Aim
SNP Detection and Analysis using NGS teaches how to detect, filter, annotate, and interpret SNPs from next-generation sequencing data. Learn end-to-end variant calling workflows, quality control, basic statistics, and reporting for research and clinical research settings.
Program Objectives
- NGS Basics: reads, coverage, paired-end, FASTQ/BAM/VCF formats.
- QC Workflow: read QC, trimming, contamination checks.
- Alignment: mapping to reference and key metrics.
- Variant Calling: SNP calling steps and common tools (workflow view).
- Filtering: depth, quality, allele balance, strand bias concepts.
- Annotation: gene impact, coding/non-coding, population frequency concepts.
- Interpretation: pathogenicity basics, GWAS link, study design context.
- Capstone: build a SNP analysis report from an NGS dataset.
Program Structure
Module 1: SNPs and NGS Foundations
- SNPs vs indels; germline vs somatic (intro).
- Sequencing design: WGS, WES, targeted panels (overview).
- Coverage, depth, duplication, mapping quality.
- File formats and metadata essentials.
Module 2: Read QC and Preprocessing
- FASTQ quality checks and adapter trimming.
- Filtering low-quality reads and artifacts.
- Basic contamination and sample identity concepts.
- QC reporting checklist.
Module 3: Alignment and Post-Processing
- Reference genome selection and indexing concepts.
- Alignment workflow and key metrics.
- Sorting, marking duplicates, indexing (overview).
- Common pitfalls: repeats, pseudogenes, low-complexity regions.
Module 4: Variant Calling (SNP Pipeline)
- Variant calling logic and outputs (VCF fields).
- Single-sample vs joint genotyping concepts.
- Germline vs somatic calling differences (overview).
- Variant quality scores and recalibration concepts (intro).
Module 5: Filtering and QC of Variants
- Depth, genotype quality, allele balance, strand bias.
- Hard filters vs model-based filtering concepts.
- Batch effects and sample-level QC.
- Creating a clean SNP set for downstream analysis.
Module 6: Variant Annotation and Prioritization
- Functional impact: synonymous, missense, nonsense, splice-site (overview).
- Population frequency concepts and filtering common variants.
- Gene panels and phenotype-driven prioritization (intro).
- Creating an annotation table for reporting.
Module 7: Interpretation and Basic Statistics
- Association basics: case-control and cohort context (intro).
- Quality checks: Ti/Tv ratio, heterozygosity, missingness (overview).
- Clinical research interpretation basics (non-diagnostic): evidence strength and limits.
- Communicating uncertainty and avoiding over-claims.
Module 8: Reporting, Reproducibility, and Best Practices
- Pipeline documentation: parameters, versions, provenance.
- Reproducible workflows (intro): containers and workflow managers.
- Data privacy and consent basics.
- Final report structure: methods, QC, results, limitations.
Final Project
- Run a SNP workflow on a provided dataset (WES/targeted).
- Deliverables: QC summary + filtered VCF + annotation table + short report.
- Submit: report with key SNPs and interpretation notes.
Participant Eligibility
- Students/researchers in Genetics, Biotechnology, Bioinformatics, Molecular Biology
- Basic genomics concepts helpful; basic Linux/R/Python helpful
- Anyone starting NGS variant analysis
Program Outcomes
- Understand SNP calling workflows and key QC metrics.
- Filter and annotate SNPs for research questions.
- Interpret results with clear limitations.
- Create a portfolio-ready SNP analysis report.
Program Deliverables
- e-LMS Access: lessons, sample datasets, templates.
- Toolkit: QC checklist, filtering template, annotation sheet, report outline.
- Capstone Support: feedback on pipeline and report.
- Assessment: certification after submission.
- e-Certification and e-Marksheet: digital credentials.
Future Career Prospects
- NGS Variant Analysis Trainee
- Bioinformatics Associate (Genomics)
- Genomics Research Assistant
- Clinical Research Genomics Assistant
Job Opportunities
- Genomics Labs/CROs: variant calling and annotation support.
- Hospitals/Research Centers: translational genomics research teams.
- Biotech/Pharma: biomarker and genetics analytics groups.
- Universities: population genetics and genomics research labs.
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