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MGnify: the microbiome analysis resource in 2020
MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than do...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145632/ https://www.ncbi.nlm.nih.gov/pubmed/31696235 http://dx.doi.org/10.1093/nar/gkz1035 |
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author | Mitchell, Alex L Almeida, Alexandre Beracochea, Martin Boland, Miguel Burgin, Josephine Cochrane, Guy Crusoe, Michael R Kale, Varsha Potter, Simon C Richardson, Lorna J Sakharova, Ekaterina Scheremetjew, Maxim Korobeynikov, Anton Shlemov, Alex Kunyavskaya, Olga Lapidus, Alla Finn, Robert D |
author_facet | Mitchell, Alex L Almeida, Alexandre Beracochea, Martin Boland, Miguel Burgin, Josephine Cochrane, Guy Crusoe, Michael R Kale, Varsha Potter, Simon C Richardson, Lorna J Sakharova, Ekaterina Scheremetjew, Maxim Korobeynikov, Anton Shlemov, Alex Kunyavskaya, Olga Lapidus, Alla Finn, Robert D |
author_sort | Mitchell, Alex L |
collection | PubMed |
description | MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations. |
format | Online Article Text |
id | pubmed-7145632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-71456322020-04-13 MGnify: the microbiome analysis resource in 2020 Mitchell, Alex L Almeida, Alexandre Beracochea, Martin Boland, Miguel Burgin, Josephine Cochrane, Guy Crusoe, Michael R Kale, Varsha Potter, Simon C Richardson, Lorna J Sakharova, Ekaterina Scheremetjew, Maxim Korobeynikov, Anton Shlemov, Alex Kunyavskaya, Olga Lapidus, Alla Finn, Robert D Nucleic Acids Res Database Issue MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations. Oxford University Press 2020-01-08 2019-11-07 /pmc/articles/PMC7145632/ /pubmed/31696235 http://dx.doi.org/10.1093/nar/gkz1035 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://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 Mitchell, Alex L Almeida, Alexandre Beracochea, Martin Boland, Miguel Burgin, Josephine Cochrane, Guy Crusoe, Michael R Kale, Varsha Potter, Simon C Richardson, Lorna J Sakharova, Ekaterina Scheremetjew, Maxim Korobeynikov, Anton Shlemov, Alex Kunyavskaya, Olga Lapidus, Alla Finn, Robert D MGnify: the microbiome analysis resource in 2020 |
title | MGnify: the microbiome analysis resource in 2020 |
title_full | MGnify: the microbiome analysis resource in 2020 |
title_fullStr | MGnify: the microbiome analysis resource in 2020 |
title_full_unstemmed | MGnify: the microbiome analysis resource in 2020 |
title_short | MGnify: the microbiome analysis resource in 2020 |
title_sort | mgnify: the microbiome analysis resource in 2020 |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145632/ https://www.ncbi.nlm.nih.gov/pubmed/31696235 http://dx.doi.org/10.1093/nar/gkz1035 |
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