Note: This workflow documents the steps used to reproduce the data processing, clone assignment, and mutational burden analysis from the original study. Files and phylogenetic resources were taken from the authors’ published Zenodo dataset when our independently reproduced clustering results did not sufficiently match the original analysis.

Part 1: Download and Prepare All the Data

Dependencies

• Git clone pipeline: bact-gen-scripts
• Conda environment: bact-gen-scripts.yml

Step 1.1: SRA Download using Accession Number from Their Supplemental Material Table 1

sbatch /storage/home/hcoda1/2/bli629/scratch/redo_paper/download_parallel.sbatch

Step 1.2: Zip All the Short Reads

sbatch /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/gz_01.sbatch

Step 1.3: Short Reads Quality Control using fastp

sbatch /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/fastp_01.sbatch

Step 1.4: Use Pipeline bact-gen-scripts to Get the Alignments Mapping Against Reference Genome for Each Sample

sbatch /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/map_to_bam_original.sbatch

Step 1.5: Identify the Alignments with Low Quality

python /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/filter_percentage.py

Part 2: Clone Assignment

Dependencies

• Conda environment: clone_assignment.yml

Step 2.1: Determine the Samples with Unique Strain-Type-Patient Pair as Representative for Following SNP Distance Clustering

Result accession numbers are in accession_unique_pair.txt:

python /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/unique_ST_Patient_pair.py

Step 2.2: Combine the Alignments for the Samples with Unique Strain-Type-Patient Pair and Run Through snp-sites to Extract SNPs

1. Combine the alignments:

   xargs -I {} -a /path/to/accession_unique_pair.txt cat /path/to/bact-gen/step1.4/output/folder/{}/{}_qc.mfa > /path/to/output/merged.aln

2. Run snp-sites:

   snp-sites /path/to/output/merged.aln -o /path/to/output/merged_snpsites.aln

3. Rename the .aln to .fasta:

   cp /path/to/output/merged_snpsites.aln /path/to/output/merged_snpsites.fasta

Step 2.3: Run pairsnp to Obtain Pairwise SNP Distance Matrices

pairsnp -t 12 /path/to/output/merged_snpsites.aln > /path/to/pairsnp/output.csv

Step 2.4: UPGMA Clustering to Cluster the Samples Based on the SNP Distance Matrix

python /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/UPGMA_clustering.py

After clustering the representative samples, the cluster results were reflected to all the samples using the R script below:

Rscript /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/cluster.R

Part 3: Mutational Burden Analysis Workflow

Dependencies

• Conda environment: phyloeffects.yml, snpeff.yml

Step 3.1: Use Alignment Files and Phylogenetic Trees to Reconstruct the Ancestral Genome for Each Clone

All the alignments, tree, and position mapping files are downloaded from Zenodo, as our clustering result is not ideal

sbatch /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/phylloeffects.sbatch

Step 3.2: Extract the Reconstructed Ancestral Genome

This step is incorporated with Step 3.

Step 3.3: Compare the Reconstructed Ancestral Genome with the Reference Genome at Each Mapped Position

Variants are identified and saved to VCF files:

sbatch /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/reconstruction_and_vcf_building.sbatch

Step 3.4: Annotate the Output VCF Files Using SnpEff

• The SnpEff database was manually built using the GenBank file from the NCBI Reference Genome.
• Follow the instructions from the SnpEff documentation.
• Database name was set as: GCA_000006765.1_ASM676v1_genomic

sbatch /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/snpEff.sbatch

Step 3.5: Conduct Mutational Burden Analysis

python /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/mutational_burden_analysis.py

Step 3.6: Generate Manhattan Plot Visualization

Use the R script provided for visualization:

Rscript /storage/home/hcoda1/2/bli629/scratch/original_paper/all_codes/manhattan_plot.R