Wiki¶

Data location:¶

The data was transferred from Athena medmicro):

/MedMicro/Clinton/CRE Pfizer Feb 2022/CRE study_1A_results_17022022 
/MedMicro/Clinton/CRE Pfizer Feb 2022/CRE study_1B_results_21022022

to Ilifu:

/scratch3/users/katiel/Clinton/CRE_study_August_2022/

Reference data:¶

Klebsiella pneumoniae – strain HS11286 (GenBank accession no. CP003200.1) (n=18);
Serratia marcescens – strain KS10 (GenBank accession no. CP027798.1) (n=3);
Escherichia coli – strain ATCC 25922 (GenBank accession no. CP009072.1) (n=1); and
Enterobacter cloacae – strain ATCC 13047 (GenBank accession no. NC_014121.1) (n=1).

/scratch3/users/katiel/Clinton/CRE_study_August_2022/ref_genomes

Objectives workflow:¶

QC:¶

11 sample failed QC phred scores before trimming and filtering; none failed after filtering and trimming. Filtering and trimming were executed as follows:

nextflow run kviljoen/fastq_QC --reads '/scratch3/users/katiel/Clinton/CRE_study_August_2022/raw/study_1A_B_combined/*_R{1,2}_001.fastq.gz' -profile ilifu

QC reports can be found in the 'files' tab

AMR profiling¶

The preference from Clinton is to do AMR profiling with the ResFinder DB. I'm getting errors there that I think relate to the header formatting though so in the interim have run with the ARG_annot DB that we used for previous projects as:

ARGannot¶

nextflow run kviljoen/uct-srst2 --reads '/scratch3/users/katiel/Clinton/CRE_study_August_2022/2022-09-19-fastq_QC/bbduk/*_{1,2}.fq' -profile ilifu --gene_db /cbio/users/katie/Nicol/Ps_aerug_srst2_MLST/ARGannot_r3.fasta --outdir /scratch3/users/katiel/Clinton/CRE_study_August_2022/srst2_ARGannot/coverage_80_run --min_gene_cov 80

Individual results files compiled as:

srst2 --prev_output *results.txt --output ARGannot_AMRs

CARD DB:¶

This database is the recommended by srst2 and has been formatted by them already. The DB was downloaded with:

wget https://github.com/katholt/srst2/blob/master/data/CARD_v3.0.8_SRST2.fasta?raw=true -O CARD_v3.0.8_SRST2.fasta

Pipeline execution as:

nextflow run kviljoen/uct-srst2 --reads '/scratch3/users/katiel/Clinton/CRE_study_August_2022/2022-09-19-fastq_QC/bbduk/*_{1,2}.fq' -profile ilifu --gene_db /scratch3/users/katiel/Clinton/CRE_study_August_2022/ref_files/CARD_v3.0.8_SRST2.fasta --outdir /scratch3/users/katiel/Clinton/CRE_study_August_2022/srst2_CARD/coverage_80_run --min_gene_cov 80

Individual results files compiled as:

srst2 --prev_output *results.txt --output CARD_AMRs

Virulence factors¶

Building the relevant VFDB for Klebsiella requires a python script that needs the biopython module (use the /cbio/users/katie/singularity_containers/srst2_v2.simg singularity container for this)
NB: in order to use the correct python version (2.7.5) for srst2 I first had to comment out the lines at the end of my .bashrc file relating to conda initialize

Build genus-specific DB:

python /cbio/users/katie/Nicol/Ps_aerug_srst2_MLST/VFDB/srst2/database_clustering/VFDBgenus.py --infile /scratch3/users/katiel/Clinton/CRE_study_August_2022/ref_files/VFDB_setB_nt.fas --genus Klebsiella 

was used to create the VF DB Klebsiella.fsa

The same procedure (as last year ;) was executed for Escherichia, Serratia and Enterobacter

cd-hit (needed to build vfdb as outlined here https://github.com/katholt/srst2#using-the-vfdb-virulence-factor-database-with-srst2) docker images was pulled from here https://quay.io/repository/biocontainers/cd-hit?tab=tags and converted to singularity image on BST server:

singularity exec /cbio/users/katie/singularity_containers/cd-hit.simg /bin/bash

then run CD-HIT to cluster the sequences for this genus, at 90% nucleotide identity:

 cd-hit -i Klebsiella.fsa -o Klebsiella_cdhit90 -c 0.9 > Klebsiella_cdhit90.stdout

Repeat for other .fsa DBs

NExt parse the cluster output and tabulate the results using the specific Virulence gene DB compatible script (use srst2_v2.simg again)

python /cbio/users/katie/Nicol/Ps_aerug_srst2_MLST/VFDB/srst2/database_clustering/VFDB_cdhit_to_csv_KLedit.py --cluster_file Klebsiella_cdhit90.clstr --infile Klebsiella.fsa --outfile Klebsiella_cdhit90.csv

Next convert the resulting csv table to a SRST2-compatible sequence database using:

python /cbio/users/katie/Nicol/Ps_aerug_srst2_MLST/VFDB/srst2/database_clustering/csv_to_gene_db.py -t Klebsiella_cdhit90.csv -o Klebsiella_VF_clustered.fasta -s 5

The actual VF typing can now be done using this clustered DB:

nextflow run kviljoen/uct-srst2 --reads '/scratch3/users/katiel/Clinton/CRE_study_August_2022/2022-09-19-fastq_QC/bbduk/*_{1,2}.fq' -profile ilifu --gene_db /scratch3/users/katiel/Clinton/CRE_study_August_2022/ref_files/Klebsiella_VF_clustered.fasta --outdir /scratch3/users/katiel/Clinton/CRE_study_August_2022/srst2_VFs/coverage_80_run --min_gene_cov 80

Again combine individual sample results files with e.g.

srst2 --prev_output *genes* --output Klebsiella_VFs

MLST¶

MLST profiles were downloaded for E. coli and K. pneumoniae as:

python /cbio/users/katie/Nicol/Ps_aerug_srst2_MLST/VFDB/srst2/scripts/getmlst.py --species 'Escherichia coli#1'
python /cbio/users/katie/Nicol/Ps_aerug_srst2_MLST/VFDB/srst2/scripts/getmlst.py --species 'Escherichia coli#2'
python /cbio/users/katie/Nicol/Ps_aerug_srst2_MLST/VFDB/srst2/scripts/getmlst.py --species 'Klebsiella pneumoniae'

Note: MLST profiles not available for Serratia marecescens or Enterobacter cloacae

MLST profiling execution:

nextflow run kviljoen/uct-srst2 --reads '/scratch3/users/katiel/Clinton/CRE_study_August_2022/2022-09-19-fastq_QC/bbduk/*_{1,2}.fq' -profile ilifu --mlst_definitions /scratch3/users/katiel/Clinton/CRE_study_August_2022/ref_files/Klebsiella_definitions --mlst_db /scratch3/users/katiel/Clinton/CRE_study_August_2022/ref_files/Klebsiella_pneumoniae.fasta --mlst_delimiter _ --outdir /scratch3/users/katiel/Clinton/CRE_study_August_2022/srst2_MLSTs/Klebsiella_MLSTs