End-to-end DNA sequencing analysis pipeline for variant discovery and functional annotation using FastQC, fastp, BWA-MEM, SAMtools, Picard, GATK HaplotypeCaller and Ensembl Variant Effect Predictor (VEP).
Next-Generation Sequencing (NGS) technologies have transformed genomics by enabling rapid and cost-effective identification of genetic variants across the genome. Variant calling is a fundamental step in DNA sequencing analysis, allowing researchers to detect single nucleotide polymorphisms (SNPs), insertions, deletions (InDels), and other genomic alterations that may contribute to genetic diversity, disease susceptibility, or biological function.
This project presents a complete end-to-end DNA-Seq variant calling and functional annotation workflow implemented using widely adopted open-source bioinformatics tools. Starting from raw paired-end sequencing reads, the pipeline performs quality assessment, preprocessing, sequence alignment, post-alignment processing, variant discovery, and functional annotation to generate biologically meaningful insights from sequencing data.
The workflow was implemented on publicly available human sequencing datasets from NCBI BioProject PRJNA627318 and demonstrates practical experience with Linux-based bioinformatics pipelines, reproducible genomics analysis, and industry-standard tools commonly used in academic research and clinical genomics.
This project implements a complete DNA sequencing variant calling pipeline using publicly available human next-generation sequencing (NGS) datasets. Starting from raw paired-end sequencing reads, the workflow performs quality assessment, read preprocessing, sequence alignment, post-alignment processing, variant calling, and functional annotation using industry-standard bioinformatics tools. The pipeline demonstrates a reproducible Linux-based workflow for identifying and interpreting genomic variants and provides practical experience with widely adopted tools used in genomics research.
- Project Overview
- Objectives
- Dataset
- Pipeline Workflow
- Software Used
- Directory Structure
- Methodology
- Results
- Variant Annotation
- Skills Demonstrated
- Acknowledgements
- References
- License
This repository presents an end-to-end DNA sequencing variant calling pipeline implemented on human next-generation sequencing (NGS) data.
The workflow starts from raw paired-end FASTQ files and progresses through quality control, read preprocessing, sequence alignment, BAM processing, variant calling, and functional annotation.
The pipeline follows widely adopted bioinformatics best practices and employs industry-standard software including FastQC, fastp, BWA-MEM, SAMtools, Picard, GATK HaplotypeCaller, and Ensembl VEP.
- Assess raw sequencing read quality
- Perform adapter trimming and quality filtering
- Align sequencing reads to the human reference genome (GRCh38 Chromosome 17)
- Process alignment files for downstream analysis
- Detect genomic variants using GATK HaplotypeCaller
- Perform functional annotation using Ensembl Variant Effect Predictor (VEP)
- Build a reproducible DNA sequencing workflow
Organism: Homo sapiens
Reference Genome: GRCh38 (Chromosome 17)
BioProject: PRJNA627318
SRA Project: SRP258414
| Sample ID |
|---|
| SRR11603071 |
| SRR11603072 |
| SRR11603073 |
| SRR11603080 |
| Tool | Purpose |
|---|---|
| FastQC | Raw read quality assessment |
| fastp | Adapter trimming and quality filtering |
| BWA-MEM | Sequence alignment |
| SAMtools | SAM/BAM processing |
| Picard | BAM processing and duplicate handling |
| GATK HaplotypeCaller | Variant calling |
| Ensembl VEP | Functional annotation |
The pipeline was developed and tested using the following software versions.
| Software | Version |
|---|---|
| FastQC | v0.12.1 |
| fastp | v0.23.4 |
| BWA-MEM | v0.7.17 |
| SAMtools | v1.19.2 |
| Picard | v3.4.0 |
| GATK HaplotypeCaller | v4.6.2.0 |
| Tabix / bgzip (HTSlib) | v1.19.1 |
| Ensembl Variant Effect Predictor (VEP) | Release 115 (Web Interface) |
| Java | OpenJDK 17 |
| Ubuntu | 24.04 LTS (WSL2) |
dna-seq-variant-calling-pipeline
│
├── docs/
├── figures/
├── workflow/
├── scripts/
├── results/
├── README.md
├── LICENSE
└── requirements.md
Raw FASTQ
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FastQC
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fastp
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BWA-MEM Alignment
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SAM
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BAM
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Sorted BAM
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Duplicate Removal
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Picard
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GATK HaplotypeCaller
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VCF
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Ensembl Variant Effect Predictor (VEP)
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Variant Interpretation
The DNA-Seq variant calling workflow was performed using publicly available human sequencing datasets from NCBI BioProject PRJNA627318. The analysis was carried out on Ubuntu 24.04 LTS (WSL2) using widely adopted bioinformatics tools.
Raw paired-end FASTQ files were evaluated using FastQC to assess sequencing quality, GC content, sequence duplication levels, adapter contamination, and per-base sequence quality.
Raw reads were processed using fastp to remove adapter sequences, filter low-quality reads, and improve the overall quality of the sequencing data before downstream analysis.
The human reference genome (GRCh38 Chromosome 17) was downloaded and indexed using BWA, enabling efficient sequence alignment.
