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Current state-of-the-art techniques for metage- nomic binning only utilize local features for the individual DNA sequences (contigs), neglecting additional information such as the assembly graph, in which the contigs are connected according to overlapping reads, and gene markers identified in the contigs. In this paper, we propose the use of a Variational AutoEncoder (VAE) tailored to leverage auxiliary structural information about contig relations when learning contig representations for subsequent metagenomic binning. Our method, CCVAE, improves on previous work that used VAEs for learning latent representations of the individual contigs, by constraining these representations according to the connectivity information from the assembly graph. Additionally, we incor- porate into the model additional information in the form of marker genes to better differentiate contigs from different genomes. Our experiments on both simulated and real-world datasets demon- strate that CCVAE outperforms current state-of- the-art techniques, thus providing a more effective method for metagenomic binning.
|Title of host publication
|International Conference on Machine Learning (ICML)
|Published - 2023
|ICML'23: International Conference on Machine Learning - Honolulu, United States
Duration: 23 Jul 2023 → 29 Jul 2023
|ICML'23: International Conference on Machine Learning
|23/07/2023 → 29/07/2023
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01/11/2019 → 31/10/2024