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Lookup NU author(s): Dr Ben Allen, Professor Thomas CurtisORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. The MGnify platform (https://www.ebi.ac.uk/metagenomics) facilitates the assembly, analysis and archiving of microbiome-derived nucleic acid sequences. The platform provides access to taxonomic assignments and functional annotations for nearly half a million analyses covering metabarcoding, metatranscriptomic, and metagenomic datasets, which are derived from a wide range of different environments. Over the past 3 years, MGnify has not only grown in terms of the number of datasets contained but also increased the breadth of analyses provided, such as the analysis of long-read sequences. The MGnify protein database now exceeds 2.4 billion non-redundant sequences predicted from metagenomic assemblies. This collection is now organised into a relational database making it possible to understand the genomic context of the protein through navigation back to the source assembly and sample metadata, marking a major improvement. To extend beyond the functional annotations already provided in MGnify, we have applied deep learning-based annotation methods. The technology underlying MGnify's Application Programming Interface (API) and website has been upgraded, and we have enabled the ability to perform downstream analysis of the MGnify data through the introduction of a coupled Jupyter Lab environment.
Author(s): Richardson L, Allen B, Baldi G, Beracochea M, Bileschi ML, Burdett T, Burgin J, Caballero-Perez J, Cochrane G, Colwell LJ, Curtis T, Escobar-Zepeda A, Gurbich TA, Kale V, Korobeynikov A, Raj S, Rogers AB, Sakharova E, Sanchez S, Wilkinson DJ, Finn RD
Publication type: Article
Publication status: Published
Journal: Nucleic Acids Research
Year: 2023
Volume: 51
Issue: D1
Pages: D753-D759
Print publication date: 06/01/2023
Online publication date: 07/12/2022
Acceptance date: 01/11/2022
Date deposited: 23/01/2023
ISSN (print): 0305-1048
ISSN (electronic): 1362-4962
Publisher: Oxford University Press
URL: https://doi.org/10.1093/nar/gkac1080
DOI: 10.1093/nar/gkac1080
PubMed id: 36477304
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