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MGnify: the microbiome sequence data analysis resource in 2023
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, metatran...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825492/ https://www.ncbi.nlm.nih.gov/pubmed/36477304 http://dx.doi.org/10.1093/nar/gkac1080 |
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author | Richardson, Lorna Allen, Ben Baldi, Germana Beracochea, Martin Bileschi, Maxwell L Burdett, Tony Burgin, Josephine Caballero-Pérez, Juan Cochrane, Guy Colwell, Lucy J Curtis, Tom Escobar-Zepeda, Alejandra Gurbich, Tatiana A Kale, Varsha Korobeynikov, Anton Raj, Shriya Rogers, Alexander B Sakharova, Ekaterina Sanchez, Santiago Wilkinson, Darren J Finn, Robert D |
author_facet | Richardson, Lorna Allen, Ben Baldi, Germana Beracochea, Martin Bileschi, Maxwell L Burdett, Tony Burgin, Josephine Caballero-Pérez, Juan Cochrane, Guy Colwell, Lucy J Curtis, Tom Escobar-Zepeda, Alejandra Gurbich, Tatiana A Kale, Varsha Korobeynikov, Anton Raj, Shriya Rogers, Alexander B Sakharova, Ekaterina Sanchez, Santiago Wilkinson, Darren J Finn, Robert D |
author_sort | Richardson, Lorna |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9825492 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98254922023-01-10 MGnify: the microbiome sequence data analysis resource in 2023 Richardson, Lorna Allen, Ben Baldi, Germana Beracochea, Martin Bileschi, Maxwell L Burdett, Tony Burgin, Josephine Caballero-Pérez, Juan Cochrane, Guy Colwell, Lucy J Curtis, Tom Escobar-Zepeda, Alejandra Gurbich, Tatiana A Kale, Varsha Korobeynikov, Anton Raj, Shriya Rogers, Alexander B Sakharova, Ekaterina Sanchez, Santiago Wilkinson, Darren J Finn, Robert D Nucleic Acids Res Database Issue 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. Oxford University Press 2022-12-07 /pmc/articles/PMC9825492/ /pubmed/36477304 http://dx.doi.org/10.1093/nar/gkac1080 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Database Issue Richardson, Lorna Allen, Ben Baldi, Germana Beracochea, Martin Bileschi, Maxwell L Burdett, Tony Burgin, Josephine Caballero-Pérez, Juan Cochrane, Guy Colwell, Lucy J Curtis, Tom Escobar-Zepeda, Alejandra Gurbich, Tatiana A Kale, Varsha Korobeynikov, Anton Raj, Shriya Rogers, Alexander B Sakharova, Ekaterina Sanchez, Santiago Wilkinson, Darren J Finn, Robert D MGnify: the microbiome sequence data analysis resource in 2023 |
title | MGnify: the microbiome sequence data analysis resource in 2023 |
title_full | MGnify: the microbiome sequence data analysis resource in 2023 |
title_fullStr | MGnify: the microbiome sequence data analysis resource in 2023 |
title_full_unstemmed | MGnify: the microbiome sequence data analysis resource in 2023 |
title_short | MGnify: the microbiome sequence data analysis resource in 2023 |
title_sort | mgnify: the microbiome sequence data analysis resource in 2023 |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825492/ https://www.ncbi.nlm.nih.gov/pubmed/36477304 http://dx.doi.org/10.1093/nar/gkac1080 |
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