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Developing data interoperability using standards: A wheat community use case.

ABSTRACT: In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets. During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations. To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach's potential to be generalizable to other (agricultural) domains.

SUBMITTER: Dzale Yeumo E

PROVIDER: S-EPMC5747345 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Developing data interoperability using standards: A wheat community use case.

Dzale Yeumo Esther E Alaux Michael M Arnaud Elizabeth E Aubin Sophie S Baumann Ute U Buche Patrice P Cooper Laurel L Ćwiek-Kupczyńska Hanna H Davey Robert P RP Fulss Richard Allan RA Jonquet Clement C Laporte Marie-Angélique MA Larmande Pierre P Pommier Cyril C Protonotarios Vassilis V Reverte Carmen C Shrestha Rosemary R Subirats Imma I Venkatesan Aravind A Whan Alex A Quesneville Hadi H

F1000Research 20171016

In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. ...[more]

PMID: 29333241

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Project description:BackgroundPhysical activity data provides important information on disease onset, progression, and treatment outcomes. Although analyzing physical activity data in conjunction with other clinical and microbiological data will lead to new insights crucial for improving human health, it has been hampered partly because of the large variations in the way the data are collected and presented.ObjectiveThe aim of this study was to develop a Physical Activity Ontology (PACO) to support structuring and standardizing heterogeneous descriptions of physical activities.MethodsWe prepared a corpus of 1140 unique sentences collected from various physical activity questionnaires and scales as well as existing standardized terminologies and ontologies. We extracted concepts relevant to physical activity from the corpus using a natural language processing toolkit called Multipurpose Text Processing Tool. The target concepts were formalized into an ontology using Protégé (version 4). Evaluation of PACO was performed to ensure logical and structural consistency as well as adherence to the best practice principles of building an ontology. A use case application of PACO was demonstrated by structuring and standardizing 36 exercise habit statements and then automatically classifying them to a defined class of either sufficiently active or insufficiently active using FaCT++, an ontology reasoner available in Protégé.ResultsPACO was constructed using 268 unique concepts extracted from the questionnaires and assessment scales. PACO contains 225 classes including 9 defined classes, 20 object properties, 1 data property, and 23 instances (excluding 36 exercise statements). The maximum depth of classes is 4, and the maximum number of siblings is 38. The evaluations with ontology auditing tools confirmed that PACO is structurally and logically consistent and satisfies the majority of the best practice rules of ontology authoring. We showed in a small sample of 36 exercise habit statements that we could formally represent them using PACO concepts and object properties. The formal representation was used to infer a patient activity status category of sufficiently active or insufficiently active using the FaCT++ reasoner.ConclusionsAs a first step toward standardizing and structuring heterogeneous descriptions of physical activities for integrative data analyses, PACO was constructed based on the concepts collected from physical activity questionnaires and assessment scales. PACO was evaluated to be structurally consistent and compliant to ontology authoring principles. PACO was also demonstrated to be potentially useful in standardizing heterogeneous physical activity descriptions and classifying them into clinically meaningful categories that reflect adequacy of exercise.

Project description:BackgroundDietary habits offer crucial information on one's health and form a considerable part of the patient-generated health data. Dietary data are collected through various channels and formats; thus, interoperability is a significant challenge to reusing this type of data. The vast scope of dietary concepts and the colloquial expression style add difficulty to standardizing the data. The interoperability issues of dietary data can be addressed through Common Data Elements with metadata annotation to some extent. However, making culture-specific dietary habits and questionnaire-based dietary assessment data interoperable still requires substantial efforts.ObjectiveThe main goal of this study was to address the interoperability challenge of questionnaire-based dietary data from different cultural backgrounds by combining ontological curation and metadata annotation of dietary concepts. Specifically, this study aimed to develop a Dietary Lifestyle Ontology (DILON) and demonstrate the improved interoperability of questionnaire-based dietary data by annotating its main semantics with DILON.MethodsBy analyzing 1158 dietary assessment data elements (367 in Korean and 791 in English), 515 dietary concepts were extracted and used to construct DILON. To demonstrate the utility of DILON in addressing the interoperability challenges of questionnaire-based multicultural dietary data, we developed 10 competency questions that asked to identify data elements sharing the same dietary topics and assessment properties. We instantiated 68 data elements on dietary habits selected from Korean and English questionnaires and annotated them with DILON to answer the competency questions. We translated the competency questions into Semantic Query-Enhanced Web Rule Language and reviewed the query results for accuracy.ResultsDILON was built with 262 concept classes and validated with ontology validation tools. A small overlap (72 concepts) in the concepts extracted from the questionnaires in 2 languages indicates that we need to pay closer attention to representing culture-specific dietary concepts. The Semantic Query-Enhanced Web Rule Language queries reflecting the 10 competency questions yielded correct results.ConclusionsEnsuring the interoperability of dietary lifestyle data is a demanding task due to its vast scope and variations in expression. This study demonstrated that we could improve the interoperability of dietary data generated in different cultural contexts and expressed in various styles by annotating their core semantics with DILON.

