PlasmIdent

This pipeline idenfitifes circular plasmids in in bacterial genome assemblies by aligning long sequencing reads to putative plasmids. When overlapping long reads confirm circular plasmids, resistance genes are identified and additional parameters calculated.

The pipeline includes the following steps:

• Gene prediction with Glimmer3
• Identification of antibiotic resistance genes in the CARD Database RGI
• Long read alignment against assembly
• Coverage analysis with Mosdepth
• GC Content and GC Skew
• Identification of reads that overlap the gap in the plasmid, indicating circular reads

Requirements

• Linux or Mac OS
• Java 8.x
• Docker or Singularity container application or Conda package manager

Installation

1. Install nextflow

curl -s https://get.nextflow.io | bash 

This creates the nextflow executable in the current directory

2. Download pipeline

You can either get the latest version by cloning this repository

git clone https://github.com/imgag/plasmIDent

or download on of the releases.

3. Download dependencies

All the dependencies for this pipeline can be downloaded in a docker container.

docker pull caspargross/plasmident

Alternative dependency installations:

• Singularity Container
• Use conda environment (no docker)

Run Application

The pipeline requires an input file with a sample id (string) and paths for the assembly file in .fasta format and long reads in .fastq or .fastq.gz. The paths can either be absolute or relative to the launch directory. In normal configuration (with docker), it is not possible to follow symbolic links.

The file must be tab-separated with three columns:

sample_id	assembly_fasta	longread_fq

The file must not have a header line and start directly with the data. Here is an example file:

myid1	/path/to/assembly1.fasta	/path/to/reads1.fastq.gz
myid2	/path/to/assembly2.fasta	/path/to/reads2.fastq.gz

The pipeline is started with the following command:

nextflow run plasmident --input read_locations.tsv

There are other run profiles for specific environments.

Optional run parameters

• --outDir Path of output folder
• --seqPadding Number of bases added at contig edges to improve long read alignment [Default: 1000]
• --covWindow Moving window size for coverage and gc content calculation [Default: 50]
• --max_cpu Number of threads used per process [Default: 4]

• --max_memory Maximum amount of memory available

• --targetCov Large read files are subsampled to this target coverage to speed up the process [Default: 50]

Results

The pipeline creates the following output folders:

• alignment: Contains the long read alignment (full genome)
• coverage: Long read coverage for the whole input genome (compressed bedfile)
• gc: Windowed GC content (full genome)
• genes: Predicted gene locations
• plasmids: Nucleotide sequences for all confirmed plasmids in separate FASTA files
• resistances: GFF file with locations of identified resistance genes

Additional file:

• sampleID_summary.csv: Tabular text file with contig lengths, plasmid status and identified antibiotic resistance genes.