QasimHussain/p.aeruginosa-panimmunophagomics

 

Quantitative Analysis of Pseudomonas aeruginosa Pan-Immunophagomics

Abstract

The evolutionary arms race between Pseudomonas aeruginosa and its corresponding bacteriophages represents a critical dynamic influencing clinical pathogenesis and environmental adaptation. This repository encapsulates an automated, end-to-end bioinformatics pipeline designed to deeply map the immunological landscape of P. aeruginosa. By integrating high-throughput CRISPR array extractions, comprehensive defense system annotations via DefenseFinder, and prophage identification frameworks, this project establishes a unified pan-immunophagomics multidimensional data matrix. This repository is fully provisioned for automated replication, rendering high-confidence topological visualizations that map innate and adaptive antimicrobial resistance markers in strict adherence to rigorous scientific publishing standards.

Repository Architecture

This repository is structurally partitioned into distinct environments to separate public dataset publishing from raw modular analysis. To comply with GitHub storage limits while ensuring structural continuity, all bulk sequence data, model profiles, and generated results directories are structurally preserved using placeholder documentation.

p.aeruginosa-panimmunophagomics/
├── data/                   # Standardized CSVs containing summarized immunophagomics data
├── notebooks/                  # Jupyter notebooks utilized for statistical correlation modeling
├── local-analysis/                   # Core modular bioinformatics processing pipeline
│   ├── data/                         # Intermediate sequence data, HMM algorithms, and reference genomes
│   ├── results/                      # Output matrices, HTML dashboards, and static figure generation
│   ├── 05_generate_interactive_figures.py # Python orchestrator for publication-grade modeling
│   ├── run_minced.sh                 # Shell script invoking CRISPR topology profiling
│   └── integrate_pan_immuno.py       # Combinatorial logic framework mapping diverse metrics
├── analysis.sh                       # Core execution engine mapping the overarching pipeline
├── installation.sh                   # Scaffolds required dependencies and Conda environments
└── environment.yml                   # Strict dependency tracking for reproducible builds

Methodological Framework

The analytical architecture leverages parallel processing frameworks and established heuristic schemas to conduct systematic evaluations across high-throughput assemblies.

1. 1.High-Throughput Array Extraction: Automated scanning of nucleotide schemas isolates distinct CRISPR spacer sequences and tandem repeats utilizing strict array algorithms.
2. 2.Prokaryotic Defense Repertoires: Application of mdmparis-defense-finder combined with hierarchical Hidden Markov Models (HMM) enables the deterministic annotation of supplementary antiviral defense architectures (e.g., Restriction-Modification, CBASS, Gabija, and Thoeris mechanisms).
3. 3.Prophage Element Identification: Computational integration segments active, questionable, and incomplete viral structures representing historical bacteriophage exposure metrics.
4. 4.Data Dimensionality and Synthesis: Individual analytical outputs are standardized and transformed into a unitary normalized matrix. This multidimensional matrix evaluates the comparative frequency of loci components per specific clinical or environmental isolate.

Execution and Replication

It is strictly recommended that the procedural sequence be preserved to enforce programmatic dependency alignment across the operating system.

I. Scaffold and Initialization

Provide necessary permissions and allocate the structural parameters required to run the pipeline.

chmod +x installation.sh analysis.sh test_analysis.sh local-analysis/run_minced.sh
./installation.sh

II. Core Data Synthesis

Execute the overarching pan-immunophagomics analytical script leveraging the local constraints:

./test_analysis.sh

System Requirements

The complete aggregation matrices require standard parallel threading capability mapping GNU configurations and base Conda module allocations utilizing Python (>=3.10.x). Static visualization exports utilize Plotly framework dependencies mapped against standard foundational C-library integrations.

License

This project is licensed under the terms of the MIT License.