Whole Genome Sequencing Analysis
Comprehensive Genomic Variant Discovery and Population Genetics Study
Project Overview
This advanced bioinformatics project focuses on the comprehensive analysis of whole genome sequencing (WGS) data to identify genetic variants, understand population structure, and discover genomic signatures associated with complex traits and diseases. Through sophisticated computational pipelines and statistical methods, we process terabytes of sequencing data to extract meaningful biological insights.
The project encompasses the complete WGS analysis workflow—from raw sequencing reads to variant calling, annotation, and functional interpretation. By integrating population genomics, evolutionary analysis, and machine learning approaches, we uncover genetic diversity patterns, identify disease-associated variants, and contribute to precision medicine initiatives. Our work advances understanding of human genetic variation and its implications for health and disease.
Project Details
- Status: Completed
- Duration: 2022-2024
- Role: Lead Bioinformatician
- Field: Genomics, Population Genetics
- Scale: Large-scale Analysis
Research Objectives
Variant Discovery
Identify SNPs, indels, and structural variants across whole genomes using state-of-the-art calling algorithms.
Population Genetics
Analyze genetic diversity, population structure, and evolutionary patterns across different populations.
Clinical Translation
Identify disease-associated variants and provide actionable insights for precision medicine applications.
Computational Pipeline
Data Processing
- Quality control and read alignment (BWA, Bowtie2)
- Duplicate removal and base quality recalibration
- Variant calling (GATK, FreeBayes, DeepVariant)
- Structural variant detection (Manta, Delly)
- Joint genotyping and variant filtering
Analysis & Interpretation
- Variant annotation (VEP, ANNOVAR, SnpEff)
- Population genetics analysis (PCA, ADMIXTURE)
- Selection signature detection
- Functional impact prediction (CADD, SIFT, PolyPhen)
- Genome-wide association studies (GWAS)
Key Achievements & Contributions
- Processed and analyzed terabytes of WGS data from diverse populations using high-performance computing clusters
- Identified millions of genetic variants including rare and population-specific SNPs, indels, and structural variants
- Developed automated pipelines for quality control, variant calling, and annotation using Snakemake and Nextflow
- Characterized population structure and genetic diversity patterns revealing migration and admixture events
- Discovered disease-associated variants through case-control studies and genetic burden analysis
- Implemented machine learning models for variant pathogenicity prediction and phenotype association
- Created comprehensive visualization dashboards for exploring genomic variation patterns and population genetics results
Technology Stack
Results & Impact
1000+
Genomes Analyzed
10M+
Variants Identified
High
Accuracy Pipeline
Novel
Discoveries
Applications & Impact
🧬 Precision Medicine
Identification of actionable variants for personalized treatment strategies and pharmacogenomics.
🌍 Population Genomics
Understanding human genetic diversity, evolution, and migration patterns across populations.
🔬 Disease Research
Discovery of genetic risk factors for complex diseases and rare genetic disorders.
Technical Highlights
🚀 Performance Optimization
- Parallel processing on HPC clusters
- Optimized memory management for large datasets
- Automated workflow orchestration
- Real-time quality metrics monitoring
📊 Data Integration
- Multi-omics data integration
- Public database annotation (dbSNP, ClinVar, gnomAD)
- Functional genomics resources integration
- Interactive visualization dashboards
Interested in Genomics Research?
Let's discuss whole genome sequencing, population genetics, or bioinformatics collaborations.