DOI: 10.5281/zenodo.13682750
title: Exploring the global metaplasmidome: unravelling plasmid landscapes and the spread of antibiotic resistance genes across diverse ecosystems

description: Plasmid content was predicted from assembled data already publicly available or constructed from reads for this study. The assembled data supplied by Pasolli and colleagues (Pasolli et al., 2019) , metasub consortium (Danko et al., 2020) and TARA ocean (Tully et al., 2018) were used for the human microbiome, the built environment and the marine ecosystem respectively. For assembly in the current study, reads from metagenomes were selected from two main databases. For the soil ecosystem, the metagenomes were selected from the dedicated curated database “TerrestrialMetagenomeDB” (Corrêa et al., 2020). 

If the metagenomes were not assembled, reads were assembled by using megahit 1.2.9 with the metalarge option (Li et al., 2015) after cleaning the data with bbduk2 (qtrim=rl trimq=28 minlen=25 maq=20 ktrim=r k=25 mink=11 and a list of adapters to remove) from the bbtools suite (https://jgi.doe.gov/data-and-tools/software-tools/bbtools/).

Plasmids were predicted for each assembly by using both reference-based and reference-free approaches as described in previous works (Hilpert et al., 2021; Hennequin et al., 2022) and available on the github website (https://github.com/meb-team/PlasSuite/). The databases used for the first approach included those for chromosomes (archaea and bacteria) and plasmids from RefSeq, as well as the MOB-suite tool (Robertson and Nash, 2018), SILVA (Quast et al., 2013) and phylogenetic markers hosted by chromosomes (Wu et al., 2013). The database created for this purpose is available at this address https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases. Two reference-free methods were applied to contigs that were not affiliated with chromosomes (discarded) or plasmids (retained in the first step): PlasFlow (Krawczyk et al., 2018) and PlasClass (Pellow et al., 2020). Previously undetected viruses were removed by using ViralVerify (https://github.com/ablab/viralVerify)(Antipov et al., 2020) that provides in parallel plasmid/non-plasmid classification. This step would also remove potential plasmid-phage elements as described by Pfeifer et al.  (Pfeifer et al., 2021), but would minimise false positives. Eukaryotic contamination was removed by aligning the sequences against the NT database and human chromosomes (GRCh38) using minimap2 (Li, 2018) with -x asm5 option. Contigs mapping with 95% identity for at least 80% coverage were removed. The predicted plasmids, hereafter referred as plasmid-like sequences (PLSs), were grouped by "scientific names" (i.e. 27) such as defined in the SRA metadata (air, lake, wetland…) and subsequently named ecosystems. These ecosystems were grouped in 9 biomes (Tab Supplementary 4). The data were then dereplicated by ecosystems using cd-hit-est with a threshold of 99%. The dereplicated PLSs were then clustered using MMseqs2 (Steinegger and Söding, 2017) with 80% of coverage an 90% of identity (--min-seq-id 0.90 -c 0.8 --cov-mode 1 --cluster-mode 2 --alignment-mode 3 --kmer-per-seq-scale 0.2) to define plasmid-like clusters (PLCs).



The PLC sequences are included in the file "predicted_PLC.fasta" and the main features are dercribed in the file "metadata_PLC.tsv"



fasta_id: fasta identification of the PLC

ecosystem: ecosystem from which the PLC originates

biome: biome of the ecosystem

latitude, longitude: GPS coordinate of the ecosystem

length: PLC length

map_markers: plasmid marker genes detected by PlasSuite (Hilpert et al., 2021)

map_ncbi: PLCs present in the RefSeq plasmid database(Hilpert et al., 2021)

nb_genes: Number of genes detected by Prokka implemented in PlasSuite

nb_args: ARGs detected by PlasSuite

plascad: results from plascad (Che et al., 2021)


 

 

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