Bioinformatics - variant analysis

Variant analysis is essential for detecting genetic differences, including single nucleotide polymorphisms (SNPs), insertions/deletions (indels), and structural variants. It is widely used in clinical genomics, population genetics, and disease research.

Pipeline Overview

1. Demultiplexing: Sorting sequencing reads based on sample-specific barcodes.
2. Quality Control (QC): Evaluation of raw sequencing quality to identify potential issues such as contamination or low read quality.
3. Alignment: Mapping the sequencing reads to a reference genome (e.g., using BWA, Bowtie2).
4. Variant Calling: Identifying variants (SNPs, indels, and structural variations) using GATK.
5. Variant Annotation: Annotating variants with biological information such as gene locations and predicted functional impacts.
6. Variant Filtering: Applying quality filters to select high-confidence variants based on factors like read depth, genotype quality, and variant allele frequency.
7. Visualization: Visualizing variants and associated data with tools like IGV (Integrative Genomics Viewer).

Expected Result Output

1. Main Results File: A .vcf (Variant Call Format) file containing all identified variants with details such as genotype, reference allele, and variant allele.
2. Additional Results:
   • SNP/Indel List: A detailed list of variants along with their annotations (e.g., functional predictions).
   • IGV Browser View: A snapshot of the alignment and variants across specific regions.