes in KEGG are structurally resolved, of which several are not annotated by CATH. The enzymes within the annotated set contain four times as many domains with an Alpha/Beta class in CATH than Mainly Alpha and Mainly Beta, respectively. In comparison, for all proteins annotated by CATH the Alpha/Beta class only occurs twice as often as each of the other two folds.In Figure The most frequent approach to the interconnection of data on protein structures that are spread over multiple original data sources is the usage of hyperlinks. Examples are PDBsum  and the Two groups currently address the problem of inconsistent use of terminology in the PDB: the PDB uniformity project  and the COLUMBA currently integrates twelve data sources concerned with different aspects of protein sequences and structures. Notably, COLUMBA does not store the coordinates of structures themselves but is designed to enable users to find 'the right' set of structures based on annotations. This is by intention, since there are already many programs that can efficiently parse, visualize, or compare protein structures from PDB files.An important design principle of COLUMBA is that it never mixes data from different sources into a single table. Each data source is considered as a dimension in which PDB entries, compounds, and chains are annotated. We call this approach multidimensional data integration , which iOur approach is in contrast to projects that aim at a tighter semantic integration, merging logically similar types of information into a single table. Such a semantic integration approach was for instance followed in the TAMBIS project . HoweverWe believe that the advantages of our approach prevail for mainly two reasons:• Users recognize the origin of the data they query and obtain as result. In our experience, biologists often have their favorite set of databases, where they know about the pitfalls and peculiarities. By keeping data separated, personal preferences or differences in trust in particular databases can be expressed and the results can be judged based on prior experience.• Subtle differences in the semantics of fields of different databases are conserved. For instance, both Swiss-Prot keywords and GO annotations express functional annotation. However, the process of creating this annotation is quite different, and it is often meaningful to discriminate between the two.Furthermore, separating data and software for the different data sources greatly simplifies system maintenance. Changes to data sources, including the deletion or addition of data sources, only affect a well defined part of the schema and of the web interface.Our perception of considering annotation sources as dimensions describing some primary objects is also followed in the EnsMart project . EnsMartCOLUMBA has proven to be very useful for a number of tasks in our own structural research. Generating sets of structures, which previously required days of manual browsing or writing of parsers, now only takes a few mouse clicks, or an SQL query. Once the set of PDB entries and chains is obtained, there are many other programs for visualizing or comparing structures. COLUMBA's future development will further concentrate on annotation of structure in contrast to the structure and its coordinates itself. The next data sources to be integrated are those covering protein domains and motifs, i.e., InterPro  and its .The database is available at ST designed and implemented the web-interface. KR was responsible for the implementation of the annotation workflow. HM helped importing PDB data. UL designed the overall architecture and co-supervises the project. TS maintains the server infrastructure. IK provided data on the classification of protein folds, and RP on protein ligands. CF co-supervised the project and was the main source of motivation for building an integrated protein annotation database.


We present an effective, rapid, systematic data mining approach for identifying genes or proteins related to a particular interest. A selected combination of programs exploring PubMed abstracts, universal gene/protein databases , and state-of-the-art pathway knowledge bases  was assembled to distinguish enzymes with hydrolytic activities that are expressed in the extracellular space of cancer cells. Proteins were identified with respect to six types of cancer occurring in the prostate, breast, lung, colon, ovary, and pancreas.The data mining method identified previously undetected targets. Our combined strategy applied to each cancer type identified a minimum of 375 proteins expressed within the extracellular space and/or attached to the plasma membrane. The method led to the recognition of human cancer-related hydrolases , among which were prostatic acid phosphatase, prostate-specific antigen, and sulfatase 1.The combined data mining of several databases overcame many of the limitations of querying a single database and enabled the facile identification of gene products. In the case of cancer-related targets, it produced a list of putative extracellular, hydrolytic enzymes that merit additional study as candidates for cancer radioimaging and radiotherapy. The proposed data mining strategy is of a general nature and can be applied to other biological databases for understanding biological functions and diseases. Recent advances in genomics and associated high throughput technologies have resulted in the exponential growth of biological databases. These consist of annotated genomic databases such as those at NCBI Genomic Biology , EnsemblDeriving biological insights from these unprecedented quantities of data is a challenge -16. In rWe present a systematic strategy for data mining that aims to address the above-mentioned problems Figure . It is b® . Surprisingly, neither UniProt nor Entrez Gene (both of which are large depositories of information) states that these enzymes are related to cancer. In our data mining findings, alkaline phosphatase is listed in LSGraph under different isoenzymes  in all six tissue types studied. However, ALP is