Sustainable data and metadata management at the BD2K-LINCS Data Coordination and Integration Center

Abstract

The NIH-funded LINCS Consortium is creating an extensive reference library of cell-based perturbation response signatures and sophisticated informatics tools incorporating a large number of perturbagens, model systems, and assays. To date, more than 350 datasets have been generated including transcriptomics, proteomics, epigenomics, cell phenotype and competitive binding profiling assays. The large volume and variety of data necessitate rigorous data standards and effective data management including modular data processing pipelines and end-user interfaces to facilitate accurate and reliable data exchange, curation, validation, standardization, aggregation, integration, and end user access. Deep metadata annotations and the use of qualified data standards enable integration with many external resources. Here we describe the end-to-end data processing and management at the DCIC to generate a high-quality and persistent product. Our data management and stewardship solutions enable a functioning Consortium and make LINCS a valuable scientific resource that aligns with big data initiatives such as the BD2K NIH Program and concords with emerging data science best practices including the findable, accessible, interoperable, and reusable (FAIR) principles.

<p>Informatics Approaches for Harmonized Intelligent Integration of Stem Cell Research</p>

“…For example, the Library of Integrated Network-based Cellular Signatures (LINCS) project, a multicenter NIH-funded program, created a comprehensive library of molecular signatures supporting data integration, modeling and analysis methodologies. 49 Broad sharing of genomic-and health-related data requires proper governance and security. 50 In the context of stem cell research, data and sample sharing represent a scientific and ethical challenge to ensure appropriate protection of individual interests as well as maintaining public trust.…”

Improving the Utility of the Tox21 Dataset by Deep Metadata Annotations and Constructing Reusable Benchmarked Chemical Reference Signatures

“…Advances in screening technologies, including detection sensitivity and throughput, robotics, and data science, have enabled many large scale data generation projects during the last two decades [26,30]. Examples of publicly funded research consortia focused on small molecule discovery and characterization include the Molecular Libraries Program (MLP) [39], the Tox21 screening program, the Psychoactive Drug Screening Program (PDSP) [40], the Library of Integrated Network-based Cellular Signatures (LINCS) [21], and Illuminating the Druggable Genome (IDG) [41,42]. While such individual projects can have enormous scientific impact, their combined value and impact may yet be considerably larger, because integrated “big data” have potential to provide new insights that cannot be obtained from individual datasets.…”

“…Proteomics, transcriptomics, metabolomics, and target-based cell and biochemical screening data can have compatible metadata enabling their integrative analysis. We recently illustrated best practices of metadata management in another large scale data generation project [21], the Library of Integrated Network-based Cellular Signatures (LINCS) [22]. To that end, we endeavored to further improve the reusability of the Tox21 data and illustrate newfound usability after fully annotating assay information by established reference ontologies followed by aggregating the data to enable specific actionable insights.…”

Mining data and metadata from the gene expression omnibus

“…Such mappings would enable cross-referencing to major public chemical databases to enrich the annotations by providing additional annotations, such as mechanism of actions, targets, disease associations, clinical phase status, and synonyms. It has been shown that such data submission systems, with deep metadata annotations that utilize established terminologies and ontologies, contribute to interoperability and reusability of the data (Stathias et al 2018 ).…”