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Metagenomic binning with assembly graph embeddings

View ORCID ProfileAndre Lamurias, View ORCID ProfileMantas Sereika, View ORCID ProfileMads Albertsen, View ORCID ProfileKatja Hose, View ORCID ProfileThomas Dyhre Nielsen
doi: https://doi.org/10.1101/2022.02.25.481923
Andre Lamurias
1Department of Computer Science, Aalborg University, Aalborg, Denmark
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  • For correspondence: andrel@cs.aau.dk
Mantas Sereika
2Center for Microbial Communities, Aalborg University, Denmark February 25, 2022
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Mads Albertsen
2Center for Microbial Communities, Aalborg University, Denmark February 25, 2022
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Katja Hose
1Department of Computer Science, Aalborg University, Aalborg, Denmark
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Thomas Dyhre Nielsen
1Department of Computer Science, Aalborg University, Aalborg, Denmark
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Abstract

Despite recent advancements in sequencing technologies and assembly methods, obtaining high-quality microbial genomes from metagenomic samples is still not a trivial task. Current metagenomic binners do not take full advantage of assembly graphs and are not optimized for long-read assemblies. Deep graph learning algorithms have been proposed in other fields to deal with complex graph data structures. The graph structure generated during the assembly process could be integrated with contig features to obtain better bins with deep learning.

We propose GraphMB, which uses graph neural networks to incorporate the assembly graph into the binning process. We test GraphMB on long-read datasets of different complexities, and compare the performance with other binners in terms of the number of High Quality (HQ) genome bins obtained. With our approach, we were able to obtain unique bins on all real datasets, and obtain more bins on most datasets. In particular, we obtained on average 17.5% more HQ bins when compared to state-of-the-art binners and 13.7% when aggregating the results of our binner with the others. These results indicate that a deep learning model can integrate contig-specific and graph-structure information to improve metagenomic binning. GraphMB is available from https://github.com/MicrobialDarkMatter/GraphMB.

Competing Interest Statement

MA is employed at DNASense ApS that consults and performs sequencing. The remaining authors declare no conflict of interest.

Footnotes

  • https://github.com/MicrobialDarkMatter/GraphMB

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 27, 2022.
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Metagenomic binning with assembly graph embeddings
Andre Lamurias, Mantas Sereika, Mads Albertsen, Katja Hose, Thomas Dyhre Nielsen
bioRxiv 2022.02.25.481923; doi: https://doi.org/10.1101/2022.02.25.481923
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Metagenomic binning with assembly graph embeddings
Andre Lamurias, Mantas Sereika, Mads Albertsen, Katja Hose, Thomas Dyhre Nielsen
bioRxiv 2022.02.25.481923; doi: https://doi.org/10.1101/2022.02.25.481923

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