subsequently lost within the IPA step of the data mining process due to missing annotation in the original gene/protein databases as well as its being placed within the IPA «cell-cell interactions» network and not the «cancer» network. Therefore, unless entries are thoroughly investigated one by one, a search using simple keywords in gene or protein databases will not identify alkaline phosphatase as an extracellular cancer-related enzyme.et al. [B) would not have identified this sulfatase whose recognition within our hit list is consequent to the utilization of a combination of programs and the search within the space of functionally-related proteins.One of the new hits and a potential EMCIT target identified by the present strategy is a human enzyme, extracellular sulfatase 1 (SULF1) -53. Thiset al. . SimilarAnother EMCIT candidate that has been established is prostate-specific antigen (PSA), a well-known and clinically useful prostate cancer marker that has proteolytic activities. This molecule, also known as kallikrein 3 (KLK3), is secreted in the extracellular space Figure .in silico modeling simulations of their complexes with putative drugs. The prerequisite for molecular modeling of protein-ligand complexes is the availability of the three-dimensional (3D) structure for the target in the Protein Data Bank (PDB) or, in its absence, the building of a 3D-structure homology model. In our case, when these targets were checked for 3D-structure availability in the PDB, it was found that both PAP and ALP have already been crystallized  whereas the structures of human PSA (in contrast to that of the horse homologue) and SULF1 have not yet been solved.As mentioned above, our interest in the identification of extracellular hydrolytic enzymes, such as prostatic acid phosphatase, alkaline phosphatase, sulfatase 1 and prostate-specific antigen, is driven by our focus on finding novel, radiolabeled molecules that are excellent substrates for such enzyme candidates, as these could be used to hydrolyze the radiolabeled prodrugs for our EMCIT approach. These enzymes can be further studied by We have developed a new, rapid, data mining strategy that combines the literature, web-based gene-product databases, and complex knowledge pathway databases. The strategy, which is based on a combination of curated knowledge pathway bases with protein databases, has revealed the unique characteristics of several programs, including the entity retrieval capability and the Gene Ontology term-filtering specification of LSGraph and the full-text-based knowledge of IPA. The complementarity of content and functionalities of the query tools in the knowledge bases help to ascertain the identified hits as well as to discover other related neighbors of the known targets. We believe that this strategy overcomes many data mining drawbacks.The application of this strategy to the search for extracellular hydrolases involved in human cancers has led to the discovery and identification of several previously "hidden" proteins that would not have been detected by a search using a single information source. Our findings indicate that the approach benefits from the complementarity of the databases and query tools employed and can distinguish useful and previously uncharacterized cancer-related targets with additional characteristics  valuable in the noninvasive radiodiagnosis and treatment of cancer. Alkaline phosphatase (various cancers), prostatic acid phosphatase and prostate-specific antigen (prostate cancer), and extracellular sulfatase 1 (pancreatic cancer) are four interesting results. While these are being used in our laboratory as potential targets in the design of novel, radiolabeled diagnostic and therapeutic agents for cancer, the strategy for data mining is of a general nature and can be applied to other biological databases for understanding biological functions and diseases.A), knowledge pathway databases (B), annotated gene/protein databases (C), and manual review (D).A graphical representation including the steps of the strategy of our data mining method is presented in Figure A) was done using PubMed and the RefViz™ (version 1.1.0) [® [The literature search (n 1.1.0)  and Quosn 1.1.0)  applicatn 1.1.0) , Web of .1.0) [® , and othB) used the pathway analysis programs LSGraph  [® Systems, Mountain View, California, U.S.A.) [The functional relationship, knowledge-base search ( France)  and Inge U.S.A.) . LSGraphall entities in the current literature, ranked in order from most to least cited.This application performs advanced bibliographic queries in the abstracts of PubMed by desired keyword («filter» function) and carries out exhaustive searches due to the systematic coverage of synonyms and homonyms. LSGraph can also interpret large-scale experimental datasets (microarrays). Its «Most cited entities» function allows the retrieval of the entities that are discussed most frequently in abstracts selected by «filter» function keyword and exports them into the user-defined set . In our case, we set this function so that we retrieved Additional LSGraph function «Best GO» of the Gene Ontology can identify and rank all GO terms associated with the entities and subsequently downsize the list based on the GO term of choice. The Gene Ontology (GO) project is a collaborative effort that aims to address the need for consistent descriptions of gene products in different databases . The GO This is a web-based software application that allows users to identify the biological mechanisms, pathways, and functions most relevant to a dataset of genes or proteins of interest. It is founded on the largest knowledge base of biological networks created from millions of expert-curated relationships between genes, proteins, cells, tissues, drugs, and diseases. The networks linked to cancer are generated from the full text of articles (contrary to LSGraph) published in the most important scientific journals. Imported gene identifiers (here from LSGraph) are mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base (IPKB). These