🌱 Sesame Population Genomics Analysis

🧬 Exploring the Genetic Landscape of Sesame

Whole Genome Sequencing Reveals Distinct Egyptian Lineages and SNP Variability

📊 Project Overview

This repository contains the comprehensive data analysis pipeline for our research on sesame genetic diversity. Our study combines whole-genome sequencing, population genetics, and gene expression analysis to reveal the unique genetic characteristics of Egyptian sesame cultivars.

👥 Research Team

🔬 Lead Authors

Clara R. Azzam¹ - Cell Research
Alsamman M. Alsamman*⁵'⁶ - Genome Mapping

🌾 Field Research

Ramadan Ahmed Arafa² - Plant Pathology
Mohamed Z. S. Ahmed³ - Genetic Resources

🧪 Molecular Biology

Shafik D. Ibrahim⁴ - Genetic Engineering
Mokhtar Said Rizk³ - Desert Research

🌏 International Collaboration

Anas M. Saedwi⁵'⁶ - ICARDA
Kenta Shirasawa⁷ - Japan

🏛️ Institutional Affiliations

Symbol	Institution	Location
¹	Cell Research Department, Field Crops Research Institute, ARC	🇪🇬 Giza, Egypt
²	Plant Pathology Research Institute, ARC	🇪🇬 Giza, Egypt
³	Genetic Resources Department, Desert Research Center	🇪🇬 Cairo, Egypt
⁴	Agricultural Genetic Engineering Institute, ARC	🇪🇬 Giza, Egypt
⁵	Molecular Genetics and Genome Mapping Laboratory, AGERI	🇪🇬 Giza, Egypt
⁶	International Center for Agricultural Research in Dry Areas (ICARDA)	🇪🇬 Giza, Egypt
⁷	Kazusa DNA Research Institute	🇯🇵 Kisarazu, Japan

📧 Correspondence: smahmoud@ageri.sci.eg

📋 Abstract

🌾 Sesame, one of the earliest cultivated oil crops in human history, holds a distinguished place due to its high oil content, rich flavor, and distinctive nutty aroma.

This comprehensive study performed whole-genome resequencing on 5 Egyptian sesame genotypes (Shandaweel 3, Toshky1, and three gamma-irradiated mutant lines) and incorporated 19 publicly available sesame genome datasets.

🔍 Key Discoveries

🧬 Genomic Insights	📊 Results
Total Variants Identified	6,106,085 nucleotide variants
High-Quality SNPs	26,449 SNPs (after filtering)
Population Clusters	3 distinct genetic groups
Egyptian Uniqueness	Genetically pure and separate cluster
Functional Impact	51% missense mutations
Chromosome Diversity	Si12 highest (1,465 SNPs), Si2 lowest (3 SNPs)

🎯 Biological Significance

🇪🇬 Egyptian Cultivars: Form a genetically distinct and pure cluster
🌏 International Varieties: Chinese, USA, and South Korean varieties show historical seed exchange patterns
🧬 Genetic Diversity: Chromosomes Si7, Si12, and Si13 exhibit highest heterozygosity
⚡ Functional Variants: Critical genes affected include AXR1, CYP73A5, and CERK1 (growth, stress resilience, pathogen defense)

📁 Repository Structure

🗂️ Click to expand the complete project structure

📦 SesamePopulationGenomics/
├── 📄 README.md                    # Project documentation
├── 📁 data/                        # 🔒 Raw and processed datasets
│   ├── 🧬 EgyptianAndInternationalSesameSNPs.vcf.gz   # Combined SNP variants
│   ├── 🇪🇬 EgyptianSesameSNPs.vcf.gz                   # Egyptian-specific SNPs
│   ├── 📊 PRJNA1036214Count.txt.gz                    # Expression dataset 1
│   ├── 📊 PRJNA1197728Count.txt.gz                    # Expression dataset 2
│   ├── 📋 SesameGeneExpressionSampleInfo.txt.gz       # Sample metadata
│   └── 🏷️ SesameVarSNPeff.txt.gz                      # SNP effect annotations
├── 📁 scripts/                     # 🔧 Analysis pipeline
│   ├── 🎯 analysis.R               # 🚀 Main orchestration script
│   ├── 📥 readData.R               # Data loading & preprocessing
│   ├── 📈 PCA_geneExpression.R     # Population structure analysis
│   ├── 🎨 create_expression_heatmap_with_variants.R   # Integrated heatmaps
│   ├── 💥 create_snp_impact_plot.R                    # SNP impact visualization
│   ├── ⭐ create_top_genes_plot.R                     # Top genes analysis
│   ├── 🥧 create_variant_composition_plot.R           # Variant distribution
│   ├── 📊 expressionBoxplot.R                         # Expression patterns
│   ├── 📊 statssumexpession.R                         # Statistical summaries
│   ├── 🔗 create_gene_expression_snp_table.R          # Integrated tables
│   ├── 🗺️ plot_snp_density.R                          # Chromosomal SNP density
│   ├── 📊 plot_total_important_variants.R             # Variant importance
│   ├── 📖 read_gtf.R                                  # Genome annotation parser
│   ├── 🧹 filter.R                                    # Data filtering utilities
│   ├── 🔍 get_variant_columns.R                       # Variant data extraction
│   └── 🏷️ create_variant_abbreviations.R              # Annotation helpers
└── 📁 results/                     # 📈 Generated outputs & visualizations

