Biological experiments and bioinformatics analysis are producing large datasets of omics data (e.g. genomics, proteomics, interactomics, etc.), stored in diversi ed sources and databases. Biological data and bioinformatics results can be related to various information, such as clinical data (e.g. cancer stage) or environment data (e.g. place where a patient lives), but this requires novel data integration mechanisms and analysis algorithms. Novel data structures are needed for data integration, while e cient algorithms are necessary for managing integrated data and to analyze results in order to extract knowledge and underline correlation with environmental factors. In this paper an ontology-based system for clinical data integration and analysis is presented. The system, called eMiRo (Electronic MedIcal RecOrd), includes geo-reference features for epidemiological analysis based on geographical and clinical information. Using eMiRo, a physician is able to handle and integrate biological data; moreover, the system supports the retrieval of additional information such as ontology terms and information about genes and diseases. When conducting a study, for each gene relevant to the study, biological function and relations with other genes as well as involvement in biological processes is reported.
The growth of biological resources and information led to the storage of large
data, such as: physiological processes, gene patterns, disease-gene associations,
clinical trials; in this context it is necessary the development of bioinformatics
tools for data management in order to index heterogeneous resources in
common structures and to provide a comprehensive view of the state of knowledge.
In recent years, many bioinformatics studies have been focused on gene role in
biological processes for preventing and treating diseases. Tools for heterogeneous
data integration and association are developed to improve knowledge in
experiments related to gene samples from Microarray and Next Generation Sequencing
(NGS) technologies. In this scenario it is necessary to develop algorithms able
to merge data from multiple sources, with heterogeneous models and formats,
to obtain a global information which satis es the requirements formulated by
users (e.g. researchers). The literature presents various solutions to improve and
to automatize gene data analysis, for example [
In this paper we present a system designed to manage clinical and biological data, and to correlate such data by means of available ontologies. The system, called eMiRo, has been developed to manage clinical and genomic data and integrates them using ontologies and genomic information from external sources. Moreover, eMiRo includes a geographical feature for epidemiological analysis. 2
We design a system starting by the following requirements: clinical and biological data management, bioinformatics results from ontologies and semantics data, support for geographical data. The actors identi ed for the system are listed below: { Physician: all features are available, it is able to manage data patients (using a model con gured by the administrator), performs query to lter the informations, consults the geographical analysis results, and requires data integration features for a speci c disease; custom access modes are de nable for this pro le to create sub-pro les. { Administrator: refers to the user which is able to handle system and its features, as well as user pro les, component con guration, and data. { Client: identi ed with a person or service (e.g. an algorithm) which accessed the data-integration function using the genieR as web-service. 2.1
System may be roughly divided into three main components, brie y: { eMiRo (or 'eMiRo-component', to distinguish this from the entire system called with the same name): it handles the biological data and the interactions with users, as well as data exchanges with other modules (genieR and geoP); { genieR: it concerns data analysis and data-integration features, also supports retrieving and management of unstructured information from external sources (e.g. ontologies); { geoP: it is related to geographical features: analysis, and geographic coordinates managing (e.g. it converts raw text into coordinates using the Google Maps API).
Figure 1 shows the interactions among the components; external sources and API calling are also shown. eMiRo-component is the basic module of the system; it is in charge of the following functions: { graphical front-end management; { querying; { biological data handling; { database connection and components interaction; { login, security and user roles management; { analytics.
User is able to access the system through a Web-Panel which, essentially, consists in a set of Graphical User Interfaces (GUI) that are generated dynamically in according to a prede ned model for the information required by user. Geographical data are shown to user using a Map-View (from Google Maps API), and a summary table-view to show only the information of interest for the user (e.g. a physician).
Genomic & Ontology
Database genieR
Web
Service eMiRo
WebApp Database
Geocoder Datastore geoP
Geocoder genieR is designed as a web-service to allow external clients (e.g. algorithms or other information systems) to invoke its public functions. This components concerns the data-integration features; speci cally it uni es in a custom structure the information given by several external sources. A mapping between Disease Ontology and Gene Ontology is used to integrate annotations in order to obtain the gene-disease associations.
Therefore, it is used for the management and the integration of data from
remote genomic and ontology sources. eMiRo supports integration from: Disease
Ontology [
genieR main sub-components are: { GenierServlet: it consists in two modules, dealing respectively to handle requests from clients (even Web-Panel accesses genieR as an external resource) and to structure the output in according to JSON format in order to make faster the data stream and easy the parsing operations. { Datastore: it periodically retrieves and parse the information from supported external sources and organizes these in a structure compatible with the eMiRo data model. { DataIntegrator: it is the core module of genieR; it takes care to integrate information in Datastore and to generate an output for the client.