genes, called focus genes, are overlaid onto a global molecular network developed from information contained in the IPKB. Networks of these focus genes are then algorithmically generated based on their connectivity. The Functional Analysis of IPA identifies the biological functions and/or diseases that are most significant to the data set (e.g. cancer).C) was performed using the world's largest collections of genes and proteins, UniProt [The gene/protein database search  was performed on the full text of publications identified in PubMed queries directly or indirectly through online links to references in gene/protein databases.Manual review of the literature  Using analysis tools incorporated in LSGraph, we filtered the complete corpus of the PubMed down to abstracts that contain evidences of entity-to-entity relationship and are related to one of the six tumor-tissue types. The keywords "cancer" or "tumor" were not used in the query, but later in IPA (see (v)), because the evidence for the disease at this level of the search may not necessarily be known. Instead, the tissue names, prostate/prostatic, breast, lung/pulmonary, ovary/ovarian, colon/colonic, pancreas/pancreatic, which are present in the abstracts, were used in the searches. To ensure that we would not miss studies on model organisms such as the mouse, the search was not limited to humans.(ii) The LSGraph function «Most cited entities» was used to extract all entities present in retrieved abstracts defined by the «filter» keyword and order them from the most cited to the least cited entity.viz. UniProt accession numbers.(iii) Entities with localization specified by cellular component, «extracellular region», «membrane», of the Gene Ontology database (LSGraph «Best GO») were saved in a new set containing protein identifiers, (iv) The set with all UniProt identifiers was imported into the IPA and thus enlarged by related entities  Figure .etc.) and not for the pathological process of interest, i.e. cancer. From this point on, the entities were sifted down to the set known to be involved in cancer by selecting the IPA subnetworks in which cancer occurs as the most significant annotation. Figure (v) The functional analysis of IPA can identify the biological functions and/or diseases that are most significant to the dataset. Genes from the dataset found to be associated with "cancer" were considered for further analysis Figure . Up to t(vi) The final set of filtered entities was exported and stored in a Microsoft Excel spreadsheet see  containiA) and the single gene/protein database method (C).(vii) The identified extracellular proteins that were retained by the above-mentioned criteria were filtered by IPA protein family (defined by Entrez Gene). The final count of hits was compared with the number of hits identified by the literature search method  , a progrThe proposed method is rapid, and the protocol can be performed within several days. Steps (i) – (iii) are automatized searches where only the filtering keyword entries or GO term entries are required. Steps (iii) – (v) as well as step (vii) require the manual export/import of the UniProt identifiers from LSGraph into IPA and from IPA into Microsoft Excel file format. Resulting proteins hits with enzymatic activities were confirmed manually by checking the associated literature references; hydrolases were confirmed among "Enzymes," "Peptidases," "Phosphatases," and other IPA families. Particular cases (see Results and Discussion) were consulted in original publications.in vitro results. All authors read and approved the final manuscript.PP carried out the data mining work and drafted the manuscript. LKI conceived the study design, did preliminary data mining work, and helped to draft the manuscript. LKI and AIK initiated the work. AIK together with SJA participated in the design and coordination of the work, as well as interpretation of experimental Data mining hit list for six common cancer types. This file contains detail information about gene products identified in six tumor-tissue types. The genes are listed in six separate Excel spreadsheets by gene name, synonyms, description, UniProt accession codes, and IPA-based cellular location and family.Click here for file


Both intrinsic structural and phylogenetic features, as well as information relating to the interactions of the epitopes with the host's immune system will be catalogued.Epitopes can be defined as the molecular structures bound by specific receptors, which are recognized during immune responses. The Immune Epitope Database and Analysis Resource (IEDB) project will catalog and organize information regarding antibody and T cell epitopes from infectious pathogens, experimental antigens and self-antigens, with a priority on NIAID Category A-C pathogens (.To effectively represent and communicate the information related to immune epitopes, a formal ontology was developed. The semantics of the epitope domain and related concepts were captured as a hierarchy of classes, which represent the general and specialized relationships between the various concepts. A complete listing of classes and their properties can be found at The IEDB's ontology is the first ontology specifically designed to capture both intrinsic chemical and biochemical information relating to immune epitopes with information relating to the interaction of these structures with molecules derived from the host immune system. We anticipate that the development of this type of ontology and associated databases will facilitate rigorous description of data related to immune epitopes, and might ultimately lead to completely new methods for describing and modeling immune responses. Epitopes of human and non-human primates, rodents, and other species for which detailed information is available will be included. It is envisioned that this new effort will catalyze the development of new methods to predict and model immune responses, will aid in the discovery and development of new vaccines and diagnostics, and will assist in basic immunological