| Autor: |
Pau Piera Líndez, Joachim Johansen, Svetlana Kutuzova, Arnor Ingi Sigurdsson, Jakob Nybo Nissen, Simon Rasmussen |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Communications Biology, Vol 6, Iss 1, Pp 1-10 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2399-3642 |
| DOI: |
10.1038/s42003-023-05452-3 |
| Popis: |
Abstract 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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