Package: cb-platon
Platon: identification and characterization of bacterial plasmid contigs from short-read draft assembliesContentsDescriptionInput/OutputInstallationBioCondaGitHub/PipUsageExamplesModeDatabaseDependenciesCitationIssuesDescriptionTL;DRPlaton detects plasmid-borne contigs within bacterial draft (meta) genomes assemblies. Therefore, Platon analyzes the distribution bias of protein-coding gene families among chromosomes and plasmids. This analysis is complemented by comprehensive contig characterizations followed by heuristic filters.Platon conducts three analysis steps:It predicts and searches protein sequences against a custom and pre-computed database comprising marker protein sequences (MPS) and related replicon distribution scores (RDS). These scores express the empirically measured bias of protein sequence family distributions among plasmids and chromosomes pre-computed on complete NCBI RefSeq replicons. Platon calculates the mean RDS for each contig and either classifies them as chromosome if the RDS is below a sensitivity cutoff determined to 95% sensitivity or as plasmid if the RDS is above a specificity cutoff determined to 99.9% specificity. Exact values for these thresholds have been computed based on Monte Carlo simulations of artifical replicon fragments created from complete RefSeq chromosome and plasmid sequences.Contigs passing the sensitivity filter get comprehensivley characterized. Hereby, Platon tries to circularize the contig sequences, searches for rRNA, replication, mobilization and conjugation genes, oriT sequences, incompatibility group DNA probes and finally performs a BLAST+ search against the NCBI plasmid database.Finally, to increase the overall sensitivity, Platon classifies all remaining contigs based on the gathered information by several heuristics.Fig: Replicon distribution and alignment hit frequencies of MPS. Shown are summed plasmid and chromosome alignment hit frequencies per MPS plotted against plasmid/chromosome hit count ratios scaled to [-1 (chromosome), 1 (plasmid)]; Hue: normalized RDS values (min=-100, max=100), hit count outliers below 10-4 and above 1 are discarded for the sake of readability.Input/OutputInputPlaton accepts draft (meta) genome assemblies in fasta format. If contigs have been assembled with SPAdes, Platon is able to extract the coverage information from the contig names.OutputFor each contig classified as plasmid sequence the following columns are printed toSTDOUTas tab separated values:Contig IDLengthCoverage# ORFsRDSCircularityIncompatibility Type(s)# Replication Genes# Mobilization Genes# OriT Sequences# Conjugation Genes# rRNA Genes# Plasmid Database HitsIn addition, Platon writes the following files into the output directory:<prefix>.plasmid.fasta: contigs classified as plasmids or plasmodal origin<prefix>.chromosome.fasta: contigs classified as chromosomal origin<prefix>.tsv: dense information as printed to STDOUT (see above)<prefix>.json: comprehensive results and information on each single plasmid contig.
All files are prefixed (<prefix>) as the input genome fasta file.InstallationPlaton can be installed via BioConda or Pip.
However, we encourage to useCondato automatically install all required 3rd party dependencies. In all cases a mandatorydatabasemust be downloaded.BioConda$condainstall-cconda-forge-cbioconda-cdefaultsplatonPip$python3-mpipinstall--usercb-platonPlaton requires the following 3rd party executables which must be installed & executable:Prodigal (2.6.3)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2848648https://github.com/hyattpd/ProdigalDiamond (2.0.6)https://pubmed.ncbi.nlm.nih.gov/25402007http://www.diamondsearch.orgBlast+ (2.10.1)https://www.ncbi.nlm.nih.gov/pubmed/2231712https://blast.ncbi.nlm.nih.govMUMmer (4.0.0-beta2)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC395750/https://github.com/gmarcais/mummerHMMER (3.3.1)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3695513/http://hmmer.org/INFERNAL (1.1.4)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810854http://eddylab.org/infernalDatabase downloadPlaton requires a mandatory database which is publicly hosted at Zenodo:Further information is provided in thedatabasesection below.$wgethttps://zenodo.org/record/4066768/files/db.tar.gz
$tar-xzfdb.tar.gz
$rmdb.tar.gzThe db path can either be provided via parameter (--db) or environment variable (PLATON_DB):$platon--db<db-path>genome.fasta

$exportPLATON_DB=<db-path>
$platongenome.fastaAdditionally, for a system-wide setup, the database can be copied to the Platon base directory:$cp-rdb/<platon-installation-dir>UsageUsage:usage:platon[--dbDB][--prefixPREFIX][--outputOUTPUT][--mode{sensitivity,accuracy,specificity}][--characterize][--meta][--help][--verbose][--threadsTHREADS][--version]<genome>

Identificationandcharacterizationofbacterialplasmidcontigsfromshort-readdraftassemblies.