investigations.An epitope can be defined as the molecular structure recognized by the products of immune responses. According to this definition, epitopes are the specific molecular entities engaged in binding to antibody molecules or specific T cell receptors. An extended definition also includes the specific molecules binding in the peptide binding sites of MHC receptors. We have previously described  the gene).The IEDB will catalog structural and phylogenetic information about epitopes, information about their capacity to bind to specific receptors , as well as the type of immune response observed following engagement of the receptors . Intrinsic features of an epitope are those characteristics that can be unequivocally defined and are specified within the epitope sequence/structure itself. Examples of intrinsic features are the epitope's sequence, structural features, and binding interactions with other immune system molecules. To describe an immune response associated with a specific epitope, context information also needs to be taken into account. Contextual information includes, for example, the species of the host, the route and dose of immunization, the health status and genetic makeup of the host, and the presence of adjuvants. In this respect, the IEDB project transcends the strict boundaries of database development and reaches into a systems biology application, attempting for the first time to integrate structural information about epitopes with comprehensive details describing their complex interaction with the immune system of the host, be it an infected organism or a vaccine recipient -3.For these reasons, it was apparent at the outset of the project that it was crucial to develop a rigorous conceptual framework to represent the knowledge related to the epitopes. Such a framework was key to sharing information and ideas among developers, scientists, and potential users, and to allowing the design of an effective logical structure of the database itself. Accordingly, we decided to develop a formal ontology. Over the years, the term "ontology" has been defined and utilized in many ways by the knowledge engineering community . We willSeveral existing databases catalog epitope related data. We gratefully acknowledge that we have been able use these previous experiences in the design and implementation of the IEDB. MHCPEP , SYFPEITThe ontology developed for IEDB and described herein complements two explicit ontologies that are presently available: the IMGT-Ontology and the Gene Ontology (GO). The IMGT-Ontology  was creaThe IEDB is being developed as a web-accessible database using Oracle 10g and Enterprise Java (J2EE). Industry standard software design has been followed and it is expected that IEDB will be available for public users by the end of 2005. was used to design and document the IEDB ontology. Protégé is a free, open source ontology editor and knowledge-base framework, written in Java. It provides an environment for creating ontologies and the terms used in those ontologies. Protégé supports class, slot, and instance creation, allowing users to specify relationships between appropriate entities. Two features that IEDB ontology effort used extensively were Protégé's support for creating ontology terms and for viewing the term hierarchies and the definitions. The support for a central repository on ontologies, along with browsing support, is key in reviewing and reusing ontologies.Protégé ), which allows interoperability with other ontologies.While there are several open source tools available  for deve. Herein, we report a detailed description of the novel aspects of the IEDB ontology. In designing our application architecture, we have followed the common system engineering practice of first determining the scope and nature of the data involved. A first essential step is to understand the semantics of the domain and to capture that knowledge in an agreed-upon format. Arranging the domain concepts in a taxonomy is one of the initial organizing steps in the ontology design process. The class hierarchy represents the generalization and specialization relationships between the various classes of objects in a domain [We have previously described some of the general concepts relating to the IEDB design ,2. More a domain . BrieflyOur approach for creating the class hierarchy was a top-down development process where we defined each class in a domain and then identified its properties before building the hierarchy. The main classes identified for IEDB are Reference, Epitope Structure, Epitope Source, MHC Binding, Naturally Processed Ligand, T Cell Response, and B Cell Response Figure . The Epi"Reference" is the class encompassing information related to the data source from which an epitope and its related information are extracted into the IEDB. We have identified three broad subclasses of References that describe where epitope information will be obtained. They are Journal Article, Patent Application, and Direct Submission. The complete listing of slots (fields) encompassed by the Journal Article, Patent Application, and Submission classes are provided in Figure The Epitope Structure and Epitope Source classes capture intrinsic features of an epitope. The Epitope Structure class captures the physical and chemical features of an epitope. Virtually any molecular structure may provoke an immune response, such as proteins, carbohydrates, DNA, and lipids. In the Epitope Structure class, structural information relating to linear sequences and 2-D structures of the epitope, if available, are catalogued. The Epitope Source class captures the phylogenetic source of an epitope, including species of origin, gene name, protein name, and links to other databases for more detailed information about proteins and genes. Figures The experimental data and information about specific experiments and the methodology utilized are captured in the Assay Information class. The name of the assay used, the type of response measured in the assay, and the readout of the assay are examples of information