A tool for core-genome MLST typing for bacterial data. This has been initially developed for Clostridium difficile, but could be adpated to other bacteria.
- Singularity - instructions can be found here https://github.com/sylabs/singularity/blob/master/INSTALL.md
- Java version 8 or later (required for nextflow)
- Nextflow - https://www.nextflow.io
Build image
cd singularity
sudo singularity build hash-cgmlst.img Singularity
If you do not have sudo access to your compute machine, the image can be built on another installation and copied across.
The image can be mounted and tested if desired:
singularity shell hash-cgmlst.img
Fetch the miniKraken2 database, from the root of the repository
mkdir minikraken2
cd minikraken2
wget ftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/minikraken2_v1_8GB_201904_UPDATE.tgz
tar -xzf minikraken2_v1_8GB_201904_UPDATE.tgz
rm minikraken2_v1_8GB_201904_UPDATE.tgz
cd minikraken2_v1_8GB
mv * ../
cd ..
rmdir minikraken2_v1_8GB
A version of this is included - it can be updated if desired, but this is not required for hash-cgMLST.
To update this, visit cgmlst.org and download the allele files for each gene to ridom_scheme/files and then run:
cd bin
python makeReferenceDB.py
At present the scripts are designed to be run on two specific server set-ups, each with its own profile. Updates can be made to the nextflow.config file for other set-ups.
The two profiles are:
- cluster - an example implementation for a SGE based cluster
- ophelia - an example for a local bare-metal server (would adapt to a stand-alone machine if the amount of memory available per core is changed)
To run the cgMLST analysis
nextflow hash-cgMLST.nf --seqlist example_data/example_input.csv --outputPath comparison_study_data/example_output -resume -profile ophelia
This will download 2 example pairs of fastq files from EBI and process these through the pipeline. Please see example_data/example_input.csv for an example of an input file. The file type column should be set to ebi to download from EBI and the file_name column should be a sample or run identifier.
Using the example_six_hospitals.csv file as an input would allow you to run hash-cgMLST on the 973 samples used in the study describing hash-cgMLST.
Outputs provided include:
- QC data:
*_base_qual.txt,*_kraken.txt,*_length.txt,*.raw_fastqc.html,*.clean_fastqc.html - Spades contigs and assembly stats:
*_spades_contigs.fa,*_cgmlst.stats - cgMLST genes as a multifasta file:
*_cgmlst.fa - standard cgMLST calls as a json file:
*_cgmlst.profile(no tracking of novel alleles is done, just recorded missing for now) - hash-cgMLST calls as a json file:
*_cgmlst.json - standard MLST calls:
*_mlst.txt
To run a comparison for hash-cgMLST profiles after the nextflow pipeline above is complete use the bin/compareProfiles.py script:
bin/compareProfiles.py -i comparison_study_data/example_output -o comparison_study_data/example_compare.txt
The bin/compareProfilesExclude.py script ignores the 26 genes likely prone to mis-assembly.
Each downloaded set of gzipped fastq files will be stored in the working directory, as will a pair of gzipped cleaned fastq files. You will need to periodically delete your working directory to prevent it from growing too large for very large projects.