Tutorial 1: Introduction

Getting started with genomic data analysis

Learning Objectives

By the end of this tutorial, you will be able to:

1. Set up your R environment for genomic analysis
2. Load and inspect a VCF file
3. Understand the basic structure of variant data

Prerequisites

• R (≥ 4.0) installed
• RStudio (recommended)
• ~30 minutes

Setup

First, install the required packages:

install.packages(c("vcfR", "tidyverse"))

# Install toolone from GitHub
# install.packages("devtools")
devtools::install_github("Breeding-Insight/BIGr")

Load the packages:

library(vcfR)
library(tidyverse)
library(toolone)

Loading Data

From a file

# Read a VCF file
vcf <- read.vcfR("path/to/your/file.vcf.gz")

# Check what you loaded
vcf

Expected output:

***** Object of Class vcfR *****
1000 samples
50000 variants
Object size: 125.3 Mb

Using example data

For this tutorial, we’ll use built-in example data:

data("example_vcf", package = "toolone")
vcf <- example_vcf

Inspecting the Data

Basic information

# How many samples?
ncol(vcf@gt) - 1

# How many variants?
nrow(vcf@fix)

# What chromosomes are present?
unique(vcf@fix[, "CHROM"])

The VCF structure

A VCF file has three main components:

1. Meta information (header lines starting with ##)
2. Fixed fields (CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO)
3. Genotype data (sample-level information)

# View the fixed fields
head(vcf@fix)

# View genotype data for first few samples
vcf@gt[1:5, 1:5]

NoteVCF Format Details

For a complete description of VCF format, see the VCF specification.

Extracting Information

Get genotypes as a matrix

# Extract genotypes (GT field)
gt_matrix <- extract.gt(vcf, element = "GT")

# View dimensions
dim(gt_matrix)

# Preview
gt_matrix[1:5, 1:5]

Get depth information

# Extract read depth (DP field)
dp_matrix <- extract.gt(vcf, element = "DP", as.numeric = TRUE)

# Summary statistics
summary(as.vector(dp_matrix))

Quick Visualization

Let’s make a simple plot of read depth distribution:

# Flatten the matrix and plot
dp_values <- as.vector(dp_matrix)
dp_values <- dp_values[!is.na(dp_values)]

hist(dp_values, 
     breaks = 50, 
     main = "Read Depth Distribution",
     xlab = "Depth",
     col = "steelblue")

Example depth histogram

Summary

In this tutorial, you learned how to:

• ✅ Install and load required packages
• ✅ Read VCF files into R
• ✅ Inspect basic properties of variant data
• ✅ Extract genotype and depth matrices
• ✅ Create a simple visualization

Next Steps

Continue to Tutorial 2: Working with Data to learn about filtering and manipulating variant data.

Exercises

Try these on your own:

1. Load your own VCF file and check how many samples and variants it contains
2. Extract the GQ (genotype quality) field and plot its distribution
3. Find the chromosome with the most variants

TipExercise 3 Solution

# Count variants per chromosome
table(vcf@fix[, "CHROM"]) |> 
  sort(decreasing = TRUE) |> 
  head()