Metazen – metadata capture for metagenomes View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Jared Bischof, Travis Harrison, Tobias Paczian, Elizabeth Glass, Andreas Wilke, Folker Meyer

ABSTRACT

BACKGROUND: As the impact and prevalence of large-scale metagenomic surveys grow, so does the acute need for more complete and standards compliant metadata. Metadata (data describing data) provides an essential complement to experimental data, helping to answer questions about its source, mode of collection, and reliability. Metadata collection and interpretation have become vital to the genomics and metagenomics communities, but considerable challenges remain, including exchange, curation, and distribution. Currently, tools are available for capturing basic field metadata during sampling, and for storing, updating and viewing it. Unfortunately, these tools are not specifically designed for metagenomic surveys; in particular, they lack the appropriate metadata collection templates, a centralized storage repository, and a unique ID linking system that can be used to easily port complete and compatible metagenomic metadata into widely used assembly and sequence analysis tools. RESULTS: Metazen was developed as a comprehensive framework designed to enable metadata capture for metagenomic sequencing projects. Specifically, Metazen provides a rapid, easy-to-use portal to encourage early deposition of project and sample metadata. CONCLUSIONS: Metazen is an interactive tool that aids users in recording their metadata in a complete and valid format. A defined set of mandatory fields captures vital information, while the option to add fields provides flexibility. More... »

PAGES

18

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1944-3277-9-18

DOI

http://dx.doi.org/10.1186/1944-3277-9-18

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029309308

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25780508


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137 schema:name Biological Sciences Division, Argonne National Laboratory, 9700 S. Cass Ave, 60439, Argonne, IL, USA
138 Computation Institute, University of Chicago, 5735 S Ellis Ave, 60637, Chicago, IL, USA
139 Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S. Cass Ave, 60439, Argonne, IL, USA
140 rdf:type schema:Organization
 




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