CholerAegon is a nextflow pipeline for assembly and antimicrobial resistance gene detection with long and/or short read data. It will assemble your data with a suitable method, run RGI and Abricate to detect AMR genes, and then try to predict the resistances conferred by the found genes.

To run CholerAegon, you need Nextflow and Singularity (now called Apptainer). If you use Conda, you can create an environment for CholerAegon like so:

conda create -n choleraegon nextflow singularity
conda activate choleraegon

Required parameters:

• pick a release revision of this repository with -r, e.g. -r 0.3.0
• pick your executor (local, slurm, etc) and engine (singularity) with -profile, most likely -profile local,singularity
• supply read data with --samples, --longreads, --shortreads or --fasta

Optional parameters:

• supply a genome reference with --genome_reference to get average nucleotide identity values for the assemblies
• specify the output folder with --output (default results_CholerAegon)
• add --update_card (or use stand-alone) to download the newest CARD resistance database data

• run on Nanopore reads (one .fq file per sample)

nextflow run RaverJay/CholerAegon -r 0.3.0 -profile local,singularity \
--longreads 'sample_lr_*.fq' --genome_reference my_pathogen.fa --output results_lr

• run on short paired-end reads (supply the filename pattern in quotes, with a '*' and '{1,2}')

nextflow run RaverJay/CholerAegon -r 0.3.0 -profile local,singularity \
--shortreads 'sample_sr_ID*_{1,2}.fq' --genome_reference my_pathogen.fa --output results_sr

• run hybrid assembly with both long and short reads

nextflow run RaverJay/CholerAegon -r 0.3.0 -profile local,singularity \
--samples list_of_samples.csv --genome_reference my_pathogen.fa --output results_samples

where list_of_samples.csv has the following structure:

sample1_name,s1_nanopore_reads.fq,s1_short_reads_pair1.fq.gz,s1_short_reads_pair2
sample2_name,s2_nanopore_reads.fq,s2_short_reads_pair1.fq.gz,s2_short_reads_pair2
...

--fasta <assembly/assemblies>       supply pre-assembled genomes instead of read data,
                                      do only AMR detection
--genome_reference <reference>      optionally supply a genome reference for the analyzed pathogen,
                                      will produce %ANI values for all assemblies
--min_coverage_percent              specify minimum percentage of covered bases for an AMR gene
                                      to be considered present (default 80)
--min_identity_percent              specify minimum percentage of identitical bases for an AMR gene
                                      to be considered present (default 80)
--do_all_assemblies                 do not skip short-read-only and long-read-only
                                      assemblies in hybrid mode (default off)

CholerAegon makes use of the bioconda docker containers hosted on quai.io. Docker support coming soon. Support for cluster/cloud execution coming soon.

It loosely emerged from Cholera Antibiotic rEsistance Gene detectiON. But Aegon is also the true name of A Song of Ice and Fire's Jon Snow, meant as a reference to Cholera scientist John Snow.

If you found CholerAegon helpful for your work or research kindly cite:

Fuesslin, V., Krautwurst, S., Srivastava, A., Winter, D., Liedigk, B., Thye, T., Herrera-León, S., Wohl, S., May, J., Fobil, J.N. and Eibach, D., 2022. Prediction of Antibiotic Susceptibility Profiles of Vibrio cholerae Isolates From Whole Genome Illumina and Nanopore Sequencing Data: CholerAegon. Frontiers in Microbiology, 13.