captured in the Assay Information class. This important class is used as a superclass of several other classes (and thus its properties are inherited by those classes). A complete listing the properties (slots/fields) in the Assay Information class is shown in Figure As with Assay Information, the classes Immunization, Antigen, and Antigen Presenting Cell are used in multiple other class descriptions. Features relating to the induction of the immune response are captured in the Immunization class Figure . It has Similarly, antigens are defined as the whole molecules that react with the products of an immune response . Information relating to the antigen and any associated carrier molecule is captured in the Antigen class Figure . During The MHC Binding class captures the details relating to the interaction of the epitope with specific MHC molecules and information relating to the MHC molecule along with any available Epitope-MHC complex structure details. This class also has a slot that is restricted to be an instance of the Assay Information class Figure .Extrinsic features of an epitope are captured by Naturally Processed Ligand, T Cell Response, and B Cell Response classes. Extrinsic features are context-dependent attributes, being dependent upon specific experimental conditions. The Naturally Processed Ligand class captures data related to epitopes that are naturally processed and presented on the cell surface. This class has properties that are instances of classes including Antigen Presenting Cell, Antigen, and Assay Information Figure .The Naturally Processed Ligand class differs from the MHC Binding class in that information related to the antigen that was processed and the cell types in which the processing occurred is represented. MHC Binding class captures data relating to in vitro MHC binding assays, which assess the epitope's binding capacity to the MHC molecule. Hence the MHC Binding class does requires neither the Antigen class not the Antigen Presenting Cells class. In general, naturally processed ligands are assessed in the absence of a T cell response, for example, identified by direct elution from MHC molecules extracted from infected cells or antigen presenting cells. Thus, the Immunization class is not used as a value restriction by the Naturally Processed Ligand class.The T Cell Response class captures all of the T cell mediated immunity-related information Figure . It has There are three classes that capture information about the 3D structure of complexes: Epitope-MHC Complex, Epitope-MHC-TCR Complex, and Antigen-Antibody Complex. The Epitope-MHC Complex, Epitope-MHC-TCR Complex, and Antigen-Antibody Complex classes are used as restrictions on properties of the MHC Binding, T Cell Response, and B Cell Response classes respectively Figures , and 6B.. One of the files provided as supplementary material contains two examples of how two literature references [Each class has numerous slots that capture detailed information associated with epitopes. As mentioned above, a complete list of all the classes, their properties, and relationship, can be found at ferences ,25 contaThe IEDB will be a comprehensive resource pertaining to epitopes of the immune system. By extensively curating both intrinsic and extrinsic features associated with epitopes, the IEDB is expected to provide substantially greater detail about specific epitopes than any other databases presently available. The IEDB will be populated with data derived from three main sources, namely the peer-reviewed literature, patent applications, and direct submission. Epitope data published in the literature and patent applications are curated manually by the IEDB's curation team. Data from already existing epitope databases, whose authors have agreed to share their data, will also be imported into the IEDB. Apart from these, a main data source will be the direct submission of data from the Large-scale Antibody and T Cell Epitope Discovery programs  that areCommunication between database developers, researchers, analysis tool developers, and team members is crucial, and can be performed in harmony only when a common vocabulary is established. An ontology, which is an explicit formal specification of the terms in the domain and relationships among them, is an effective way to share the knowledge contained in that domain. Accordingly, since the IEDB's domain is epitope-related data, we have created an ontology that captures detailed conceptual structure related to these data.The development of this ontology has relevance for the expansion and modification of the epitope knowledge base. Our ontology design defines individual concepts as separate classes and then defined relationships between these classes and other objects in the domain. These classes serve to restrict the values that will describe properties of objects in the database. For example, the species is a separate concept defined in its own class. Depending on the context, this can refer to an immunized species or the species from which antigen presenting cells are derived. Similarly MHC Molecules is defined as a separate class, and it is used as a value restriction by concepts like MHC Binding and Antigen Presenting Cells. Defining concepts as separate classes and using them to restrict the values of properties in other classes facilitates the expansion and modification of our ontology. Adding properties (slots) to concepts is a task easily accomplished when there are well-defined class descriptions that may serve as value restrictions on the properties, and providing that these class descriptions are general enough to apply in all instances. We have ensured in our design that each concept is atomic and that it can be re-used by various classes.The development of a formal ontology is valuable to database users and in particular to scientists contributing data to, and downloading data from, the IEDB. We anticipate that the availability of a formal ontology will ensure that a common language and shared understanding of concepts will inspire this type