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A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications
Metagenomics is a culture-independent method for studying the microbes inhabiting a particular environment. Comparing the composition of samples (functionally/taxonomically), either from a longitudinal study or cross-sectional studies, can provide clues into how the microbiota has adapted to the env...
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/PMC9366992/ https://www.ncbi.nlm.nih.gov/pubmed/35950838 http://dx.doi.org/10.1093/gigascience/giac077 |
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author | Nassar, Maaly Rogers, Alexander B Talo', Francesco Sanchez, Santiago Shafique, Zunaira Finn, Robert D McEntyre, Johanna |
author_facet | Nassar, Maaly Rogers, Alexander B Talo', Francesco Sanchez, Santiago Shafique, Zunaira Finn, Robert D McEntyre, Johanna |
author_sort | Nassar, Maaly |
collection | PubMed |
description | Metagenomics is a culture-independent method for studying the microbes inhabiting a particular environment. Comparing the composition of samples (functionally/taxonomically), either from a longitudinal study or cross-sectional studies, can provide clues into how the microbiota has adapted to the environment. However, a recurring challenge, especially when comparing results between independent studies, is that key metadata about the sample and molecular methods used to extract and sequence the genetic material are often missing from sequence records, making it difficult to account for confounding factors. Nevertheless, these missing metadata may be found in the narrative of publications describing the research. Here, we describe a machine learning framework that automatically extracts essential metadata for a wide range of metagenomics studies from the literature contained in Europe PMC. This framework has enabled the extraction of metadata from 114,099 publications in Europe PMC, including 19,900 publications describing metagenomics studies in European Nucleotide Archive (ENA) and MGnify. Using this framework, a new metagenomics annotations pipeline was developed and integrated into Europe PMC to regularly enrich up-to-date ENA and MGnify metagenomics studies with metadata extracted from research articles. These metadata are now available for researchers to explore and retrieve in the MGnify and Europe PMC websites, as well as Europe PMC annotations API. |
format | Online Article Text |
id | pubmed-9366992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-93669922022-08-12 A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications Nassar, Maaly Rogers, Alexander B Talo', Francesco Sanchez, Santiago Shafique, Zunaira Finn, Robert D McEntyre, Johanna Gigascience Research Metagenomics is a culture-independent method for studying the microbes inhabiting a particular environment. Comparing the composition of samples (functionally/taxonomically), either from a longitudinal study or cross-sectional studies, can provide clues into how the microbiota has adapted to the environment. However, a recurring challenge, especially when comparing results between independent studies, is that key metadata about the sample and molecular methods used to extract and sequence the genetic material are often missing from sequence records, making it difficult to account for confounding factors. Nevertheless, these missing metadata may be found in the narrative of publications describing the research. Here, we describe a machine learning framework that automatically extracts essential metadata for a wide range of metagenomics studies from the literature contained in Europe PMC. This framework has enabled the extraction of metadata from 114,099 publications in Europe PMC, including 19,900 publications describing metagenomics studies in European Nucleotide Archive (ENA) and MGnify. Using this framework, a new metagenomics annotations pipeline was developed and integrated into Europe PMC to regularly enrich up-to-date ENA and MGnify metagenomics studies with metadata extracted from research articles. These metadata are now available for researchers to explore and retrieve in the MGnify and Europe PMC websites, as well as Europe PMC annotations API. Oxford University Press 2022-08-11 /pmc/articles/PMC9366992/ /pubmed/35950838 http://dx.doi.org/10.1093/gigascience/giac077 Text en © The Author(s) 2022. Published by Oxford University Press GigaScience. 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 | Research Nassar, Maaly Rogers, Alexander B Talo', Francesco Sanchez, Santiago Shafique, Zunaira Finn, Robert D McEntyre, Johanna A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications |
title | A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications |
title_full | A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications |
title_fullStr | A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications |
title_full_unstemmed | A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications |
title_short | A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications |
title_sort | machine learning framework for discovery and enrichment of metagenomics metadata from open access publications |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366992/ https://www.ncbi.nlm.nih.gov/pubmed/35950838 http://dx.doi.org/10.1093/gigascience/giac077 |
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