Introduction

My passion for disease biology has led me to systematically investigate the multilayered mechanisms of gene regulation. I’ve already established a deep understanding of the regulatory landscape through my prior work:

🎨 Epigenomics (ChIP-seq): Analyzing the dynamic blueprint of the epigenome (see my Epigenomic portfolio).

🎨 Transcriptomics: Investigating the functional output of gene expression (see my Transcriptomics portfolio).

However, a fundamental piece of the puzzle has been missing: Genomics. To achieve a truly holistic view—moving beyond how the code is regulated to encompass the primary variations within the code itself—I must delve into the DNA sequence.

This portfolio marks my entry into the essential world of genomics, focusing specifically on Single Nucleotide Polymorphism (SNP) analysis. This foundational work is crucial for identifying the genetic variations that drive disease susceptibility.

I am extremely fortunate to be mentored in this new domain by Dr. Metha Yaikwawong, a respected expert in SNP analysis. Under his guidance, I am committed to bridging the gap between underlying genetic variation, epigenetic control, and resulting transcriptomic profiles, thereby contributing a complete picture to the field of precision medicine.

Overview of Genome Analysis

Firstly, I would like to clarify that ”genome analysis” ≠ only “variant analysis”, but variant analysis (detecting mutations) is one major branch within it. And SNPs analysis is under variant analysis!

1️⃣ What “Genome Analysis” Actually Includes

Genome analysis is a big umbrella — it includes:

2️⃣ Variant (Mutation) Analysis — the Core Idea

When people say they “detect mutations” from sequencing data, what they really mean is they find differences between a sample genome and a reference genome. These differences are your “variants” or “mutations”.

🧬 The different between “mutations” and “variants”

In modern genomics, people mostly use “variant” because not every DNA change is harmful.

🧬 Terminology in Practice

3️⃣ Main Types of Variants

🧬Sequencing strategy for variant calling

4️⃣ Variant Calling Workflow

1. Sample → DNA Extraction → Sequencing (FASTQ)
   🎯Goal: high-quality reads for alignment
   🧩Input: paired-end FASTQ files (sample_R1.fastq, sample_R2.fastq)
   ⚙️Tools: Illumina sequencer, Nanopore, PacBio
   ↓
2. Quality Control (QC)
   🎯Goal: Check read quality, adapter contamination, GC content.
   ⚙️Tools: Adapter trimming (Trimmomatic, Cutadapt),QC report (FastQC, MultiQC)
   🧩Output: cleaned FASTQ files
   ↓
3. Alignment to Reference Genome (BAM)
   🎯Goal:Map reads to a reference genome (e.g., hg38 for human).
   ⚙️Tools: BWA-MEM, Bowtie2, HISAT2
   🧩Output: SAM → convert to BAM (samtools view -b)
   ↓
4. Post-processing of BAM (Sorting, Duplicate removal, Recalibration)
   🎯Goal: Sort reads, mark PCR duplicates, and perform base quality recalibration.
   ⚙️Tools: samtools sort, Picard MarkDuplicates, GATK BaseRecalibrator
   🧩Output: cleaned and indexed BAM file (sample.bam, sample.bai)
   ↓
5. SNP Calling (VCF)
   🎯Goal: Compare aligned reads to the reference genome → find positions with base changes.
   ⚙️Tools: GATK HaplotypeCaller, bcftools mpileup + call, FreeBayes
   🧩Output: VCF (Variant Call Format) file \
   ↓
6. Variant Filtering & Quality Control
   🎯Goal: Remove false positives and low-quality SNPs.
   ⚙️Tools: GATK VariantFiltration, vcftools, bcftools filter
   🧩Output: high-confidence VCF 🎯Filtering criteria:
   • Depth (DP) > 10 \
   • Quality (QUAL) > 30 \
   • Variant allele frequency (VAF) threshold
     ↓
7. SNP Annotation (gene, function, dbSNP ID)
   🎯Goal: Add biological meaning to each SNP: gene name, transcript, coding effect, dbSNP ID, clinical significance.
   ⚙️Tools: ANNOVAR, VEP (Variant Effect Predictor), SnpEff
   🎯Databases used:
   • dbSNP (known SNPs) \
   • ClinVar (pathogenicity) \
   • gnomAD (population frequency) \
   • RefSeq / Ensembl gene models
     ↓
8. Downstream / Biological Interpretation (depends on purpose) \

5️⃣ Conceptual Flow

🐚 UNIX (core bioinformatics)

FASTQ ──▶ QC ──▶ Alignment ──▶ BAM ──▶ Variant Calling ──▶ VCF

🧬 The different of Variant Calling (Allele Frequency Expectations): Germline vs Somatic

🐍 Python (data science layer)

VCF ──▶ Filtering ──▶ Summary stats ──▶ Visualization ──▶ Report

🧬 Full SNP Analysis Pipeline with Tool Mapping

NOTE

There are so many details about the analysis such as

• Germline and somatic variant calling is different, thus tools and setting are different.

