Osefunmi/Hackbio_2025_NGS_Stage1_project

WGS of samples of Listeria Monocytogenes from South Africa in 2017 # South African Listeria Outbreak WGS Project ## Introduction South Africa faced a public health crisis that would become the world's largest recorded outbreak for a particular infection. Unravelling the cause of the outbreak could only be done by WGS of DNA from infected patients. The goal of this script is to: Confirm the identity of the organism, Determine the antimicrobial resistance (AMR) profile of these pathogens. Detect if there might be a toxin that is accelerating the death rate Suggest antibiotics/treatment options that could be used managing the cases ## Step 1: Creating directories and downloading data. `mkdir Project && cd Project` `mkdir raw_data && cd raw_data` `wget raw.githubusercontent.com` `bash SA_Polony_100_download.sh` ## Step 2: Quality control and combinig qc report I ran FastQC on all raw reads to assess quality. `fastqc raw_data/*.fastq.gz -o qc_reports/` I combined all qc report using multiqc `multiqc qc_reports` I opened the multiqc html file showing a combined report. I looked out for good quality of our data and delete those with poor quality from our raw data set. we also delete all data without both the forward and backward sequences. ## Step 3: Trimming We trimmed adapters and low-quality reads using fastp. first we created a sh document to run through bash `nano trim.sh` ``` #!/bin/bash mkdir trimmed_data trimmed_report for R1 in *_1.fastq.gz do # Detect matching R2 file R2=${R1/_1.fastq.gz/_2.fastq.gz} sample=$(basename "$R1" _1.fastq.gz) fastp \ -i "$R1" \ -I "$R2" \ -o trimmed_data/${sample}_1.trim.fastq.gz \ -O trimmed_data/${sample}_2.trim.fastq.gz \ -h trimmed_reports/${sample}_fastp.html \ -j trimmed_reports/${sample}_fastp.json \ --thread 8 done ``` save and close the bash script `bash trim.sh` ## Step 4: Genome Assembly (SPAdes) I assembled my genome using spades. First I created a bash script then I ran the scri …