Dataset Information

Standardized metadata for human pathogen/vector genomic sequences.

ABSTRACT: High throughput sequencing has accelerated the determination of genome sequences for thousands of human infectious disease pathogens and dozens of their vectors. The scale and scope of these data are enabling genotype-phenotype association studies to identify genetic determinants of pathogen virulence and drug/insecticide resistance, and phylogenetic studies to track the origin and spread of disease outbreaks. To maximize the utility of genomic sequences for these purposes, it is essential that metadata about the pathogen/vector isolate characteristics be collected and made available in organized, clear, and consistent formats. Here we report the development of the GSCID/BRC Project and Sample Application Standard, developed by representatives of the Genome Sequencing Centers for Infectious Diseases (GSCIDs), the Bioinformatics Resource Centers (BRCs) for Infectious Diseases, and the U.S. National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health (NIH), informed by interactions with numerous collaborating scientists. It includes mapping to terms from other data standards initiatives, including the Genomic Standards Consortium's minimal information (MIxS) and NCBI's BioSample/BioProjects checklists and the Ontology for Biomedical Investigations (OBI). The standard includes data fields about characteristics of the organism or environmental source of the specimen, spatial-temporal information about the specimen isolation event, phenotypic characteristics of the pathogen/vector isolated, and project leadership and support. By modeling metadata fields into an ontology-based semantic framework and reusing existing ontologies and minimum information checklists, the application standard can be extended to support additional project-specific data fields and integrated with other data represented with comparable standards. The use of this metadata standard by all ongoing and future GSCID sequencing projects will provide a consistent representation of these data in the BRC resources and other repositories that leverage these data, allowing investigators to identify relevant genomic sequences and perform comparative genomics analyses that are both statistically meaningful and biologically relevant.

SUBMITTER: Dugan VG

PROVIDER: S-EPMC4061050 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Standardized metadata for human pathogen/vector genomic sequences.

Dugan Vivien G VG Emrich Scott J SJ Giraldo-Calderón Gloria I GI Harb Omar S OS Newman Ruchi M RM Pickett Brett E BE Schriml Lynn M LM Stockwell Timothy B TB Stoeckert Christian J CJ Sullivan Dan E DE Singh Indresh I Ward Doyle V DV Yao Alison A Zheng Jie J Barrett Tanya T Birren Bruce B Brinkac Lauren L Bruno Vincent M VM Caler Elizabet E Chapman Sinéad S Collins Frank H FH Cuomo Christina A CA Di Francesco Valentina V Durkin Scott S Eppinger Mark M Feldgarden Michael M Fraser Claire C Fricke W Florian WF Giovanni Maria M Henn Matthew R MR Hine Erin E Hotopp Julie Dunning JD Karsch-Mizrachi Ilene I Kissinger Jessica C JC Lee Eun Mi EM Mathur Punam P Mongodin Emmanuel F EF Murphy Cheryl I CI Myers Garry G Neafsey Daniel E DE Nelson Karen E KE Nierman William C WC Puzak Julia J Rasko David D Roos David S DS Sadzewicz Lisa L Silva Joana C JC Sobral Bruno B Squires R Burke RB Stevens Rick L RL Tallon Luke L Tettelin Herve H Wentworth David D White Owen O Will Rebecca R Wortman Jennifer J Zhang Yun Y Scheuermann Richard H RH

PloS one 20140617 6

High throughput sequencing has accelerated the determination of genome sequences for thousands of human infectious disease pathogens and dozens of their vectors. The scale and scope of these data are enabling genotype-phenotype association studies to identify genetic determinants of pathogen virulence and drug/insecticide resistance, and phylogenetic studies to track the origin and spread of disease outbreaks. To maximize the utility of genomic sequences for these purposes, it is essential that ...[more]

PMID: 24936976

Similar Datasets

Project description:BackgroundSystematic study of clinical phenotypes is important for a better understanding of the genetic basis of human diseases and more effective gene-based disease management. A key aspect in facilitating such studies requires standardized representation of the phenotype data using common data elements (CDEs) and controlled biomedical vocabularies. In this study, the authors analyzed how a limited subset of phenotypic data is amenable to common definition and standardized collection, as well as how their adoption in large-scale epidemiological and genome-wide studies can significantly facilitate cross-study analysis.MethodsThe authors mapped phenotype data dictionaries from five different eMERGE (Electronic Medical Records and Genomics) Network sites studying multiple diseases such as peripheral arterial disease and type 2 diabetes. For mapping, standardized terminological and metadata repository resources, such as the caDSR (Cancer Data Standards Registry and Repository) and SNOMED CT (Systematized Nomenclature of Medicine), were used. The mapping process comprised both lexical (via searching for relevant pre-coordinated concepts and data elements) and semantic (via post-coordination) techniques. Where feasible, new data elements were curated to enhance the coverage during mapping. A web-based application was also developed to uniformly represent and query the mapped data elements from different eMERGE studies.ResultsApproximately 60% of the target data elements (95 out of 157) could be mapped using simple lexical analysis techniques on pre-coordinated terms and concepts before any additional curation of terminology and metadata resources was initiated by eMERGE investigators. After curation of 54 new caDSR CDEs and nine new NCI thesaurus concepts and using post-coordination, the authors were able to map the remaining 40% of data elements to caDSR and SNOMED CT. A web-based tool was also implemented to assist in semi-automatic mapping of data elements.ConclusionThis study emphasizes the requirement for standardized representation of clinical research data using existing metadata and terminology resources and provides simple techniques and software for data element mapping using experiences from the eMERGE Network.

Dataset Information

Standardized metadata for human pathogen/vector genomic sequences.

Publications

Standardized metadata for human pathogen/vector genomic sequences.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure