plASgraph2: using graph neural networks to detect plasmid contigs from an assembly graph

Frontiers in Microbiology

Microbiology

Janik Sielemann¹  Tomáš Vinař²  Broňa Brejová³  Cedric Chauve⁴  Katharina Sielemann⁵ 

[1] Computational Biology, Faculty of Biology, Center for Biotechnology & Graduate School Digital Infrastructures for the Life Sciences (DILS), Bielefeld Institute for Bioinformatics Infrastructure, Bielefeld University, Bielefeld, Germany;Department of Applied Informatics, Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia;Department of Computer Science, Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia;Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada;Genetics and Genomics of Plants, Faculty of Biology, Center for Biotechnology & Graduate School Digital Infrastructures for the Life Sciences (DILS), Bielefeld Institute for Bioinformatics Infrastructure, Bielefeld University, Bielefeld, Germany;

关键词: bioinformatics;    machine learning (ML);    classification;    plasmids;    assembly graph;   

DOI  :  10.3389/fmicb.2023.1267695

 received in 2023-07-26, accepted in 2023-09-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Sielemann, Sielemann, Brejová, Vinař and Chauve.