  Microbes have a profound impact on our health and environment, but our
understanding of the diversity and function of microbial communities is
severely limited. Through DNA sequencing of microbial communities
(metagenomics), DNA fragments (reads) of the individual microbes can be
obtained, which through assembly graphs can be combined into long contiguous
DNA sequences (contigs). Given the complexity of microbial communities, single
contig microbial genomes are rarely obtained. Instead, contigs are eventually
clustered into bins, with each bin ideally making up a full genome. This
process is referred to as metagenomic binning.
  Current state-of-the-art techniques for metagenomic binning rely only on the
local features for the individual contigs. These techniques therefore fail to
exploit the similarities between contigs as encoded by the assembly graph, in
which the contigs are organized. In this paper, we propose to use Graph Neural
Networks (GNNs) to leverage the assembly graph when learning contig
representations for metagenomic binning. Our method, VaeG-Bin, combines
variational autoencoders for learning latent representations of the individual
contigs, with GNNs for refining these representations by taking into account
the neighborhood structure of the contigs in the assembly graph. We explore
several types of GNNs and demonstrate that VaeG-Bin recovers more high-quality
genomes than other state-of-the-art binners on both simulated and real-world
datasets.

Genomics;Machine Learning;Quantitative Methods