NGS Exome Analysis Pipeline

A complete, automated pipeline for Next-Generation Sequencing (NGS) exome analysis. Process FASTQ files to annotated variants with a single command.

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⚡ Quick Start (3 Commands)

# 1. Clone the repository (run once)
git clone https://github.com/Babajan-B/Exome-Analysis-End-to-END.git ~/NGS
cd ~/NGS

# 2. Install every dependency (tools, reference genome, databases)
bash install_all.sh

# 3. Run the full pipeline on all FASTQ pairs in ~/NGS/data/
bash ULTIMATE_MASTER_PIPELINE.sh ~/NGS/data 16

After the first run you only need step 3.
The pipeline creates a timestamped ZIP in ~/NGS/ with every report, annotation, and summary.

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🎯 What It Does

Complete End-to-End Pipeline

Input: Paired-end FASTQ files
Output: Annotated variants, functional classifications, quality reports

Automated Steps:

1. Quality Control (FastQC)
2. Read Trimming (fastp)
3. Alignment (BWA-MEM to hg19)
4. BAM Processing (SAMtools, GATK)
5. Variant Calling (GATK HaplotypeCaller)
6. Variant Filtering (PASS only)
7. ANNOVAR Annotation (5 databases)
   • refGene
   • ClinVar
   • gnomAD
   • dbSNP (avsnp150)
   • Prediction scores (dbnsfp42a)
8. snpEff Annotation
9. Zygosity Classification (Heterozygous/Homozygous)
10. Functional Separation
    • By Type: SNPs, Insertions, Deletions
    • By Location: Exonic, Non-Exonic
    • By Effect: Nonsynonymous, Synonymous, Stopgain, Frameshift
11. VCF Compression & Indexing (for IGV)
12. ZIP Archive with all essential results

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🚀 Features

✅ One-Command Installation - Everything installed automatically
✅ One-Command Analysis - From FASTQ to results in one run
✅ Dual Annotation - Both ANNOVAR and snpEff
✅ Smart Filtering - PASS variants only, reduces file sizes by 90%
✅ Functional Classification - Automatic separation by variant type and effect
✅ Zygosity Information - Het/Hom status for each variant
✅ IGV-Ready - Compressed, indexed VCFs for genome browser
✅ Excel-Ready - Tab-delimited TXT files for easy analysis
✅ Production-Ready - Used in clinical research labs

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📋 System Requirements

Minimum

• OS: Linux (Ubuntu 20.04+, CentOS 7+, or similar)
• RAM: 16 GB minimum, 32 GB recommended
• Storage: 100 GB free space
• CPU: 8+ cores (16+ recommended)
• Internet: Required for initial setup

Software (Auto-Installed)

• Python 3.7+
• Java 21 (for GATK, snpEff)
• Perl (for ANNOVAR)
• All bioinformatics tools installed by install_all.sh

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🛠️ Installation

Step 1: Clone Repository

git clone https://github.com/Babajan-B/Exome-Analysis-End-to-END.git ~/NGS
cd ~/NGS

Step 2: Run Installation Script

bash install_all.sh

This installs:

• FastQC, fastp, BWA, SAMtools, GATK, snpEff
• Reference genome (hg19) with indices
• ANNOVAR with 9 clinical databases
• All dependencies

Time: 45-60 minutes
Storage: ~15 GB

Step 3: Verify Installation

bash CHECK_INSTALLATION.sh

Confirms all tools and databases are ready.

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🎯 Usage

Standard Workflow

# 1. Add FASTQ files to data directory
mkdir -p ~/NGS/data
# Copy your *_R1.fastq.gz and *_R2.fastq.gz files here

# 2. Run complete pipeline
cd ~/NGS
bash ULTIMATE_MASTER_PIPELINE.sh ~/NGS/data 16

Parameters:

• First argument: Directory containing FASTQ files
• Second argument: Number of CPU threads (default: 16)

The pipeline will:

• Auto-detect all sample pairs
• Process each sample sequentially
• Generate comprehensive results
• Create final ZIP archive

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📦 Output Structure

Final Deliverable

One ZIP File: NGS_Results_Complete_[timestamp].zip (~400-600 MB)