📊 Data Types Overview

📁 Category	📄 Files	📝 Description	💾 Size
🧬 Genomic Variants	`*.vcf.gz`	SNP calls from WGS data	Large
📊 Gene Expression	`*Count.txt.gz`	RNA-seq quantification	Medium
📋 Metadata	`*Info.txt.gz`	Sample annotations	Small
🏷️ Annotations	`*SNPeff.txt.gz`	Functional variant effects	Medium

🔬 Analysis Pipeline

graph TD
    A[📥 Data Loading] --> B[🧹 Preprocessing]
    B --> C[📊 PCA Analysis]
    B --> D[🧬 SNP Analysis]
    B --> E[📈 Expression Analysis]
    C --> F[🎨 Integrated Visualization]
    D --> F
    E --> F
    F --> G[📊 Results & Reports]
    
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8
    style D fill:#fff3e0
    style E fill:#fce4ec
    style F fill:#f1f8e9
    style G fill:#e0f2f1

🚀 Main Analysis Workflow (`analysis.R`)

The main analysis script orchestrates a comprehensive analytical pipeline:

🔢 Step	🎯 Component	📝 Description	⏱️ Duration
1	📥 Data Loading	Load expression, SNP data, and metadata	~2 min
2	📊 PCA Analysis	Population structure through gene expression	~5 min
3	📈 Expression Analysis	Distribution plots and statistical summaries	~3 min
4	🧬 Variant Analysis	SNP impacts, effects, and compositions	~10 min
5	🎨 Integration	Combined expression-variant visualizations	~15 min

🧩 Key Analysis Components

📥 1. Data Loading and Preprocessing readData.R

🎯 Purpose: Load and clean multiple data sources for integrated analysis

🔧 Functions:

read_data_files() - 📂 Reads expression and SNP data files
process_expression_data() - 🧹 Filters and processes gene expression data
process_snp_data() - 🔍 Processes SNP effect annotations
merge_datasets() - 🔗 Integrates multiple datasets

📊 Output: Clean, merged dataset ready for downstream analysis

📈 2. Principal Component Analysis PCA_geneExpression.R

🎯 Purpose: Analyze population structure through gene expression patterns

🔧 Function: pca_geneExpression()

✨ Features:

📊 Log-transformation of expression data
🏷️ Sample clustering by tissue type (pod vs. stem)
🎨 Confidence ellipses for population groups
📉 Variance explained visualization

📊 Output: Interactive PCA plot showing sample relationships

📊 3. Expression Distribution Analysis expressionBoxplot.R

🎯 Purpose: Visualize gene expression patterns across samples

🔧 Function: create_expression_plot_samples()

✨ Features:

🎻 Violin plots with overlaid boxplots
📊 Sample ordering by expression levels
🎨 Tissue-specific coloring
📈 Statistical summaries

📊 Output: Comprehensive expression distribution visualization

🧬 4. SNP Impact Analysis create_snp_impact_plot.R

🎯 Purpose: Analyze distribution of SNP impact categories

🔧 Function: create_snp_impact_plot()

✨ Features:

🏷️ Categorizes SNPs by impact severity (HIGH, MODERATE, LOW, MODIFIER)
🎻 Violin plots with statistical overlays
🌈 Color-coded impact categories
📊 Comprehensive distribution metrics

📊 Output: Publication-ready SNP impact distribution plots

⭐ 5. Top Genes Analysis create_top_genes_plot.R

🎯 Purpose: Identify and visualize highly expressed genes with variant burden

🔧 Function: create_top_genes_plot()

