Dataset Information

OMeta: an ontology-based, data-driven metadata tracking system.

ABSTRACT:

Background

The development of high-throughput sequencing and analysis has accelerated multi-omics studies of thousands of microbial species, metagenomes, and infectious disease pathogens. Omics studies are enabling genotype-phenotype association studies which identify genetic determinants of pathogen virulence and drug resistance, as well as phylogenetic studies designed to track the origin and spread of disease outbreaks. These omics studies are complex and often employ multiple assay technologies including genomics, metagenomics, transcriptomics, proteomics, and metabolomics. To maximize the impact of omics studies, it is essential that data be accompanied by detailed contextual metadata (e.g., specimen, spatial-temporal, phenotypic characteristics) in clear, organized, and consistent formats. Over the years, many metadata standards developed by various metadata standards initiatives have arisen; the Genomic Standards Consortium's minimal information standards (MIxS), the GSCID/BRC Project and Sample Application Standard. Some tools exist for tracking metadata, but they do not provide event based capabilities to configure, collect, validate, and distribute metadata. To address this gap in the scientific community, an event based data-driven application, OMeta, was created that allows users to quickly configure, collect, validate, distribute, and integrate metadata.

Results

A data-driven web application, OMeta, has been developed for use by researchers consisting of a browser-based interface, a command-line interface (CLI), and server-side components that provide an intuitive platform for configuring, capturing, viewing, and sharing metadata. Project and sample metadata can be set based on existing standards or based on projects goals. Recorded information includes details on the biological samples, procedures, protocols, and experimental technologies, etc. This information can be organized based on events, including sample collection, sample quantification, sequencing assay, and analysis results. OMeta enables configuration in various presentation types: checkbox, file, drop-box, ontology, and fields can be configured to use the National Center for Biomedical Ontology (NCBO), a biomedical ontology server. Furthermore, OMeta maintains a complete audit trail of all changes made by users and allows metadata export in comma separated value (CSV) format for convenient deposition of data into public databases.

Conclusions

We present, OMeta, a web-based software application that is built on data-driven principles for configuring and customizing data standards, capturing, curating, and sharing metadata.

SUBMITTER: Singh I

PROVIDER: S-EPMC6322262 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

OMeta: an ontology-based, data-driven metadata tracking system.

Singh Indresh I Kuscuoglu Mehmet M Harkins Derek M DM Sutton Granger G Fouts Derrick E DE Nelson Karen E KE

BMC bioinformatics 20190107 1

<h4>Background</h4>The development of high-throughput sequencing and analysis has accelerated multi-omics studies of thousands of microbial species, metagenomes, and infectious disease pathogens. Omics studies are enabling genotype-phenotype association studies which identify genetic determinants of pathogen virulence and drug resistance, as well as phylogenetic studies designed to track the origin and spread of disease outbreaks. These omics studies are complex and often employ multiple assay t ...[more]

PMID: 30612540

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Project description:BackgroundThe increasing use of common data elements (CDEs) in numerous research projects and clinical applications has made it imperative to create an effective classification scheme for the efficient management of these data elements. We applied high-level integrative modeling of entire clinical documents from real-world practice to create the Clinical MetaData Ontology (CMDO) for the appropriate classification and integration of CDEs that are in practical use in current clinical documents.MethodsCMDO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of CMDO by conceptualizing its first-level terms based on an analysis of clinical-practice procedures, (2) identifying CMDO concepts for representing clinical data of general CDEs by examining how and what clinical data are generated with flows of clinical care practices, (3) assigning hierarchical relationships for CMDO concepts, (4) developing CMDO properties (e.g., synonyms, preferred terms, and definitions) for each CMDO concept, and (5) evaluating the utility of CMDO.ResultsWe created CMDO comprising 189 concepts under the 4 first-level classes of Description, Event, Finding, and Procedure. CMDO has 256 definitions that cover the 189 CMDO concepts, with 459 synonyms for 139 (74.0%) of the concepts. All of the CDEs extracted from 6 HL7 templates, 25 clinical documents of 5 teaching hospitals, and 1 personal health record specification were successfully annotated by 41 (21.9%), 89 (47.6%), and 13 (7.0%) of the CMDO concepts, respectively. We created a CMDO Browser to facilitate navigation of the CMDO concept hierarchy and a CMDO-enabled CDE Browser for displaying the relationships between CMDO concepts and the CDEs extracted from the clinical documents that are used in current practice.ConclusionsCMDO is an ontology and classification scheme for CDEs used in clinical documents. Given the increasing use of CDEs in many studies and real-world clinical documentation, CMDO will be a useful tool for integrating numerous CDEs from different research projects and clinical documents. The CMDO Browser and CMDO-enabled CDE Browser make it easy to search, share, and reuse CDEs, and also effectively integrate and manage CDEs from different studies and clinical documents.

Project description:This paper introduces the objectives, methods and results of ontology development in the EU co-funded project Advancing Clinico-genomic Trials on Cancer-Open Grid Services for Improving Medical Knowledge Discovery (ACGT). While the available data in the life sciences has recently grown both in amount and quality, the full exploitation of it is being hindered by the use of different underlying technologies, coding systems, category schemes and reporting methods on the part of different research groups. The goal of the ACGT project is to contribute to the resolution of these problems by developing an ontology-driven, semantic grid services infrastructure that will enable efficient execution of discovery-driven scientific workflows in the context of multi-centric, post-genomic clinical trials. The focus of the present paper is the ACGT Master Ontology (MO).ACGT project researchers undertook a systematic review of existing domain and upper-level ontologies, as well as of existing ontology design software, implementation methods, and end-user interfaces. This included the careful study of best practices, design principles and evaluation methods for ontology design, maintenance, implementation, and versioning, as well as for use on the part of domain experts and clinicians.To date, the results of the ACGT project include (i) the development of a master ontology (the ACGT-MO) based on clearly defined principles of ontology development and evaluation; (ii) the development of a technical infrastructure (the ACGT Platform) that implements the ACGT-MO utilizing independent tools, components and resources that have been developed based on open architectural standards, and which includes an application updating and evolving the ontology efficiently in response to end-user needs; and (iii) the development of an Ontology-based Trial Management Application (ObTiMA) that integrates the ACGT-MO into the design process of clinical trials in order to guarantee automatic semantic integration without the need to perform a separate mapping process.

Dataset Information

OMeta: an ontology-based, data-driven metadata tracking system.

Background

Results

Conclusions

Publications

OMeta: an ontology-based, data-driven metadata tracking system.

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