Variant Calling

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Identify genetic variants (SNPs, insertions, deletions) by comparing sequences

Overview

Variant Calling identifies genetic variations (SNPs, insertions, deletions) by comparing sample sequences to a reference. This is essential for:

- **Personalized medicine**: Identifying disease-associated variants - **Population genetics**: Studying genetic diversity - **Evolutionary biology**: Tracking genetic changes - **Clinical diagnostics**: Detecting pathogenic mutations

Variant types

- **SNPs (Single Nucleotide Polymorphisms)**: Single base changes - **Insertions**: Added nucleotides - **Deletions**: Removed nucleotides - **Indels**: Combined insertions and deletions

Input Format

Required

- **Reference sequence**: FASTA format reference genome or sequence - **Sample sequences**: Sequences to compare against reference (FASTA or FASTQ)

Parameters

- **Quality threshold**: Minimum quality score for variant calling - **Minimum mapping quality**: Quality threshold for read alignment - **Minimum base quality**: Quality threshold for individual bases

Example

Reference: Reference genome in FASTA format Sample: Sequencing reads in FASTQ or FASTA format

Output Explanation

Variant Results

- **Total Variants**: Total number of variants identified - **SNPs**: Count of single nucleotide polymorphisms - **Insertions**: Count of inserted nucleotides - **Deletions**: Count of deleted nucleotides

Variant Details

- **Position**: Genomic position of the variant - **Reference allele**: Base/sequence in reference - **Alternate allele**: Base/sequence in sample - **Quality scores**: Confidence in variant call - **Depth**: Number of reads supporting the variant

Output formats

- VCF (Variant Call Format) file - Variant report with detailed annotations

Use Cases

**1. Clinical Genetics** - Identify disease-causing mutations - Screen for genetic variants - Support diagnostic decisions

**2. Population Genetics** - Study genetic diversity - Identify population-specific variants - Analyze allele frequencies

**3. Cancer Genomics** - Identify somatic mutations - Study tumor evolution - Guide targeted therapies

Tips & Best Practices

1. **Quality control**: Ensure high-quality reference and sample sequences 2. **Coverage depth**: Aim for adequate sequencing depth (typically 30x or higher) 3. **Quality filtering**: Use appropriate quality thresholds to balance sensitivity and specificity 4. **Validation**: Validate important variants with independent methods 5. **Annotation**: Annotate variants with functional information 6. **Population databases**: Compare variants against population databases (e.g., dbSNP, gnomAD)

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