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The BioMart community portal: an innovative alternative to large, centralized data repositories.

ABSTRACT: The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

SUBMITTER: Smedley D

PROVIDER: S-EPMC4489294 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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The BioMart community portal: an innovative alternative to large, centralized data repositories.

Smedley Damian D Haider Syed S Durinck Steffen S Pandini Luca L Provero Paolo P Allen James J Arnaiz Olivier O Awedh Mohammad Hamza MH Baldock Richard R Barbiera Giulia G Bardou Philippe P Beck Tim T Blake Andrew A Bonierbale Merideth M Brookes Anthony J AJ Bucci Gabriele G Buetti Iwan I Burge Sarah S Cabau Cédric C Carlson Joseph W JW Chelala Claude C Chrysostomou Charalambos C Cittaro Davide D Collin Olivier O Cordova Raul R Cutts Rosalind J RJ Dassi Erik E Di Genova Alex A Djari Anis A Esposito Anthony A Estrella Heather H Eyras Eduardo E Fernandez-Banet Julio J Forbes Simon S Free Robert C RC Fujisawa Takatomo T Gadaleta Emanuela E Garcia-Manteiga Jose M JM Goodstein David D Gray Kristian K Guerra-Assunção José Afonso JA Haggarty Bernard B Han Dong-Jin DJ Han Byung Woo BW Harris Todd T Harshbarger Jayson J Hastings Robert K RK Hayes Richard D RD Hoede Claire C Hu Shen S Hu Zhi-Liang ZL Hutchins Lucie L Kan Zhengyan Z Kawaji Hideya H Keliet Aminah A Kerhornou Arnaud A Kim Sunghoon S Kinsella Rhoda R Klopp Christophe C Kong Lei L Lawson Daniel D Lazarevic Dejan D Lee Ji-Hyun JH Letellier Thomas T Li Chuan-Yun CY Lio Pietro P Liu Chu-Jun CJ Luo Jie J Maass Alejandro A Mariette Jerome J Maurel Thomas T Merella Stefania S Mohamed Azza Mostafa AM Moreews Francois F Nabihoudine Ibounyamine I Ndegwa Nelson N Noirot Céline C Perez-Llamas Cristian C Primig Michael M Quattrone Alessandro A Quesneville Hadi H Rambaldi Davide D Reecy James J Riba Michela M Rosanoff Steven S Saddiq Amna Ali AA Salas Elisa E Sallou Olivier O Shepherd Rebecca R Simon Reinhard R Sperling Linda L Spooner William W Staines Daniel M DM Steinbach Delphine D Stone Kevin K Stupka Elia E Teague Jon W JW Dayem Ullah Abu Z AZ Wang Jun J Ware Doreen D Wong-Erasmus Marie M Youens-Clark Ken K Zadissa Amonida A Zhang Shi-Jian SJ Kasprzyk Arek A

Nucleic acids research 20150420 W1

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their h ...[more]

PMID: 25897122

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Project description:BackgroundAn essential step in any medical research project after identifying the research question is to determine if there are sufficient patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real-world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually involves working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard was developed by Health Level Seven to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative in Germany has committed to this standard and created data integration centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem; however, a distributed feasibility query platform for the FHIR standard is still missing.ObjectiveThis study described the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort was part of a large COVID-19 data exchange platform and was designed to be scalable for a broad range of patient data.MethodsWe analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, backend with an ontology and terminology service, middleware for query distribution, and FHIR feasibility query execution service.ResultsWe implemented the components described in the Methods section. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test data set based on the German Corona Consensus Data Set. A performance test using specifically created synthetic data revealed the applicability of our solution to data sets containing millions of FHIR resources. The solution can be easily deployed across hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven query languages such as Clinical Quality Language and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple Health Level Seven query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative.ConclusionsWe designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR-formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.

Project description:Accuracy of taxonomic identifications is crucial to data quality in online repositories of species occurrence data, such as the Global Biodiversity Information Facility (GBIF), which have accumulated several hundred million records over the past 15 years. These data serve as basis for large scale analyses of macroecological and biogeographic patterns and to document environmental changes over time. However, taxonomic identifications are often unreliable, especially for non-vascular plants and fungi including lichens, which may lack critical revisions of voucher specimens. Due to the scale of the problem, restudy of millions of collections is unrealistic and other strategies are needed. Here we propose to use verified, georeferenced occurrence data of a given species to apply predictive niche modeling that can then be used to evaluate unverified occurrences of that species. Selecting the charismatic lichen fungus, Usnea longissima, as a case study, we used georeferenced occurrence records based on sequenced specimens to model its predicted niche. Our results suggest that the target species is largely restricted to a narrow range of boreal and temperate forest in the Northern Hemisphere and that occurrence records in GBIF from tropical regions and the Southern Hemisphere do not represent this taxon, a prediction tested by comparison with taxonomic revisions of Usnea for these regions. As a novel approach, we employed Principal Component Analysis on the environmental grid data used for predictive modeling to visualize potential ecogeographical barriers for the target species; we found that tropical regions conform a strong barrier, explaining why potential niches in the Southern Hemisphere were not colonized by Usnea longissima and instead by morphologically similar species. This approach is an example of how data from two of the most important biodiversity repositories, GenBank and GBIF, can be effectively combined to remotely address the problem of inaccuracy of taxonomic identifications in occurrence data repositories and to provide a filtering mechanism which can considerably reduce the number of voucher specimens that need critical revision, in this case from 4,672 to about 100.

Dataset Information

The BioMart community portal: an innovative alternative to large, centralized data repositories.

Publications

The BioMart community portal: an innovative alternative to large, centralized data repositories.

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