of communication, thus ensuring maximum efficiency and accuracy. The formal ontology developed will most likely require refinement and fine tuning when users provide suggestions and new technologies for performing experiments are discovered. The IEDB website will provide mechanisms for the users to provide suggestions and participate in the enhancement of the ontology. The IEDB Data Dictionary has a separate column for the users to provide comments on specific data fields. The IEDB website will also host web forms that will guide users to conform to the ontology definitions when submitting data. Apart from the web forms, an XML schema definition (XSD) will be available on the website for users to inspect and use in their data submission. Users will also be able to download epitope records from the website.In the process of developing new ontologies, it is good practice to leverage existing community standards. In our initial analysis, we confirmed that there were no explicit ontologies that efficiently captured epitope details as per the scope of the IEDB program. As mentioned above, we have utilized, as much as possible, inferred ontologies from existing epitope databases. Among the ontologies that we analyzed, IMGT-Ontology and Gene Ontology were the only two formal ontologies that were related to the epitope domain. The IMGT-Ontology was designed for the ImMunoGeneTics database. IMGT is an integrated database specializing in antigen receptors (immunoglobulins and T-cell receptors) and the major histocompatibility complex of all vertebrate species. The ontology developed for this database has specific immunological content, describing the classification and specification of terms needed for immunogenetics. The IEDB does conform to IMGT's standards about receptors and MHC molecule chains in the sense that all the chain names follow IMGT's controlled vocabulary.GO provides structured controlled vocabularies for genes, gene products, and sequences annotated for many organisms. The IEDB complements GO in terms of epitopes of immunological interest since GO is incomplete in this area. Antigens, which are primary sources of epitopes, are annotated in GO. Thus, in essence, the IEDB could be utilized to provide an extension of GO for antigens that contain epitope-related information.Perhaps the most important element in the development of the IEDB ontology is that, to the best of our knowledge, this represents the first immunological ontology specifically designed to capture both intrinsic biochemical and extrinsic context dependent information. In this respect, it is similar in spirit, but different in approach, from other knowledge resources relating to systems biology. We anticipate that the development of this type of ontology and associated databases might lead to completely new methods for describing and modeling immune responses. Accordingly, this new program might represent a novel tool to assist in the design, testing, and development of new ways to combat infectious diseases and other immune related pathologies such as cancer and autoimmune diseases.A complete listing of IEDB's class hierarchy and its properties is available at IEDB – Immune Epitope Database and Analysis ResourceMHC – Major Histocompatibility ComplexTCR – T Cell ReceptorBCR – B Cell ReceptorIMGT – Immunogenetics DatabaseGO – Gene OntologyJ2EE – Java 2 Enterprise EditionOWL – Ontology Web LanguagePDB – Protein Data BankNIAID – National Institute of Allergy and Infectious DiseasesXML – Extensible Markup LanguageXSD – XML Schema DefinitionMS generated the formal ontology using Protégé. MS, BP, HB, JS, and AS designed the initial ontology. SW, JM, WF, DM, PB provided critical insight in enhancing the initial design and creating the formal ontology. All authors participated in the preparation of the manuscript.Sample Curation. This file contains a table that shows how epitope and its related information were extracted from a literature reference and mapped into the IEDB ontology.Click here for fileIEDB Data Dictionary v12-5. The Data Dictionary contains the textual overview description and a listing of fields that are captured for IEDB.Click here for file


Dehydrogenase enzymes belong to the oxidoreductase class and utilise the coenzymes NAD and NADP. Stereo-selectivity is focused on the C4 hydrogen atoms of the nicotinamide ring of NAD(P). Depending upon which hydrogen is transferred at the C4 location, the enzyme is designated as A or B stereospecific.v1.0 (DSD) provides a compilation of enzyme stereochemical data, as sourced from the primary literature, in the form of a web-accessible database. There are two search engines, a menu driven search and a BLAST search. The entries are also linked to several external databases, including the NCBI and the Protein Data Bank, providing wide background information. The database is freely available online at: The Dehydrogenase Stereospecificity Database DSD is a unique compilation available on-line for the first time which provides a key resource for the comparative analysis of reductase hydrogen transfer stereospecificity. As databases increasingly form the backbone of science, largely complete databases such as DSD, are a vital addition. Dehydrogenase enzymes exhibit stereo-selectivity for diastereotopic hydrogen atoms, situated at the C4 position of the dihydronicotinamide ring of the pyridine nucleotide coenzymes, Nicotinamide Adenine Dinucleotide (NAD) and Nicotinamide Adenine Dinucleotide Phosphate (NADP). The dehydrogenases belong to the oxidoreductase group of enzymes (E.C. Class 1), which oxidise their substrates by transferring hydrogen from NAD or NADP. These molecules act as high energy electron donors, plus low energy acceptors and are derived from the vitamin niacin; the molecules consist of two ribose groups, linked by two phosphates, which are in turn bonded to two bases, adenine and nicotinamide Figure . NADP issyn- or anti- conformations, which are related by a 180 degree rotation about the glycosidic bond linking the nicotinamide base to the C1 of