Project description:An inherent issue in high-throughput rRNA gene tag sequencing microbiome surveys is that they provide compositional data in relative abundances. This often leads to spurious correlations, making the interpretation of relationships to biogeochemical rates challenging. To overcome this issue, we quantitatively estimated the abundance of microorganisms by spiking in known amounts of internal DNA standards. Using a 3-year sample set of diverse microbial communities from the Western Antarctica Peninsula, we demonstrated that the internal standard method yielded community profiles and taxon cooccurrence patterns substantially different from those derived using relative abundances. We found that the method provided results consistent with the traditional CHEMTAX analysis of pigments and total bacterial counts by flow cytometry. Using the internal standard method, we also showed that chloroplast 16S rRNA gene data in microbial surveys can be used to estimate abundances of certain eukaryotic phototrophs such as cryptophytes and diatoms. In Phaeocystis, scatter in the 16S/18S rRNA gene ratio may be explained by physiological adaptation to environmental conditions. We conclude that the internal standard method, when applied to rRNA gene microbial community profiling, is quantitative and that its application will substantially improve our understanding of microbial ecosystems.IMPORTANCE High-throughput-sequencing-based marine microbiome profiling is rapidly expanding and changing how we study the oceans. Although powerful, the technique is not fully quantitative; it provides taxon counts only in relative abundances. In order to address this issue, we present a method to quantitatively estimate microbial abundances per unit volume of seawater filtered by spiking known amounts of internal DNA standards into each sample. We validated this method by comparing the calculated abundances to other independent estimates, including chemical markers (pigments) and total bacterial cell counts by flow cytometry. The internal standard approach allows us to quantitatively estimate and compare marine microbial community profiles, with important implications for linking environmental microbiomes to quantitative processes such as metabolic and biogeochemical rates.

Project description:Health Surveillance Assistants (HSAs) have been providing integrated community case management (iCCM) for sick children in Malawi since 2008. HSAs report monthly iCCM program data but, at the time of this study, little of it was being used for service improvement. Additionally, HSAs and facility health workers did not have the tools to compile and visualize the data they collected to make evidence-based program decisions. From 2012 to 2013, we worked with Ministry of Health staff and partners to develop and pilot a program in Dowa and Kasungu districts to improve data quality and use at the health worker level. We developed and distributed wall chart templates to display and visualize data, provided training to 426 HSAs and supervisors on data analysis using the templates, and engaged health workers in program improvement plans as part of a data quality and use (DQU) package. We assessed the package through baseline and endline surveys of the HSAs and facility and district staff in the study areas, focusing specifically on availability of reporting forms, completeness of the forms, and consistency of the data between different levels of the health system as measured through results verification ratio (RVR). We found evidence of significant improvements in reporting consistency for suspected pneumonia illness (from overreporting cases at baseline [RVR=0.82] to no reporting inconsistency at endline [RVR=1.0]; P=.02). Other non-significant improvements were measured for fever illness and gender of the patient. Use of the data-display wall charts was high; almost all HSAs and three-fourths of the health facilities had completed all months since January 2013. Some participants reported the wall charts helped them use data for program improvement, such as to inform community health education activities and to better track stock-outs. Since this study, the DQU package has been scaled up in Malawi and expanded to 2 other countries. Unfortunately, without the sustained support and supervision provided in this project, use of the tools in the Malawi scale-up is lower than during the pilot period. Nevertheless, this pilot project shows community and facility health workers can use data to improve programs at the local level given the opportunity to access and visualize the data along with supervision support.