Input/Output:<genome>draftgenomeinfastaformat--dbDB,-dDBdatabasepath(default=<platon_path>/db)--prefixPREFIX,-pPREFIXPrefixforoutputfiles--outputOUTPUT,-oOUTPUTOutputdirectory(default=currentworkingdirectory)Workflow:--mode{sensitivity,accuracy,specificity},-m{sensitivity,accuracy,specificity}appliedfiltermode:sensitivity:RDSonly(>=95%sensitivity);specificity:RDSonly(>=99.9%specificity);accuracy:RDS&characterizationheuristics(highestaccuracy)(default=accuracy)--characterize,-cdeactivatefilters;characterizeallcontigs--metausemetagenomegenepredictionmode

General:--help,-hShowthishelpmessageandexit--verbose,-vPrintverboseinformation--threadsTHREADS,-tTHREADSNumberofthreadstouse(default=numberofavailableCPUs)--versionshowprogram'sversionnumberandexitExamplesSimple:$platongenome.fastaExpert: writing results toresultsdirectory with verbose output using 8 threads:$platon--db~/db--outputresults/--verbose--threads8genome.fastaModePlaton provides 3 different modi controlling which filters will be used.Accuracymode is the preset default.SensitivityIn thesensitivitymode Platon will classifiy all contigs with anRDSvaluebelowthe sensitivity threshold as chromosomal and all remaining contigs as plasmid. This threshold was defined to account for 95% sensitivity and computed via Monte Carlo simulations of artifical contigs resulting in an RDS=-7.9.
-> use this mode toexclude chromosomalcontigs.SpecificityIn thespecificitymode Platon will classifiy all contigs with anRDSvalueabovethe specificity threshold as plasmid and all remaining contigs as chromosomal. This threshold was defined to account for 99.9% specificity and computed via Monte Carlo simulations of artifical contigs resulting in an RDS=0.7.Accuracy (default)In theaccuracymode Platon will classifiy all contigs with:anRDSvaluebelowthe sensitivity threshold as chromosomalanRDSvalueabovethe specificity threshold as plasmid and in addition all contigs as plasmid for which one of the following is true: itcan be circularizedhas an incompatibility group sequencehas a replication or mobilization HMM hithas an oriT hithas an RDS above the conservative score (0.1), a RefSeq plasmid hit andnorRNA hitDatabasePlaton depends on a custom database based on MPS, RDS, RefSeq Plasmid database, PlasmidFinder db as well as manually curated MOB HMM models from MOBscan, custom conjugation and replication HMM models and oriT sequences from MOB-suite. This database based on UniProt UniRef90 release 202 can be downloaded here: (zipped 1.6 Gb, unzipped 2.8 Gb)https://zenodo.org/record/4066768/files/db.tar.gzPlease make sure that you use the latest Platon version along with the most recent database version! Older software versions arenotcompatible with the latest database versionDependenciesPlaton was developed and tested in Python 3.5 and depends on BioPython (>=1.71).Additionally, it depends on the following 3rd party executables:Prodigal (2.6.3)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2848648https://github.com/hyattpd/ProdigalDiamond (2.0.6)https://pubmed.ncbi.nlm.nih.gov/25402007http://www.diamondsearch.orgBlast+ (2.10.1)https://www.ncbi.nlm.nih.gov/pubmed/2231712https://blast.ncbi.nlm.nih.govMUMmer (4.0.0-beta2)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC395750/https://github.com/gmarcais/mummerHMMER (3.3.1)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3695513/http://hmmer.org/INFERNAL (1.1.4)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810854http://eddylab.org/infernalCitationSchwengers O., Barth P., Falgenhauer L., Hain T., Chakraborty T., & Goesmann A. (2020). Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein sequence-based replicon distribution scores. Microbial Genomics, 95, 295.https://doi.org/10.1099/mgen.0.000398As Platon takes advantage of the inc groups, MOB HMMs and oriT sequences of the following databases, please also cite:Carattoli A., Zankari E., Garcia-Fernandez A., Voldby Larsen M., Lund O., Villa L., Aarestrup F.M., Hasman H. (2014) PlasmidFinder and pMLST: in silico detection and typing of plasmids. Antimicrobial Agents and Chemotherapy,https://doi.org/10.1128/AAC.02412-14Garcillán-Barcia M. P., Redondo-Salvo S., Vielva L., de la Cruz F. (2020) MOBscan: Automated Annotation of MOB Relaxases. Methods in Molecular Biology,https://doi.org/10.1007/978-1-4939-9877-7_21Robertson J., Nash J. H. E. (2018) MOB-suite: Software Tools for Clustering, Reconstruction and Typing of Plasmids From Draft Assemblies. Microbial Genomics,https://doi.org/10.1099/mgen.0.000206IssuesIf you run into any issues with Platon, we'd be happy to hear about it! Please, start the pipeline with-v(verbose) and do not hesitate to file an issue including as much of the following as possible:a detailed description of the issuethe platon cmd line outputthe<prefix>.jsonfile if possibleA reproducible example of the issue with a small dataset that you can share (helps us identify whether the issue is specific to a particular computer, operating system, and/or dataset).