ribose [pro R/pro S [pro R/pro S system is superior to A/B as it denotes the absolute configuration of the atom. The A/B system works by comparing enzyme stereo-selectivity with that of yeast alcohol dehydrogenase, which is an A stereospecific enzyme. Enzymes with the same stereospecificity are designated as A, while other enzymes are denoted as B stereospecific [pro R and B is the same as pro S [The structure of NAD(P) allows presentation of either hydrogen depending on the orientation of the nicotinamide ring, which can be bound in either f ribose   spectroscopy. This method was accurate and allowed direct measurement of deuterium content at the reaction site, as well as being safer and more facile than previous techniques.Fisher  was the v1.0 database designates enzymes as either A or B stereospecific, for a given coenzyme. Two decades ago, You [The determination of stereospecificity is essential for gaining an understanding of the reaction mechanism of an enzyme. Most important of all, is the fact that knowledge of the stereochemistry based on hydride transfer can provide an insight into the relative substrate and coenzyme binding orientations; this can be instrumental in designing strategies to control and regulate reactions . We havev1.0 has been implemented as a postgreSQL relational database. This establishes a flexible infrastructure capable of addressing all foreseeable developments of the archive. The data was initially compiled in a Microsoft ACCESS database after addition of data from previous studies and exhaustive searching of the primary literature. The postgreSQL database is structured into seven normalised tables, populated from a flat-file export of the ACCESS database using PERL scripts integrated with SQL. See Figure .DSD The data was sourced from a previous compilation  and exhaCross-references to key external databases are also made. These provide links to the protein sequence, using NCBI Entrez-Protein, and the relevant protein structure in the Protein Data Bank . If applv1.0 was compiled manually from previously published, peer-reviewed articles, and verified, where possible, from the original literature. This suggests that, compared to some other databases, DSD will be accurate and reliable. Moreover, to maintain this accuracy, data was only added from the primary literature for widely accepted experimental techniques, such as NMR spectroscopy. Experimental conditions have also been included, with 113 temperature measurements, and 117 pH values, and 135 concentration measurements. As logistical considerations preclude us from undertaking independent verification of the data, we are obliged to trust the data reported in the literature.The usefulness of a database is governed by the accuracy of the data it contains. The data contained in DSD Two search methods for DSD are available: a menu-based interface and an interface based on Basic Local Alignment Search Tool (BLAST) . The impThe alternative search interface is based on BLAST . A localv1.0 is consistent with this view. Our data displays an approximately even distribution, with 236 occurrences of A stereospecific enzymes and 225 occurrences of B stereospecific enzymes  with the ring in the syn-conformation (Figure vice versa, would require a total rearrangement of the essential functional residues, to allow the correct binding orientation. This is based on the structural constraints mentioned above and therefore such an exchange is inconceivable during divergent evolution [A-stereospecific enzymes may have evolved before B-stereospecific enzymes, since A-stereospecific enzyme substrates are smaller and less complex compounds than those of B-stereospecific enzymes . Prebiotn Figure . With revolution . Bindingvolution .A relationship between the stereospecificity and equilibrium constant of a hydride transfer has also been proposed: reactions with pKeq > 11.3 correlate with A stereospecificity and reactions with pKeq <11.3 correlate with B stereospecificity. However, as with many postulated generalisations, there are exceptions, such as 3α-hydroxysteroid dehydrogenase. The enzyme reaction has a pKeq ~ 8, within the B range yet it is A stereospecific . This hiWith respect to future work, the database needs to be maintained and developed further, ensuring our links to external databases remain current and newly published data is added. Initially, as with all databases, random errors will have occurred due to human error during the data accumulation or will be extant within the original experimental data. The database will be assessed for errors and inconsistencies, thus maintaining, as far as possible, the overall veracity of our data. Moreover, feedback on the search interfaces and the general infrastructure will allow us to address issues raised and revise the database accordingly.You  includedv1.0 database is a unique, up to date compilation of dehydrogenase stereospecificities, which has advanced on reviews put together over 20 years ago. We see the database as being relatively small, due to the constraints of information, but largely complete. As new data becomes available, the database will increase in size. The ease of access to the data is of great importance and the bespoke search system and the inclusion of a BLAST search greatly facilitates this. The addition of the cross-references to several external databases provides expansive background information. We hope the database will provide an important resource which will help enhance our understanding of enzyme mechanisms. In an age when databases are increasingly forming the backbone of science, largely complete databases, such as DSD, are an important addition.The DSD  suitable for most graphical web browser.The database is available at CPT compiled the database and developed the BLAST search tool. HM was responsible for developing all postGRESQL and perl code and also designed the HTML web front end. DRF originated the concept, designed the database, and identified relevant data sources. All authors have read and approved the final manuscript.


