plASgraph2: using graph neural networks to detect plasmid contigs from an assembly graph - Sielemann, Janik; Sielemann, Katharina; Brejová, Broňa; Vinař, Tomáš; Chauve, Cedric (Frontiers in Microbiology, 2023)
Identification of plasmids from sequencing data is an important and challenging problem related to antimicrobial resistance spread and other One-Health issues. We provide a new architecture for identifying plasmid contigs in fragmented genome assemblies built from short-read data. We employ Graph Neural Networks (GNNs) and the assembly graph to propagate the information from nearby nodes, which leads to more accurate classification, especially for short contigs that are difficult to classify based on sequence features or database searches alone. We trained plASgraph2 on a dataset of samples from the ESKAPEE group of pathogens and evaluated the performance of the trained model on two testing sets of ESKAPEE and non-ESKAPEE samples. plASgraph2 either outperforms or performs on par with a wide range of state-of-the-art methods on testing sets of independent ESKAPEE samples and samples from related pathogens. On one hand, our work provides a new accurate and easy to use tool for contig classification in bacterial isolates; on the other hand, it serves as a proof-of-concept for the use of GNNs in genomics.