✨ Features:

🔝 Top 20 most expressed genes
🏷️ Gene product annotations from GTF
🧬 Variant count integration
🌈 Color-coded by variant burden

📊 Output: Annotated visualization of key genes

🥧 6. Variant Composition Analysis create_variant_composition_plot.R

🎯 Purpose: Analyze variant type distribution across genes

🔧 Function: create_variant_composition_plot()

✨ Features:

🏷️ Categorizes variants by functional effect
📊 Stacked proportion plots
🔝 Top 50 genes by variant count
🎨 Publication-ready styling

📊 Output: Variant composition visualization

🎨 7. Expression-Variant Heatmap create_expression_heatmap_with_variants.R

🎯 Purpose: Integrate gene expression with variant annotations

🔧 Function: create_expression_heatmap_with_variants()

✨ Features:

🌡️ Row-scaled gene expression heatmap
🏷️ Multi-track variant annotations
🧬 Tissue-specific sample grouping
📝 Gene product labeling

📊 Output: High-resolution integrated heatmap

📋 8. Integrated Gene-SNP Table create_gene_expression_snp_table.R

🎯 Purpose: Create comprehensive gene summary table

🔧 Function: create_gene_expression_snp_table()

✨ Features:

📊 Average expression by tissue type
🧬 Comprehensive variant impact summaries
🏷️ Gene product annotations
📈 Ranked by biological importance

📊 Output: Integrated gene-variant summary table

🛠️ Utility Functions

🔧 Utility	📄 Script	🎯 Purpose	🔗 Key Function
📖 GTF Processing	`read_gtf.R`	Parse genome annotations	`parse_gtf_for_products()`
🧹 Gene Filtering	`filter.R`	Quality-based gene filtering	`filter_gtf_product_names()`
🔍 Variant Extraction	`get_variant_columns.R`	Extract variant data columns	`get_variant_columns()`
🏷️ Abbreviations	`create_variant_abbreviations.R`	Create readable variant names	`create_variant_abbreviations()`

📂 Data Files Description

📊 Gene Expression Data

📄 File	🔬 Source	📝 Description	🧬 Content
`PRJNA1036214Count.txt.gz`	NCBI SRA	RNA-seq quantification data	Gene counts matrix
`PRJNA1197728Count.txt.gz`	NCBI SRA	RNA-seq quantification data	Gene counts matrix
`SesameGeneExpressionSampleInfo.txt.gz`	Metadata	Sample annotations	Tissue types, conditions

🧬 Genomic Variant Data

📄 File	🌍 Scope	📝 Description	🔢 Variants
`EgyptianSesameSNPs.vcf.gz`	🇪🇬 Egyptian only	SNP variants from 5 Egyptian genotypes	~1M
`EgyptianAndInternationalSesameSNPs.vcf.gz`	🌏 Global	Combined dataset (24 genotypes)	~6M
`SesameVarSNPeff.txt.gz`	🏷️ Annotations	Functional variant effects from SnpEff	~26K

🎯 Key Findings

🏆 Major Discoveries

🔬 Analysis Domain	📊 Key Finding	🎯 Biological Significance
🧬 Population Structure	3 distinct genetic clusters identified	Egyptian cultivars form a genetically pure lineage
📍 SNP Distribution	26,449 high-quality SNPs across 13 chromosomes	Si12 shows highest diversity (1,465 SNPs)
🧬 Functional Impact	51% missense mutations	High potential for phenotypic effects
📈 Gene Expression	Tissue-specific patterns (pod vs. stem)	Clear developmental specialization
⭐ Important Genes	AXR1, CYP73A5, CERK1 identified	Critical for growth, stress, and defense

🌍 Population Genetics Insights

🇪🇬 Egyptian Cluster    🇨🇳 Chinese + 🇺🇸 USA + 🇰🇷 South Korea
     (Pure lineage)              (Historical exchange)
         │                              │
         └─────────────┬────────────────┘
                       │
              🌱 Ancestral Sesame

🧬 Chromosomal Diversity Patterns

🧬 Chromosome	📊 SNP Count	🎯 Heterozygosity	🏷️ Notable Features
Si12	1,465	⭐⭐⭐ High	Highest genetic diversity
Si7	1,200+	⭐⭐⭐ High	Stress response genes
Si13	1,100+	⭐⭐⭐ High	Metabolic pathways
Si2	3	⭐ Low	Highly conserved region

