Metagenomic binning with assembly graph embeddings.

ABSTRACT:

Motivation

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.

Results

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 binn

SUBMITTER: Lamurias A 

PROVIDER: S-EPMC9525014 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature