Unknown

Dataset Information

0

Adversarial and variational autoencoders improve metagenomic binning.


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.

SUBMITTER: Lindez PP 

PROVIDER: S-EPMC10590447 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

altmetric image

Publications

Adversarial and variational autoencoders improve metagenomic binning.

Líndez Pau Piera PP   Johansen Joachim J   Kutuzova Svetlana S   Sigurdsson Arnor Ingi AI   Nissen Jakob Nybo JN   Rasmussen Simon S  

Communications biology 20231021 1


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 tetranucleo  ...[more]

Similar Datasets

| S-EPMC9910304 | biostudies-literature
| S-EPMC7506068 | biostudies-literature
| S-EPMC8605902 | biostudies-literature
| S-EPMC10553230 | biostudies-literature
| S-EPMC7946179 | biostudies-literature
| S-EPMC11340721 | biostudies-literature
| S-EPMC9246987 | biostudies-literature
| S-EPMC9813669 | biostudies-literature
| S-EPMC10148837 | biostudies-literature
| S-EPMC9710582 | biostudies-literature