a fair-based approach to enhancing the discovery and re-use of transcriptomic data assets for nuclear receptor signaling pathways

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ID: 196635
2017
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Abstract
Public transcriptomic assets in the nuclear receptor (NR) signaling field hold considerable collective potential for exposing underappreciated aspects of NR regulation of gene expression. This potential is undermined however by a series of enduring informatic pain points that retard the routine re-use of these datasets. Here we describe a coordinated biocuration and web development approach to redress this situation that is closely aligned with ideals articulated in the FAIR (findable, accessible, interoperable, re-usable) principles on data stewardship. To improve findability, biocurators engage authors of studies in collaborating journals to secure datasets for deposition in public archives. Annotated derivatives of the archived datasets are assigned digital object identifiers and regulatory molecule identifiers that support persistent linkages between datasets and their associated research articles, integration in relevant records in gene and small molecule knowledgebases, and indexing by dataset search engines. To enhance their accessibility and interoperability, datasets are visualizable in responsively designed web pages, retrievable in machine-readable spreadsheets, or through an application programming interface. Re-use of the datasets is supported by their interrogation as a universe of data points through the Transcriptomine search engine, highlighting transcriptional intersections between NR signaling pathways, physiological processes and disease states. We illustrate the value of our approach in connecting disparate research communities using a use case of persistent interoperability between the Nuclear Receptor Signaling Atlas and the Pharmacogenomics Knowledgebase. Our FAIR-aligned model demonstrates the enduring value of discovery-scale datasets that accrues from their systematic compilation, biocuration and distribution across the digital biomedical research enterprise.
Reference Key
ochsner2017dataa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Scott A. Ochsner;Yolanda F. Darlington;Apollo McOwiti;Wasula H. Kankanamge;Alexey Naumov;Lauren B. Becnel;Neil J. McKenna
Journal Proceedings of the National Academy of Sciences of the United States of America
Year 2017
DOI
10.5334/dsj-2017-011
URL
http://datascience.codata.org/articles/624
Keywords
accessibility transcriptomics interoperability re-usescience (general)

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