🧬NGS WES/TRS Analysis

This repository provides a comprehensive Next-Generation Sequencing (NGS) workflow designed for Whole Exome Sequencing (WES) and Targeted Region Sequencing (TRS) analysis — from raw FASTQ files to annotated variant calls.

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📖 Overview

The project explains and demonstrates:

• Differences between WGS, WES, and TRS
• Metadata identification in NCBI SRA
• Quality Control and read pre-processing
• Alignment, variant calling, and annotation steps
• Handling target region (BED) files
• Clinical interpretation of VCF outputs

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🧩 Tools Used

Step	Tool	Purpose
Raw QC	FastQC	Assess sequencing quality
Trimming	Trim Galore	Remove adapters & low-quality bases
Alignment	BWA	Map reads to reference genome
BAM Processing	SAMtools	Sort, index, and manage alignments
Variant Calling	GATK	Detect SNPs and indels
Annotation	SnpEff	Predict functional and clinical impact

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🧠 Workflow Summary

1. Raw Data QC → FastQC
2. Trimming & Filtering → Trim Galore
3. Alignment → BWA
4. Post-processing → SAMtools (sort, index, mark duplicates)
5. Variant Calling → GATK (HaplotypeCaller)
6. Annotation → SnpEff
7. Interpretation → Prioritize variants within target regions (using BED files)

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💡 Key Concepts

• WGS vs WES vs TRS – Understanding scope, depth, and cost
• Target BED Files – Define genomic intervals for exome/panel analysis
• Coverage Analysis – Assess sequencing depth and breadth
• Clinical Variant Reporting – Use ClinVar, OMIM, COSMIC references

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🧰 Skills Highlighted

• NGS Data Pre-processing
• Variant Detection and Annotation
• Workflow Optimization for Exome/Panel Data
• Familiarity with GATK Best Practices
• Genomic Data Interpretation

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📑 Project Report

1️⃣ Introduction

Next-Generation Sequencing (NGS) has transformed genomics by enabling fast, cost-effective sequencing of DNA and RNA. Among NGS approaches, Whole Exome Sequencing (WES) and Targeted Region Sequencing (TRS) are powerful methods for identifying variants within coding or specific genomic regions.
This project focuses on the bioinformatics workflow for WES/TRS, outlining key computational steps and tools used for transforming raw sequencing reads into biologically meaningful variant annotations.

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2️⃣ Objectives

• To perform quality assessment and preprocessing of NGS reads.
• To align reads to a reference genome and generate BAM files.
• To identify variants (SNPs and indels) using GATK and SAMtools.
• To annotate variants using SnpEff and interpret their biological significance.
• To compare workflows and outputs for WGS, WES, and TRS data.

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3️⃣ Materials and Methods

Tools and Software Used

Step	Tool	Description
Quality Check	FastQC	Evaluate raw sequence quality
Trimming	Trim Galore	Remove low-quality bases and adapters
Alignment	BWA	Map reads to reference genome
Post-Processing	SAMtools	Sort, index, and manage BAM files
Variant Calling	GATK HaplotypeCaller	Identify SNPs and indels
Annotation	SnpEff	Predict variant effects on genes/proteins

Workflow Overview

1. Input Data: Raw paired-end FASTQ files
2. Quality Control: Evaluate read quality using FastQC
3. Read Trimming: Remove adapters and low-quality sequences
4. Alignment: Map reads to reference genome using BWA
5. Sorting & Indexing: Organize and index BAM files
6. Variant Calling: Detect genomic variants using GATK
7. Annotation: Use SnpEff to classify and interpret variant impacts

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4️⃣ Results and Discussion

• High-quality reads were confirmed using FastQC before and after trimming.
• Alignment achieved high mapping efficiency with minimal read loss.
• Variant calling identified SNPs and small indels within targeted regions.
• SnpEff annotation provided functional classification of variants (e.g., synonymous, missense, nonsense).
• The workflow effectively demonstrates how bioinformatics pipelines translate raw NGS data into meaningful genomic insights.

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5️⃣ Conclusion

This project successfully demonstrates a comprehensive NGS workflow for WES and TRS analysis.
The integration of multiple open-source tools ensures reliable variant discovery, annotation, and biological interpretation.
Such workflows are essential for genomic research, clinical diagnostics, and precision medicine.

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📄 Project Report

You can view the complete project report here:https://github.com/DEBOPRIYA2320/NGS-WES-TRS-Analysis/blob/ac8d517bd6ba9b9dabdf7e66be21c139fe3b2c38/NGS%20-%20Genomics%20data%20analysis%20(Debopriya).pdf

📫 Contact

Author: Debopriya
📧 [email protected]
🔗 GitHub
🔗 LinkedIn

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🧾 License

This project is licensed under the MIT License.

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