Contains:

results/
├── Sample1/
│   ├── annovar/
│   │   ├── annotated_Sample1.hg19_multianno.txt          # ANNOVAR annotation
│   │   ├── annotated_Sample1_with_zygosity.txt           # With Het/Hom status
│   │   ├── annotated_Sample1.hg19_multianno.vcf.gz       # ANNOVAR VCF
│   │   ├── separated_by_type/
│   │   │   ├── SNPs.txt
│   │   │   ├── Insertions.txt
│   │   │   └── Deletions.txt
│   │   ├── functional_classification/
│   │   │   ├── SNPs_Exonic.txt
│   │   │   ├── SNPs_NonExonic.txt
│   │   │   ├── Exonic_Nonsynonymous.txt
│   │   │   ├── Exonic_Synonymous.txt
│   │   │   ├── Exonic_Stopgain.txt
│   │   │   └── Exonic_Frameshift.txt
│   │   └── snpeff/
│   │       ├── Sample1_snpEff_annotated.vcf.gz          # snpEff VCF
│   │       ├── Sample1_snpEff_summary.html              # snpEff report
│   │       └── Sample1_snpEff_summary.csv
│   ├── fastqc/
│   │   ├── Sample1_R1_fastqc.html                       # QC reports
│   │   └── Sample1_R2_fastqc.html
│   └── trimmed/
│       └── fastp_report.html                            # Trimming stats
└── MASTER_ANALYSIS_SUMMARY.txt                          # Overall summary

Excluded from ZIP (to keep size small):

• ❌ BAM files (large, 15-20 GB each)
• ❌ FASTQ files (large, input data)
• ❌ Intermediate files

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📊 Understanding the Results

Priority Files for Analysis

1. annotated_[SAMPLE]_with_zygosity.txt

   • Main annotation file with zygosity
   • Open in Excel/LibreOffice
   • Filter by:
     • ClinVar significance (Pathogenic/Likely Pathogenic)
     • gnomAD frequency (rare: < 0.01)
     • Functional effect (nonsynonymous, stopgain)
     • Zygosity (Heterozygous/Homozygous)

2. functional_classification/Exonic_Nonsynonymous.txt

   • Coding variants that change amino acids
   • High priority for pathogenicity analysis

3. functional_classification/Exonic_Stopgain.txt

   • Variants creating premature stop codons
   • Often deleterious

4. snpeff/[SAMPLE]_snpEff_summary.html

   • Visual summary of variant effects
   • Statistics and charts

5. VCF Files (.vcf.gz)

   • Load into IGV (Integrative Genomics Viewer)
   • Visualize variants in genomic context

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🔬 Pipeline Details

Annotation Databases

ANNOVAR (5 databases):

• refGene: Gene-based annotation
• ClinVar: Clinical significance
• gnomAD: Population frequencies (exomes)
• dbSNP (avsnp150): rsIDs and allele frequencies
• dbnsfp42a: Prediction scores (SIFT, PolyPhen, CADD, etc.)

snpEff:

• Functional consequences (HIGH/MODERATE/LOW impact)
• Protein change predictions
• Transcript-level annotations

Functional Classifications

By Variant Type:

• SNPs (Single Nucleotide Polymorphisms)
• Insertions
• Deletions
• MNPs (Multiple Nucleotide Polymorphisms)

By Location:

• Exonic (in coding regions) vs Non-Exonic
• UTR, intronic, intergenic

By Effect:

• Nonsynonymous (amino acid change)
• Synonymous (silent mutation)
• Stopgain (premature termination)
• Frameshift (indel changing reading frame)

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⏱️ Performance

Per Sample (Typical Exome):

• Raw FASTQ: 6-8 GB
• Analysis time: 2.5-3 hours (16 CPUs)
• Storage used: 20-25 GB
• Final ZIP: 150-200 MB

3 Samples:

• Total time: 7-9 hours
• Runs sequentially (one completes → next starts)

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🐛 Troubleshooting

Installation Issues

# Verify installation
bash CHECK_INSTALLATION.sh

# Check disk space
df -h ~

# Check Java version (needs 21+)
java -version

Common Errors

Error	Solution
command not found	Re-run install_all.sh or check PATH
Out of memory	Reduce thread count or add more RAM
No FASTQ pairs found	Check file naming: *_R1.fastq.gz + *_R2.fastq.gz
snpEff class version error	Install Java 21: sudo apt install openjdk-21-jdk

Getting Logs

# Pipeline logs for each sample
cat ~/NGS/results/[SAMPLE]/pipeline.log

# Summary of all samples
cat ~/NGS/MASTER_ANALYSIS_SUMMARY.txt

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📚 Documentation

• PROJECT_STRUCTURE.md - Detailed project layout
• RUN_ULTIMATE_PIPELINE.txt - Quick reference guide
• LICENSE - MIT License

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🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

For major changes, open an issue first.

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🙏 Acknowledgments

This pipeline integrates these excellent tools:

• FastQC - Quality control
• fastp - Read preprocessing
• BWA - Read alignment
• SAMtools - BAM/SAM manipulation
• GATK - Variant calling
• ANNOVAR - Variant annotation
• snpEff - Functional annotation

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📝 License

MIT License - see LICENSE file for details.

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📧 Support

• Issues: GitHub Issues
• Documentation: Check the docs/ folder
• Updates: Watch the repository for new releases

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🌟 Citation

If you use this pipeline in your research:

NGS Exome Analysis Pipeline
https://github.com/Babajan-B/Exome-Analysis-End-to-END

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Three scripts. One command. Complete analysis.

🧬 Made for researchers, by researchers.