1. Bacterial Variant Calling: S. aureus (PRJNA325350)

This project is a complete bioinformatics pipeline to identify and interpret genetic mutations in Staphylococcus aureus samples (PRJNA325350) that evolved resistance to Daptomycin (DAP) and Vancomycin (VAN).

The pipeline starts from raw FASTQ reads, performs QC, alignment (BWA), variant calling (GATK), and annotation (SnpEff), and concludes with a deep downstream analysis (Pandas) and visualization (Seaborn) to identify the "prime suspect" genes responsible for antibiotic resistance.

2. Key Findings: The "De Novo" Mutation Hitmap

The primary goal was to find de novo mutations (variants present in treated samples but absent from the Control). The analysis successfully identified 48 such mutations.

The visualization below summarizes the key evolutionary pathways taken by the DAP and VAN lineages:

3. Biological Interpretation

The analysis revealed distinct and clinically relevant resistance strategies:

1. Daptomycin (DAP) Resistance (Membrane Adaptation):

   • The DAP lineage developed significant missense mutations in genes critical for cell membrane homeostasis, including mprF (a well-known DAP resistance gene) and agrA (a master regulator of virulence).

2. Vancomycin (VAN) Resistance (Cell Wall Stress Response):

   • The VAN lineage developed missense mutations in sensor systems like vraG and vraT, which are key components of the Vra system that senses cell wall damage.

3. The "Hypermutator" Engine (mutL):

   • The most significant finding was the emergence of high-impact frameshift mutations in the DNA mismatch-repair gene mutL in the DAP lineage. This "knock-out" likely created a "hypermutator" state, accelerating the rate of mutation and allowing genes like mprF to acquire resistance mutations rapidly.

4. Methodology: The Pipeline (Jupyter Notebooks)

The project follows a clean, step-by-step notebook workflow. Each notebook is self-contained and completes one major stage of the pipeline.

• 00_Setup_and_Download.ipynb: Installs all tools (GATK, BWA, SnpEff, etc.) and downloads the 7 SRA samples.
• 01_QC_and_Trimming.ipynb: Runs FastQC, identifies adapters, and cleans reads using fastp.
• 02_Mapping_and_BAM_Processing.ipynb: Indexes the reference and maps reads using bwa mem (with Read Groups) and samtools.
• 03_Variant_Calling_GATK.ipynb: Creates sequence dictionaries and calls variants using gatk HaplotypeCaller to produce 7 VCF files.
• 04_Variant_Annotation.ipynb: Builds a custom SnpEff database and annotates the VCF files, producing 7 .ann.vcf.gz files and 7 .genes.txt reports.
• 05_Downstream_Analysis_and_Interpretation.ipynb: Programmatically aggregates the 7 reports using pandas, performs a set-difference analysis to find de novo mutations, and saves the final 48 "prime suspects" to a CSV.
• 06_Final_Visualization.ipynb: Loads the final CSV and generates the publication-quality heatmap using matplotlib/seaborn.

5. How to Reproduce

1. Clone this repository:

   git clone https://github.com/refmyoussef-source/project_variant_calling.git
   cd project_variant_calling

2. Create the Conda Environment:

   • This project's dependencies are managed in the environment.yml file.

   conda env create -f environment.yml
   conda activate variant_call_env

3. Run the Pipeline:

   • Open the notebooks/ directory and run the notebooks in numerical order (from 00 to 06).