🌾 SSR Genetic Diversity & Population Structure Analysis Pipeline

A reproducible computational framework for analyzing SSR (Simple Sequence Repeat) marker datasets in plant breeding and population genetics.

This repository provides a fully automated pipeline for:

• Marker quality control
• Genetic diversity estimation
• Population structure analysis
• Linkage disequilibrium network inference
• Genetic differentiation and gene flow analysis

The workflow is designed for crop genetics, molecular breeding, and population genomics studies.

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🔬 Scientific Applications

This pipeline is suitable for:

• Genetic diversity analysis
• Germplasm characterization
• Population structure studies
• Molecular breeding programs
• Marker-assisted selection
• Plant population genetics

Example organisms:

• Rice
• Wheat
• Maize
• Barley
• Other crop species with SSR datasets

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⚙️ Key Analytical Modules

Module	Description
Data Processing	Load and validate SSR marker datasets
Genetic Diversity	Estimate MAF, PIC, He, Shannon and Simpson indices
Population Structure	Compute genetic distances and multivariate ordination
Network Analysis	Construct linkage disequilibrium networks
Genetic Differentiation	Estimate Fst and gene flow (Nm)

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📂 Repository Structure

SSR-Genetic-Diversity-Pipeline
│
├── data
│   └── example_ssr_dataset.xlsx
│
├── pipeline
│   ├── data_processing.py
│   ├── genetic_diversity.py
│   ├── population_structure.py
│   ├── network_analysis.py
│   └── genetic_differentiation.py
│
├── notebooks
│   └── SSR_analysis_colab.ipynb
│
├── results
│
├── README.md
├── requirements.txt
└── LICENSE

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🧬 Computational Workflow

SSR Marker Dataset
        │
        ▼
Marker Quality Control
        │
        ▼
Genetic Diversity Analysis
        │
        ▼
Genetic Distance Estimation
        │
        ▼
Population Structure Analysis
   ├── PCA
   ├── MDS
   └── Hierarchical Clustering
        │
        ▼
Linkage Disequilibrium Network
        │
        ▼
Genetic Differentiation
   ├── Fst
   └── Gene Flow (Nm)

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📊 Expected Outputs

The pipeline automatically generates:

results/

diversity_indices.csv
fst_nm_results.csv

dendrogram.png
PCA.png
MDS.png
LD_network.png

These outputs enable comprehensive interpretation of genetic diversity and population structure.

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📦 Installation

Clone the repository:

git clone https://github.com/yourusername/SSR-Genetic-Diversity-Pipeline.git
cd SSR-Genetic-Diversity-Pipeline

Install dependencies:

pip install -r requirements.txt

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▶️ Running the Pipeline

Example execution in Python:

from pipeline.data_processing import *
from pipeline.genetic_diversity import *
from pipeline.population_structure import *
from pipeline.network_analysis import *
from pipeline.genetic_differentiation import *

df = load_ssr_data("data/example_ssr_dataset.xlsx")

df_clean, dropped, mono = validate_markers(df)

diversity = analyze_ssr_diversity(df_clean)

dist = compute_jaccard(df_clean)

plot_dendrogram(dist, df_clean.index, "results")

pca_analysis(df_clean, "results")

mds_analysis(dist, "results")

ld_df = ld_analysis(df_clean)

plot_ld_network(ld_df, "results")

fst_nm = calculate_fst_nm(df_clean)

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📚 Citation

If you use this pipeline in your research, please cite:

SSR Genetic Diversity & Population Structure Pipeline
GitHub Repository

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👨‍🔬 Author

Md Rezve Research Assistant — Plant Protection Lab Khulna University, Bangladesh

Research interests:

• Plant molecular genetics
• Population genomics
• Omics-driven breeding
• Computational biology

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📜 License

This project is released under the MIT License.