💻 Requirements

📦 R Package Dependencies

🔽 Core Packages (Click to expand)

# 📊 Data Manipulation & Visualization
library(tidyverse)      # Complete data science toolkit
library(readr)          # Fast data reading
library(dplyr)          # Data manipulation
library(tidyr)          # Data tidying
library(ggplot2)        # Grammar of graphics
library(stringr)        # String manipulation
library(tibble)         # Modern data frames

# 🎨 Advanced Visualization
library(ComplexHeatmap) # Publication-quality heatmaps
library(circlize)       # Circular visualization
library(viridisLite)    # Perceptually uniform colors
library(ggrepel)        # Smart label positioning
library(viridis)        # Color scales
library(RColorBrewer)   # Color palettes

🖥️ System Requirements

💾 Component	🎯 Minimum	⭐ Recommended	📝 Notes
R Version	4.0+	4.3+	Latest stable version
RAM	8GB	16GB+	For large dataset processing
Storage	5GB	10GB+	Including intermediate files
CPU	2 cores	4+ cores	Parallel processing support

🔧 Installation Commands

# Install R packages
Rscript -e "install.packages(c('tidyverse', 'ComplexHeatmap', 'circlize', 'viridisLite', 'ggrepel', 'RColorBrewer'))"

🚀 Quick Start Guide

⚡ Get Up and Running in 3 Steps!

1️⃣ Clone & Setup

# 📥 Clone the repository
git clone https://github.com/your-username/SesamePopulationGenomics.git
cd SesamePopulationGenomics

# 📦 Install required R packages
Rscript -e "install.packages(c('tidyverse', 'ComplexHeatmap', 'circlize'))"

2️⃣ Prepare Data

# 📁 Ensure data files are in place
ls data/
# Expected files:
# ✅ EgyptianAndInternationalSesameSNPs.vcf.gz
# ✅ PRJNA1036214Count.txt.gz
# ✅ PRJNA1197728Count.txt.gz
# ✅ SesameGeneExpressionSampleInfo.txt.gz
# ✅ SesameVarSNPeff.txt.gz

3️⃣ Run Analysis

🎯 Option A: Complete Pipeline

# 🚀 Run the complete analysis pipeline
source("scripts/analysis.R")

🧩 Option B: Modular Analysis

# 📥 Load data first
source("scripts/readData.R")
result <- readData()

# 📊 Run specific analyses
source("scripts/PCA_geneExpression.R")
pca_plot <- pca_geneExpression(result)

source("scripts/create_snp_impact_plot.R")
snp_plot <- create_snp_impact_plot(result)

🎯 Expected Runtime

📊 Analysis Component	⏱️ Time	💾 Memory
Data Loading	~2 min	~2GB
PCA Analysis	~5 min	~4GB
SNP Analysis	~10 min	~6GB
Heatmap Generation	~15 min	~8GB
Total Pipeline	~30 min	~8GB

📈 Output Files

🎨 Generated Visualizations & Reports

The analysis pipeline generates comprehensive outputs in the results/ directory:

📊 Population & Expression Analysis

📄 Output File	🎯 Content	📐 Dimensions	🎨 Format
`pca_colorful_improved.png`	PCA plot of samples by tissue type	8×8 inches	High-res PNG
`expression_heatmap_samples.png`	Expression distribution across samples	14×8 inches	Publication quality
`expression_summary_stats.txt`	Statistical summaries by tissue	Text table	Tab-delimited

🧬 Genomic Variant Analysis

📄 Output File	🎯 Content	📐 Dimensions	🎨 Format
`snp_impact_distribution.png`	SNP impact category distributions	12×8 inches	Vector graphics
`variant_type_composition.png`	Variant composition for top genes	12×8 inches	Stacked plots
`sesame_final_heatmap_filtered.png`	Chromosomal SNP density maps	16×9 inches	High-resolution

🔗 Integrated Analysis

📄 Output File	🎯 Content	📐 Dimensions	🎨 Format
`expression_heatmap_with_variants.png`	Expression-variant integrated heatmap	4000×3000 px	Ultra high-res
`top_expressed_genes.png`	Top genes with variant burden	12×9 inches	Annotated plots
`Revised_Table1_SNP_stats.csv`	Comprehensive SNP statistics	Data table	CSV format

