The M5nr: a novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools

By Andreas Wilke, Travis Harrison, Jared Wilkening, Dawn Field, Elizabeth M. Glass, Nikos Kyrpides, Konstantinos Mavrommatis, Folker Meyer. BMC Bioinformatics, 2012

305 citations recorded by the source corpus.

Computing of sequence similarity results is becoming a limiting factor in metagenome analysis. Sequence similarity search results encoded in an open, exchangeable format have the potential to limit the needs for computational reanalysis of these data sets. A prerequisite for sharing of similarity results is a common reference. We introduce a mechanism for automatically maintaining a comprehensive, non-redundant protein database and for creating a quarterly release of this resource. In addition, we present tools for translating similarity searches into many annotation namespaces, e.g. KEGG or NCBI's GenBank. The data and tools we present allow the creation of multiple result sets using a single computation, permitting computational results to be shared between groups for large sequence data sets.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d8cf6a4cb74eb9b67facb7e6fb7fc419377a9b2b

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In Molecular Biology

1. Travis Harrison
2. Elizabeth M. Glass
3. Folker Meyer
4. Andreas Wilke
5. Rob Knight

Citations in the corpus

• The MG-RAST metagenomics database and portal in 2015 (2015, methodology): “The M5nr database (8) allows user selection of annotation ‘namespace,’ for example, GenBank, SEED subsystems (9), or KEGG orthologs (10).”