ArticleFrontiers in microbiology2023
plASgraph2: using graph neural networks to detect plasmid contigs from an assembly graph.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network.BMC microbiology · 2026Article
- gplasCC: classification and recovery of plasmids from short-read sequencing data for any bacterial species.NAR genomics and bioinformatics · 2026Article
- Circling in on plasmids: benchmarking plasmid detection and reconstruction tools for short-read data from diverse species.Briefings in bioinformatics · 2025Article
- Plasmids encode and can mobilize onion pathogenicity in Pantoea agglomerans.The ISME journal · 2025Article
- Plaseval: a framework for comparing and evaluating plasmid detection tools.BMC bioinformatics · 2024Article
- Complete and circularized genome sequences of five nitrogen-fixingMicrobiology resource announcements · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
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 data set of samples from the ESKAPEE group of pathogens. 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 study 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. Our software is available at https://github.com/cchauve/plasgraph2 and the training and testing data sets are available at https://github.com/fmfi-compbio/plasgraph2-datasets.
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