My SNPs analysis

I will start with a VCF files which were finished upstream process.

✅ Good sources of public VCF files Sure! Here’s the same style of writing for ClinVar and other commonly used public VCF sources 👇

✅ Good sources of public VCF files

1. ClinVar (NCBI) – Provides curated variant calls with clinical significance (e.g., pathogenic, benign, uncertain significance) for the human genome (GRCh37 & GRCh38). ftp.ncbi.nlm.nih.gov
2. gnomAD (Genome Aggregation Database) – Contains population-level allele frequencies for millions of variants across exomes and genomes from diverse human -cohorts. gnomad.broadinstitute.org
3. ExAC (Exome Aggregation Consortium) – The predecessor to gnomAD, focused on exome sequencing data from over 60,000 unrelated individuals. gnomad.broadinstitute.org
4. 1000 Genomes Project / IGSR – Hosts genome-wide variant data (phase 3) across multiple populations, often used as a standard reference dataset. internationalgenome.org
5. Personal Genome Project (PGP) – Contains public genomes of volunteer participants with available VCFs (exome and whole-genome). my.pgp-hms.org

✅ Database as reference catalogue (not provide individual information)

• dbSNP (NCBI) – A comprehensive catalog of all known single nucleotide polymorphisms (SNPs) and short indels reported across studies, organized by chromosome.

1. Data set 1: 1000 Genome

I will download it from 1000 Genome

Steps

1. Download file from public resource and cut it to small file for demonstration.

# download
cd /home/kk/Jupyter_docker/SNP_interview/input/1000genome
wget https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ALL.chr1.phase3_shapeit2_mvncall_integrated_v5b.20130502.genotypes.vcf.gz

# get only first 500 lines
zcat ALL.chr1.phase3_shapeit2_mvncall_integrated_v5b.20130502.genotypes.vcf.gz | grep '^#' > 1000genome_test.vcf
zcat ALL.chr1.phase3_shapeit2_mvncall_integrated_v5b.20130502.genotypes.vcf.gz | grep -v '^#' | head -n 500 >> 1000genome_test.vcf

# compress to .gz again
bgzip 1000genome_test.vcf
tabix -p vcf 1000genome_test.vcf.gz

# change to .tsv
zcat 1000genome_test.vcf.gz | grep '^#CHROM' | sed 's/^#//' > 1000genome_test.tsv
zcat 1000genome_test.vcf.gz | grep -v '^#' | head -n 500 >> 1000genome_test.tsv

1. Inspect the fil: What’s inside a 1000G VCF

Each row (variant record) summarizes one position in the genome and how it varies across individuals.

🧠 Columns (core meaning)

🧠 How to read the INFO field

Here’s what the 1000 Genomes INFO tags mean (Phase 3 standard):

🧭 Quick biological interpretation of common INFOs

2. Data set 2:

I will download it from ClinVar (NCBI).

# get only first 500 lines
zcat clinvar_20251019.vcf.gz | grep '^#' > clinvar_test.vcf
zcat clinvar_20251019.vcf.gz | grep -v '^#' | head -n 500 >> clinvar_test.vcf

# compress to .gz again
bgzip clinvar_test.vcf
tabix -p vcf clinvar_test.vcf.gz

# change to .tsv
zcat clinvar_test.vcf | grep '^#CHROM' | sed 's/^#//' > clinvar_test.tsv
zcat clinvar_test.vcf | grep -v '^#' | head -n 500 >> clinvar_test.tsv

Then you can apply your pipeline: read the VCF with Python, filter by QUAL, count SNPs, plot distribution, etc.

If you want a bit more complexity, use a multi-sample VCF from IGSR (1000 Genomes) which allows you to do allele-frequency, sample-based filtering, etc.

Other related posts

Acknowledgment

• I would like to acknowledge Dr. Metha Yaikwawong for his encouragement and the valuable knowledge he provided on the earlier days of my programming journey.

I’m very happy 🥰 that you are visiting my computational biology portfolio and would be even happier if you could provide suggestions or feedback 🤩.

You can contact me through various online platforms here 📬 or leave a comment below using GitHub account. 👇🏼