The Stanley Medical Research Institute online genomics database (SMRIDB) is a comprehensive web-based system for understanding the genetic effects of human brain disease . This database contains fully annotated clinical metadata and gene expression patterns generated within 12 controlled studies across 6 different microarray platforms.A thorough collection of gene expression summaries are provided, inclusive of patient demographics, disease subclasses, regulated biological pathways, and functional classifications.The combination of database content, structure, and query speed offers researchers an efficient tool for data mining of brain disease complete with information such as: cross-platform comparisons, biomarkers elucidation for target discovery, and lifestyle/demographic associations to brain diseases. Brain disease studies based on experiments using genome-wide measurements with microarrays are traditionally challenging as compared to other disease areas. The biological results are often hindered by statistical issues of small sample sizes, small effect sizes, and patient-to-patient variability -3. Also,Technical problems in microarray data can also affect the analyses. Meaningful results are often limited by array platform-to-platform comparisons and overall organization/presentation of large data sets/results. Studies conducted on disparate platforms are inherently more difficult to analyze than those conducted on the same platform . Cross-pThe SMRIDB is an online data warehouse and analytical system designed to aid researchers in understanding the biological associations both between and within the brain disorders of schizophrenia, bipolar, and major depression. This open source database combines genomic patterns of brain disease with patient clinical metadata into a user-friendly query interface to enable efficient data mining for purposes of biomarker discovery and elucidating biological mechanisms of brain disease. The metadata includes a full summary of clinical history for each patient with hyperlinks to disease-level information, such that demographic- and lifestyle-associated effects can be determined as they relate to brain disorders. The genomic data has been compiled from 12 separate labs (identified as studies), each data set generated from brain tissue isolated from two controlled populations of 165 patients, diagnosed with one of the three brain disorders (plus unaffected control brain tissue). This genomic data has been generated across 6 separate human array platforms  providing patterns/trends and analytical inferences that are not limited by platform dependencies.NCBI's Database for Annotation, Visualization and Integrated Discovery (DAVID 2.0) was used as the standard source for gene annotation information . The priGene details page section). Such results allow researchers to determine markers that are related to lifestyle or clinical demographical information and identify confounding variables within a disease class.For each of the individual studies, a series of analyses were performed. Each array (representing a single patient) was subjected to a quality control (QC) analysis for chip-level parameters  with respect to the reference distribution for those parameters across the arrays. This QC analysis is represented with both graphical representations  and tabThe three disease classes were analyzed to provide a list of discriminating genes (adjusted for the demographic terms that met the criteria of significance for that gene) or markers indicative of disease Figure  between Within this pathway/GO detail page is a comprehensive summary of the gene expression profiles for each gene that is mapped to the associated pathway or GO term within each separate disease class. A confidence interval boxplot is provided within each disease comparison inclusive of every gene mapped to that pathway or GO term queried in the study Figure , along wBioinformatics mappings section for mapping criteria). So each gene summary page contains probe-level information for all of the 6 array platforms and 12 studies within the database. In addition to general bioinformatics annotations  and pathway/GO mappings (associations with gene that link to pathway/GO-centric pages), this page contains gene expression summaries for every probe that maps to this gene across all studies  to enable queries by gene, LocusLink, or pathway for any single or combined study results.The intended users of the database include any genomics researchers facing the persistent challenges of sensitivity for biomarker discovery and cross-platform microarray comparisons. However, the content within the SMRIDB is primarily designed for biologists, clinical researchers, bioinformaticians, and scientist in the field of brain disease.The size and scope of the SMRIDB makes it a unique contribution to genomics-based brain disease research. With combined gene expression profile summaries across 12 studies and 6 platforms, there is greater confidence in scientific findings such as biomarkers for disease, biological functional roles, and regulated pathways, as compared to results obtained from any one individual study.The SMRIDB is a comprehensive data mining tool to enable researchers to elucidate the biological mechanisms of bipolar disorder, schizophrenia, and depression. A diverse patient population combine with data generated across six microarray platforms and 12 studies to provide robust results to enhance the understanding of brain disease.. All users must register (name and email address) to obtain a username and password.The SMRIDB can be accessed at BWH and ME conducted the data analysis and were involved in drafting the manuscript. SR developed the web services and database backend. BB collected and catalogued the clinical information and samples. All authors read and approved the final manuscript.