Project description:BackgroundReal-world data (RWD) and real-world evidence (RWE) are playing increasingly important roles in clinical research and health care decision-making. To leverage RWD and generate reliable RWE, data should be well defined and structured in a way that is semantically interoperable and consistent across stakeholders. The adoption of data standards is one of the cornerstones supporting high-quality evidence for the development of clinical medicine and therapeutics. Clinical Data Interchange Standards Consortium (CDISC) data standards are mature, globally recognized, and heavily used by the pharmaceutical industry for regulatory submissions. The CDISC RWD Connect Initiative aims to better understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance needed to more easily implement them.ObjectiveThe aim of this study is to understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance that may be needed to implement CDISC standards more easily for this purpose.MethodsWe conducted a qualitative Delphi survey involving an expert advisory board with multiple key stakeholders, with 3 rounds of input and review.ResultsOverall, 66 experts participated in round 1, 56 in round 2, and 49 in round 3 of the Delphi survey. Their inputs were collected and analyzed, culminating in group statements. It was widely agreed that the standardization of RWD is highly necessary, and the primary focus should be on its ability to improve data sharing and the quality of RWE. The priorities for RWD standardization included electronic health records, such as data shared using Health Level 7 Fast Health care Interoperability Resources (FHIR), and the data stemming from observational studies. With different standardization efforts already underway in these areas, a gap analysis should be performed to identify the areas where synergies and efficiencies are possible and then collaborate with stakeholders to create or extend existing mappings between CDISC and other standards, controlled terminologies, and models to represent data originating across different sources.ConclusionsThere are many ongoing data standardization efforts around human health data-related activities, each with different definitions, levels of granularity, and purpose. Among these, CDISC has been successful in standardizing clinical trial-based data for regulation worldwide. However, the complexity of the CDISC standards and the fact that they were developed for different purposes, combined with the lack of awareness and incentives to use a new standard and insufficient training and implementation support, are significant barriers to setting up the use of CDISC standards for RWD. The collection and dissemination of use cases, development of tools and support systems for the RWD community, and collaboration with other standards development organizations are potential steps forward. Using CDISC will help link clinical trial data and RWD and promote innovation in health data science.

Project description:BackgroundEffective standardisation of methodologies to analyse the microbiome is essential to the entire microbiome community. Despite the microbiome field being established for over a decade, there are no accredited or certified reference materials available to the wider community. In this study, we describe the development of the first reference reagents produced by the National Institute for Biological Standards and Control (NIBSC) for microbiome analysis by next-generation sequencing. These can act as global working standards and will be evaluated as candidate World Health Organization International Reference Reagents.ResultsWe developed the NIBSC DNA reference reagents Gut-Mix-RR and Gut-HiLo-RR and a four-measure framework for evaluation of bioinformatics tool and pipeline bias. Using these reagents and reporting system, we performed an independent evaluation of a variety of bioinformatics tools by analysing shotgun sequencing and 16S rRNA sequencing data generated from the Gut-Mix-RR and Gut-HiLo-RR. We demonstrate that key measures of microbiome health, such as diversity estimates, are largely inflated by the majority of bioinformatics tools. Across all tested tools, biases were present, with a clear trade-off occurring between sensitivity and the relative abundance of false positives in the final dataset. Using commercially available mock communities, we investigated how the composition of reference reagents may impact benchmarking studies. Reporting measures consistently changed when the same bioinformatics tools were used on different community compositions. This was influenced by both community complexity and taxonomy of species present. Both NIBSC reference reagents, which consisted of gut commensal species, proved to be the most challenging for the majority of bioinformatics tools tested. Going forward, we recommend the field uses site-specific reagents of a high complexity to ensure pipeline benchmarking is fit for purpose.ConclusionsIf a consensus of acceptable levels of error can be agreed on, widespread adoption of these reference reagents will standardise downstream gut microbiome analyses. We propose to do this through a large open-invite collaborative study for multiple laboratories in 2020. Video Abstract.

Dataset Information

Developing data interoperability using standards: A wheat community use case.

Publications

Developing data interoperability using standards: A wheat community use case.

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