Filtered paired-end reads were aligned against the indexed human reference genome using BWA-MEM, generating Sequence Alignment/Map (SAM) files.
SAM files were converted to Binary Alignment/Map (BAM) format using SAMtools. The BAM files were subsequently sorted to organize alignments according to genomic coordinates.
PCR duplicates and alignment metadata were processed using SAMtools and Picard to prepare high-quality alignment files for variant discovery.
Genomic variants, including single nucleotide polymorphisms (SNPs) and small insertions/deletions (InDels), were identified using GATK HaplotypeCaller, producing Variant Call Format (VCF) files.
The generated VCF files were functionally annotated using Ensembl Variant Effect Predictor (VEP) to determine variant consequences, affected genes, transcript information, and known genomic annotations.
Annotated variants were reviewed to examine genomic locations, functional consequences, and transcript-level effects, providing biological interpretation of the identified variants.
The DNA-Seq variant calling and functional annotation workflow was successfully completed for four human sequencing samples from BioProject PRJNA627318.
| Sample | Unique Variants | Genes Affected | Transcripts | Status |
|---|---|---|---|---|
| SRR11603071 | 3,150 | 313 | 2,390 | ✅ Completed |
| SRR11603072 | 3,299 | 479 | 3,457 | ✅ Completed |
| SRR11603073 | 13,681 | 1,187 | 6,815 | ✅ Completed |
| SRR11603080 | 4,837 | 490 | 3,444 | ✅ Completed |
The majority of annotated variants were located in intronic regions, followed by 3' untranslated regions (3' UTRs), missense variants, and synonymous variants. This distribution is consistent with typical whole-genome DNA sequencing datasets, where most detected variants occur outside protein-coding exons.
- Successfully processed 4 human DNA sequencing samples
- Completed end-to-end variant calling using GATK HaplotypeCaller
- Functionally annotated all variants using Ensembl Variant Effect Predictor (VEP)
- Identified thousands of genomic variants across the analysed samples
- Detected variants affecting hundreds of genes and thousands of transcripts
- Successfully processed four paired-end sequencing datasets
- Generated sorted BAM files
- Performed duplicate handling
- Identified genomic variants using GATK HaplotypeCaller
- Annotated variants using Ensembl Variant Effect Predictor (VEP)
| Metric | Value |
|---|---|
| Organism | Homo sapiens |
| BioProject | PRJNA627318 |
| SRA Project | SRP258414 |
| Samples Analysed | 4 |
| Total Unique Variants | 24,967 |
| Total Genes Affected | 2,469* |
| Total Annotated Transcripts | 16,106 |
| Variant Calling Tool | GATK HaplotypeCaller |
| Annotation Tool | Ensembl Variant Effect Predictor (VEP) |
| Pipeline Stages | 8 |
| Software Tools Used | 7 |
| Operating System | Ubuntu 24.04 LTS (WSL2) |
| Component | Version |
|---|---|
| Operating System | Ubuntu 24.04 LTS (WSL2) |
| Java | OpenJDK 21 |
| FastQC | Installed via Ubuntu package manager |
| fastp | Installed via Ubuntu package manager |
| BWA | 0.7.17 |
| SAMtools | 1.19 |
| Picard | 3.1.1 |
| GATK | 4.6.2.0 |
| Ensembl VEP | Web Version |
| Domain | Skills |
|---|---|
| Bioinformatics | Variant Calling, DNA Sequencing |
| Programming | Bash |
| Operating System | Linux |
| Tools | GATK, BWA, Picard, SAMtools, VEP |
Although this pipeline successfully performs DNA-Seq variant calling and functional annotation, several enhancements can further improve its analytical capabilities:
- Perform joint variant calling across multiple samples to improve variant detection consistency.
- Apply Variant Quality Score Recalibration (VQSR) or advanced filtering strategies for higher-confidence variant calls.
- Integrate additional annotation databases such as ClinVar, dbNSFP, or gnomAD for clinical and population-level interpretation.
- Perform downstream pathway and gene enrichment analyses to investigate the biological significance of identified variants.
- Visualize variants using Integrative Genomics Viewer (IGV) for manual validation and inspection.
- Automate the complete workflow using workflow management systems such as Snakemake or Nextflow for improved reproducibility and scalability.
- Containerize the pipeline using Docker or Singularity to simplify deployment across different computing environments.
- Extend the workflow to whole-genome or whole-exome sequencing datasets for large-scale genomic studies.
This project was completed as part of the DrOmics DNA Sequencing Internship. The workflow was implemented and executed using publicly available human sequencing data from BioProject PRJNA627318.
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Andrews S. FastQC.
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Chen et al. fastp.
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Li H. BWA-MEM.
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Li et al. SAMtools.
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Picard Toolkit.
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Van der Auwera et al. GATK Best Practices.
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McLaren et al. Ensembl Variant Effect Predictor.
This project is licensed under the MIT License.
Shreejan V
B.Tech Biotechnology (Computational Biology)
SRM Institute of Science and Technology
GitHub: https://github.com/shreejan-v
Project completed as part of the DrOmics DNA Sequencing Internship.