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Adversarial and variational autoencoders improve metagenomic binning
Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by re-grouping the sequences by their organism of origin, thus representing a crucial processing step when exploring the biologic...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590447/ https://www.ncbi.nlm.nih.gov/pubmed/37865678 http://dx.doi.org/10.1038/s42003-023-05452-3 |
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author | Líndez, Pau Piera Johansen, Joachim Kutuzova, Svetlana Sigurdsson, Arnor Ingi Nissen, Jakob Nybo Rasmussen, Simon |
author_facet | Líndez, Pau Piera Johansen, Joachim Kutuzova, Svetlana Sigurdsson, Arnor Ingi Nissen, Jakob Nybo Rasmussen, Simon |
author_sort | Líndez, Pau Piera |
collection | PubMed |
description | Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by re-grouping the sequences by their organism of origin, thus representing a crucial processing step when exploring the biological diversity of metagenomic samples. Here we present Adversarial Autoencoders for Metagenomics Binning (AAMB), an ensemble deep learning approach that integrates sequence co-abundances and tetranucleotide frequencies into a common denoised space that enables precise clustering of sequences into microbial genomes. When benchmarked, AAMB presented similar or better results compared with the state-of-the-art reference-free binner VAMB, reconstructing ~7% more near-complete (NC) genomes across simulated and real data. In addition, genomes reconstructed using AAMB had higher completeness and greater taxonomic diversity compared with VAMB. Finally, we implemented a pipeline Integrating VAMB and AAMB that enabled improved binning, recovering 20% and 29% more simulated and real NC genomes, respectively, compared to VAMB, with moderate additional runtime. |
format | Online Article Text |
id | pubmed-10590447 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105904472023-10-23 Adversarial and variational autoencoders improve metagenomic binning Líndez, Pau Piera Johansen, Joachim Kutuzova, Svetlana Sigurdsson, Arnor Ingi Nissen, Jakob Nybo Rasmussen, Simon Commun Biol Article Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by re-grouping the sequences by their organism of origin, thus representing a crucial processing step when exploring the biological diversity of metagenomic samples. Here we present Adversarial Autoencoders for Metagenomics Binning (AAMB), an ensemble deep learning approach that integrates sequence co-abundances and tetranucleotide frequencies into a common denoised space that enables precise clustering of sequences into microbial genomes. When benchmarked, AAMB presented similar or better results compared with the state-of-the-art reference-free binner VAMB, reconstructing ~7% more near-complete (NC) genomes across simulated and real data. In addition, genomes reconstructed using AAMB had higher completeness and greater taxonomic diversity compared with VAMB. Finally, we implemented a pipeline Integrating VAMB and AAMB that enabled improved binning, recovering 20% and 29% more simulated and real NC genomes, respectively, compared to VAMB, with moderate additional runtime. Nature Publishing Group UK 2023-10-21 /pmc/articles/PMC10590447/ /pubmed/37865678 http://dx.doi.org/10.1038/s42003-023-05452-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Líndez, Pau Piera Johansen, Joachim Kutuzova, Svetlana Sigurdsson, Arnor Ingi Nissen, Jakob Nybo Rasmussen, Simon Adversarial and variational autoencoders improve metagenomic binning |
title | Adversarial and variational autoencoders improve metagenomic binning |
title_full | Adversarial and variational autoencoders improve metagenomic binning |
title_fullStr | Adversarial and variational autoencoders improve metagenomic binning |
title_full_unstemmed | Adversarial and variational autoencoders improve metagenomic binning |
title_short | Adversarial and variational autoencoders improve metagenomic binning |
title_sort | adversarial and variational autoencoders improve metagenomic binning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590447/ https://www.ncbi.nlm.nih.gov/pubmed/37865678 http://dx.doi.org/10.1038/s42003-023-05452-3 |
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