In summary, a physician is able to choose a disease, from a list, to obtain additional information, such as: ontological integration, genes involved for the disease, and meta-data for each gene founded (e.g. description, biological function, biological processes, and relations with other genes). Main commands supported by genieR are 'doget' and 'dolist' ('do': Disease-Object): { former returns information obtained from data-integration action for a speci c Disease Ontology ID (DOID); { latter gives the list of diseases that system supports for the integration operation. geoP is able to convert addresses into geographical coordinates using Google Geocoders API; using this approach a geographical coordinates management through GIS systems is allowed. Furthermore, the system supports analytics features to generate statistical information from geographical and biological data mapping (for statistical analysis are supported a set of parameter, such as: range of values, and threshold level). 2.2
eMiRo system is designed for dynamic creation of reports referred to biological information management; this solution allows users to create new examination and handling data models for existing ones. Data-Type supported by the Database Engine are available during models creation.
A 'model' is composed by a set of information related to a graphic layout and its low-level parameters (e.g. eld-name, eld-data-type, bounds for graphic elements, and the order of the elds in the resulting GUI), furthermore the persistence of models is granted by a Relational Database Management System (RDBMS). In according to this dynamic solution the Figure 2 shows the data structure for a generic examination (named 'Report-1') which consists of three elds ('date' is a default eld whose representation is not required); Figure 2-A and 2-D are referred to a speci c examination for a given patient; while, 2-B and 2-C contain information related to the model for GUI generation: elds, data-type, and generic meta-data (e.g. eld-order, and eld-width in pixel). To implement the system are chosen multiple programming languages related to the purpose for each component; this approach optimizes the deployments and the user experience. genieR and geoP are implemented using Servlet technology to deploy Web-Applications [13] and to expose their features as a service (in according with server policy con guration), for genieR the Google Cloud App Engine SDK is also integrated. eMiRo-component has been developed using the PHP and HTML5 languages, the latter extended using Bootstrap and jQuery frameworks in order to support graphical interface with dynamic elements and a responsive design based on CSS and JavaScript. Oracle MySQL has been chosen as RDBMS for biological and geographic data persistence; in addition, it contains information about the structures and the models used by system during GUI generation.
Field-1 Field-2 Field-3
Savoer id date reportsid patientid 1 2016-07-27 1 1 userid 1 During the test, the eMiRo data-integration component (named genieR) has been used to retrieve the Open Biological Ontologies datasets from the server of Gene-Ontology, Disease-Ontology, and Disgenet. OBO Foundry initiative supports the evolution of ontologies for biomedical data integration promoting ontologies designed to be interoperable and logically well-formed in order to incorporate accurate representations of biological information [14]. A dataset in OBO format contains several tags (e.g. id, name, description, synonyms, crossreferences) organized in Terms linked to each other; thus, a dataset may be represented using, for example, a graph data structure. genieR pipeline is based on two phases: preprocessing and analysis. During Preprocessing the data are integrated and organized in a single structure: (i) the ontological datasets are downloaded on cloud-memory (the data can be refreshed when the sources update their datasets); (ii) subsequently, each dataset is parsed to extract information of interest, and it is collapsed within a single structure with others. Using this approach, genieR is able to create a new large ontology (managed by the Datastore component) that contains the heterogeneous information extracted from each ontological dataset, and the novel relations found among these. Preprocessing reduces the data in the cloud-memory to improve the overall performance during the analysis. For the Analysis phase, genieR implements a component named 'DataIntegrator': when a client requests information about a disease the DataIntegrator performs an analysis of its integrated data (that represents its knowledge), and returns a results in JSON Format. Summing, genieR is able to provide for a speci c disease the relevant genes and for each the biological function, and its involvement in biological processes, and the relations with other genes. 3
In eMiRo, the user requests and the access policy are handled by web-application and DBMS Engine [15]; rst grants the user access based on his role, second checks the read and write operations by the components, as well as the direct requests to database. The communication between the components (internal and external) is performed through HTTPS protocol which allows server authentication, privacy protection and maintaining data integrity, as well as checks data exchanged between the parts and allows SSL/TLS encryption. Furthermore, account credentials are encrypted using the Secure Hash Algorithm (SHA) before being stored within the database. 4
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