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Sequencing data discovery with MetaSeek.
Hoarfrost, Adrienne; Brown, Nick; Brown, C Titus; Arnosti, Carol.
Affiliation
  • Hoarfrost A; Department of Marine Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Brown N; Independent Researcher, Durham, NC, USA.
  • Brown CT; Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA 95616, USA.
  • Arnosti C; Department of Marine Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Bioinformatics ; 35(22): 4857-4859, 2019 11 01.
Article in En | MEDLINE | ID: mdl-31225863
SUMMARY: Sequencing data resources have increased exponentially in recent years, as has interest in large-scale meta-analyses of integrated next-generation sequencing datasets. However, curation of integrated datasets that match a user's particular research priorities is currently a time-intensive and imprecise task. MetaSeek is a sequencing data discovery tool that enables users to flexibly search and filter on any metadata field to quickly find the sequencing datasets that meet their needs. MetaSeek automatically scrapes metadata from all publicly available datasets in the Sequence Read Archive, cleans and parses messy, user-provided metadata into a structured, standard-compliant database and predicts missing fields where possible. MetaSeek provides a web-based graphical user interface and interactive visualization dashboard, as well as a programmatic API to rapidly search, filter, visualize, save, share and download matching sequencing metadata. AVAILABILITY AND IMPLEMENTATION: The MetaSeek online interface is available at https://www.metaseek.cloud/. The MetaSeek database can also be accessed via API to programmatically search, filter and download all metadata. MetaSeek source code, metadata scrapers and documents are available at https://github.com/MetaSeek-Sequencing-Data-Discovery/metaseek/.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: Software / Metadata Language: En Journal: Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2019 Type: Article Affiliation country: United States

Full text: 1 Database: MEDLINE Main subject: Software / Metadata Language: En Journal: Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2019 Type: Article Affiliation country: United States