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J Biomol Screen. 2014 Jun;19(5):803-16. doi: 10.1177/1087057114522514. Epub 2014 Feb 11.

Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS).

Author information

1
Center for Computational Science, University of Miami, Miami, FL, USA.
2
ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, USA.
3
Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA.
4
Center for Molecular Therapeutics, Massachusetts General Hospital, Boston, Massachusetts, USA.
5
Broad Institute, Cambridge, Massachusetts, USA.
6
National Human Genome Research Institute, National Institutes of Health, Rockville, Maryland, USA.
7
ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, USA Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA.
8
Center for Computational Science, University of Miami, Miami, FL, USA Department of Molecular and Cellular Pharmacology, University of Miami, Miami, Florida, USA sschurer@med.miami.edu.

Abstract

The National Institutes of Health Library of Integrated Network-based Cellular Signatures (LINCS) program is generating extensive multidimensional data sets, including biochemical, genome-wide transcriptional, and phenotypic cellular response signatures to a variety of small-molecule and genetic perturbations with the goal of creating a sustainable, widely applicable, and readily accessible systems biology knowledge resource. Integration and analysis of diverse LINCS data sets depend on the availability of sufficient metadata to describe the assays and screening results and on their syntactic, structural, and semantic consistency. Here we report metadata specifications for the most important molecular and cellular components and recommend them for adoption beyond the LINCS project. We focus on the minimum required information to model LINCS assays and results based on a number of use cases, and we recommend controlled terminologies and ontologies to annotate assays with syntactic consistency and semantic integrity. We also report specifications for a simple annotation format (SAF) to describe assays and screening results based on our metadata specifications with explicit controlled vocabularies. SAF specifically serves to programmatically access and exchange LINCS data as a prerequisite for a distributed information management infrastructure. We applied the metadata specifications to annotate large numbers of LINCS cell lines, proteins, and small molecules. The resources generated and presented here are freely available.

KEYWORDS:

cell-based assays; data standards; database and data management; enzyme assays or enzyme kinetics; gene expression; metadata

PMID:
24518066
DOI:
10.1177/1087057114522514
[Indexed for MEDLINE]
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