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EBI Metagenomics in 2017: Enriching the analysis of microbial communities, from sequence reads to assemblies

Lookup NU author(s): Professor Darren Wilkinson, Professor Thomas Curtis

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2017 The Author(s). EBI metagenomics (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the analysis and archiving of sequence data derived from the microbial populations found in a particular environment. Over the past two years, EBI metagenomics has increased the number of datasets analysed 10-fold. In addition to increased throughput, the underlying analysis pipeline has been overhauled to include both new or updated tools and reference databases. Of particular note is a new workflow for taxonomic assignments that has been extended to include assignments based on both the large and small subunit RNA marker genes and to encompass all cellular micro-organisms. We also describe the addition of metagenomic assembly as a new analysis service. Our pilot studies have produced over 2400 assemblies from datasets in the public domain. From these assemblies, we have produced a searchable, non-redundant protein database of over 50 million sequences. To provide improved access to the data stored within the resource, we have developed a programmatic interface that provides access to the analysis results and associated sample metadata. Finally, we have integrated the results of a series of statistical analyses that provide estimations of diversity and sample comparisons.


Publication metadata

Author(s): Mitchell AL, Scheremetjew M, Denise H, Potter S, Tarkowska A, Qureshi M, Salazar GA, Pesseat S, Boland MA, Hunter FMI, Ten Hoopen P, Alako B, Amid C, Wilkinson DJ, Curtis TP, Cochrane G, Finn RD

Publication type: Article

Publication status: Published

Journal: Nucleic Acids Research

Year: 2018

Volume: 46

Issue: D1

Pages: D726-D735

Print publication date: 04/01/2018

Online publication date: 23/10/2017

Acceptance date: 12/10/2017

ISSN (print): 0305-1048

ISSN (electronic): 1362-4962

Publisher: Oxford University Press

URL: https://doi.org/10.1093/nar/gkx967

DOI: 10.1093/nar/gkx967


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