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A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications
Open accessPublished version of a preprint
Abstract
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.
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020) · arXiv · doi:10.48550/arxiv.2003.10555 · Cited by 1,844
The Integrative Human Microbiome Project
The Integrative HMP (iHMP) Research Network Consortium, Proctor, L. M., Creasy, H. H., Fettweis, J. M., Lloyd-Price, J., Mahurkar, A., Zhou, W., Buck, G. A., Snyder, M. P., Strauss, J. F., et al. (2019) · Nature · doi:10.1038/s41586-019-1238-8 · Cited by 1,381
Host variables confound gut microbiota studies of human disease
Vujkovic-Cvijin, I., Sklar, J., Jiang, L., Natarajan, L., Knight, R., & Belkaid, Y. (2020) · Nature · doi:10.1038/s41586-020-2881-9 · Cited by 614
Flagged first, then by citations
Research works printed but not linked: 36
- Reitmeier, 2020, Arrhythmic gut microbiome signatures predict risk of type 2 diabetes, Cell Host Microbe, 28, 258, 10.1016/j.chom.2020.06.004 · DOI
- Oh, 2020, A universal gut-microbiome-derived signature predicts cirrhosis, Cell Metab, 32, 901, 10.1016/j.cmet.2020.10.015 · DOI
- Weber, 2020, Microbial signatures of protected and impacted Northern Caribbean reefs: changes from Cuba to the Florida Keys, Environ Microbiol, 22, 499, 10.1111/1462-2920.14870 · DOI
- Forslund, 2015, Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota, Nature, 528, 262, 10.1038/nature15766 · DOI · Paper page
- Nayfach, 2016, Toward accurate and quantitative comparative metagenomics, Cell, 166, 1103, 10.1016/j.cell.2016.08.007 · DOI
- Mirzayi, 2021, Reporting guidelines for human microbiome research: the STORMS checklist, Nat Med, 27, 1885, 10.1038/s41591-021-01552-x · DOI
- Yilmaz, 2011, The genomic standards consortium: bringing standards to life for microbial ecology, ISME J, 5, 1565, 10.1038/ismej.2011.39 · DOI
- Kodama, 2012, The sequence read archive: explosive growth of sequencing data, Nucleic Acids Res, 40, D54, 10.1093/nar/gkr854 · DOI · Paper page
- Harrison, 2021, The European Nucleotide Archive in 2020, Nucleic Acids Res, 49, D82, 10.1093/nar/gkaa1028 · DOI
- Mitchell, 2018, EBI Metagenomics in 2017: enriching the analysis of microbial communities, from sequence reads to assemblies, Nucleic Acids Res, 46, D726, 10.1093/nar/gkx967 · DOI
- Wilke, 2016, The MG-RAST metagenomics database and portal in 2015, Nucleic Acids Res, 44, D590, 10.1093/nar/gkv1322 · DOI
- Chen, 2017, IMG/M: integrated genome and metagenome comparative data analysis system, Nucleic Acids Res, 45, D507, 10.1093/nar/gkw929 · DOI
- Corrêa, 2020, TerrestrialMetagenomeDB: a public repository of curated and standardized metadata for terrestrial metagenomes, Nucleic Acids Res, 48, D626
- Kasmanas, 2021, HumanMetagenomeDB: a public repository of curated and standardized metadata for human metagenomes, Nucleic Acids Res, 49, D743, 10.1093/nar/gkaa1031 · DOI
- Mukherjee, Genomes OnLine Database (GOLD) v.8: overview and updates, Nucleic Acids Res, 49, D723
- Buttigieg, 2013, The environment ontology: contextualising biological and biomedical entities, J Biomed Semantics, 4, 43, 10.1186/2041-1480-4-43 · DOI
- Bagheri, 2020, Detecting and correcting misclassified sequences in the large-scale public databases, Bioinformatics, 36, 4699, 10.1093/bioinformatics/btaa586 · DOI
- Pappas, 2022, PREGO: a literature and data-mining resource to associate microorganisms, biological processes, and environment types, Microorganisms, 10, 293, 10.3390/microorganisms10020293 · DOI
- Chaix, 2019, Text mining tools for extracting information about microbial biodiversity in food, Food Microbiol, 81, 63, 10.1016/j.fm.2018.04.011 · DOI
- Arighi, 2014, BioCreative-IV virtual issue, Database, bau039
- Mikolov, 2013, Distributed representations of words and phrases and their compositionality, Adv Neural Info Process Syst
- Mikolov, 2013, Efficient estimation of word representations in vector space, 1st International Conference on Learning Representations, ICLR 2013—Workshop Track Proceedings
- Habibi, 2017, Deep learning with word embeddings improves biomedical named entity recognition, Bioinformatics, 33, i37, 10.1093/bioinformatics/btx228 · DOI · Paper page
- Giorgi, 2018, Transfer learning for biomedical named entity recognition with neural networks, Bioinformatics, 34, 4087, 10.1093/bioinformatics/bty449 · DOI
- Luo, 2018, An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition, Bioinformatics, 34, 1381, 10.1093/bioinformatics/btx761 · DOI
- Dang, 2018, D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information, 34, 3539
- Xu, 2019, Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition, Comput Biol Med, 108, 122, 10.1016/j.compbiomed.2019.04.002 · DOI
- Vaswani, Attention is all you need, Adv Neural Info Process Syst, 2017-December, 5999
- Lee, 2019, BioBERT: a pre-trained biomedical language representation model for biomedical text mining, Bioinformatics, 36, 1234, 10.1093/bioinformatics/btz682 · DOI · Paper page
- Fries, 2021, Ontology-driven weak supervision for clinical entity classification in electronic health records, Nat Commun, 12, 1, 10.1038/s41467-021-22328-4 · DOI
- Ferguson, 2021, Europe PMC in 2020, Nucleic Acids Res, 49, D1507, 10.1093/nar/gkaa994 · DOI
- Le, 2014, Distributed representations of sentences and documents, 31st International Conference on Machine Learning, ICML, 4, 2931
- Malik-Sheriff, 2020, BioModels—15 years of sharing computational models in life science, Nucleic Acids Res, 48, D407
- Xie, 2019, Self-training with Noisy Student improves ImageNet classification, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 10684
- Yilmaz, 2011, Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications, Nat Biotechnol, 29, 415, 10.1038/nbt.1823 · DOI
- Callahan, 2017, Exact sequence variants should replace operational taxonomic units in marker-gene data analysis, ISME J, 11, 2639, 10.1038/ismej.2017.119 · DOI
Metadata
- Authors
- Nassar, M., Rogers, A. B., Talo', F., Sanchez, S., Shafique, Z., Finn, R. D., & McEntyre, J.
- Affiliations
- European Bioinformatics Institute (GB)
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- Corresponding author
- McEntyre, Johanna
- ORCID
- Venue
- GigaScience
- Publisher
- Oxford University Press (OUP)
- Published
- 2022
- Volume
- 11
- ISSN
- 2047-217X (ISSN-L)
- PMC
- PMC9366992
- OpenAlex
- W4291017647
- Cited by
- 13
- References
- 5
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 2 · 2023: 4 · 2022: 1
- FWCI
- 0.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- GigaScience (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (221523)
- Biotechnology and Biological Sciences Research Council (BB/S009043/1)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- machine learning, open access publications, Europe PMC, MGnify, European Nucleotide Archive, metadata enrichment, confounding factors
- MeSH terms
- Machine Learning, Metadata, Cross-Sectional Studies, Longitudinal Studies, Access to Information, Metagenomics
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