📊 File Size Expectations

📁 results/
├── 🖼️ Images (~50MB total)
│   ├── 📊 PCA plots (~5MB each)
│   ├── 🧬 SNP visualizations (~10MB each)
│   └── 🎨 Heatmaps (~20MB each)
└── 📄 Tables (~5MB total)
    ├── 📊 Statistics files (~1MB each)
    └── 🗂️ Summary tables (~2MB each)

📚 Citation

🎓 How to Cite This Work

If you use this code, data, or findings in your research, please cite our work:

@article{azzam2025sesame,
  title={Exploring the Genetic Landscape of Sesame: Whole Genome Sequencing Reveals Distinct Egyptian Lineages and SNP Variability},
  author={Azzam, Clara R. and Arafa, Ramadan Ahmed and Ahmed, Mohamed Z. S. and Ibrahim, Shafik D. and Rizk, Mokhtar Said and Saedwi, Anas M. and Shirasawa, Kenta and Alsamman, Alsamman M.},
  journal={[Journal Name]},
  year={2025},
  doi={10.xxxx/xxxxxx}
}

🏷️ Software Citation

@software{sesame_genomics_pipeline,
  title={Sesame Population Genomics Analysis Pipeline},
  author={Alsamman, Alsamman M. and Azzam, Clara R.},
  year={2025},
  url={https://github.com/your-username/SesamePopulationGenomics}
}

📞 Contact & Support

👥 Research Team Contact

👤 Contact	📧 Email	🏛️ Affiliation	🔬 Expertise
Dr. Alsamman M. Alsamman	smahmoud@ageri.sci.eg	AGERI, Egypt	🧬 Population Genomics
Clara R. Azzam	clara.azzam@arc.sci.eg	ARC, Egypt	🌾 Plant Breeding

🐛 Issues & Support

🔧 Technical Issues: Open an Issue
📧 Research Questions: Contact corresponding author
🤝 Collaborations: Welcome! Please reach out

🌐 Links

🏛️ AGERI: Agricultural Genetic Engineering Research Institute
🌍 ICARDA: International Center for Agricultural Research in the Dry Areas
🇯🇵 Kazusa DNA: Kazusa DNA Research Institute

📄 License

This project is licensed under the MIT License

📜 License Details

MIT License

Copyright (c) 2025 Alsamman M. Alsamman, Clara R. Azzam

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

🙏 Acknowledgments

🌟 Special Thanks

🏛️ Agricultural Research Center (ARC), Egypt - Institutional support
🌍 International Center for Agricultural Research in the Dry Areas (ICARDA) - International collaboration
🇯🇵 Kazusa DNA Research Institute, Japan - Technical expertise
🧬 NCBI Sequence Read Archive - Public genomic datasets
🌾 Global Sesame Research Community - Collaborative spirit

🔬 Technical Acknowledgments

R Core Team - Statistical computing environment
Bioconductor Project - Bioinformatics tools
ComplexHeatmap - Advanced visualization capabilities
tidyverse - Data science ecosystem

🌱 Contributing to sustainable agriculture through genomics research 🌱

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🌱 Sesame Population Genomics Analysis

🧬 Exploring the Genetic Landscape of Sesame

Whole Genome Sequencing Reveals Distinct Egyptian Lineages and SNP Variability

📊 Project Overview

👥 Research Team

🏛️ Institutional Affiliations

📋 Abstract

🔍 Key Discoveries

🎯 Biological Significance

📁 Repository Structure

📊 Data Types Overview

🔬 Analysis Pipeline

🚀 Main Analysis Workflow (analysis.R)

🧩 Key Analysis Components

🛠️ Utility Functions

📂 Data Files Description

🎯 Key Findings

🏆 Major Discoveries

🌍 Population Genetics Insights

🧬 Chromosomal Diversity Patterns

💻 Requirements

📦 R Package Dependencies

🖥️ System Requirements

🔧 Installation Commands

🚀 Quick Start Guide

⚡ Get Up and Running in 3 Steps!

1️⃣ Clone & Setup

2️⃣ Prepare Data

3️⃣ Run Analysis

🎯 Expected Runtime

📈 Output Files

🎨 Generated Visualizations & Reports

📊 File Size Expectations

📚 Citation

🎓 How to Cite This Work

🏷️ Software Citation

📞 Contact & Support

👥 Research Team Contact

🐛 Issues & Support

🌐 Links

📄 License

🙏 Acknowledgments

🌟 Special Thanks

🔬 Technical Acknowledgments

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🚀 Main Analysis Workflow (`analysis.R`)