Visualizing biological data is a critical component in bioinformatics. It helps in understanding the data, conveying information, and deriving insights. Julia, with its high-performance and extensive library support, offers great tools for visualizing biological data. In this blog post, we will explore how to use Julia for visualizing various types of biological data.

Getting Started

Before we dive into visualizations, let’s make sure Julia is installed on your system. Visit the JuliaLang official website to download and install Julia. Once installed, open the Julia REPL and install the necessary packages.

For this tutorial, we will use the Plots.jl package for plotting, and BioSequences.jl for handling biological sequences:

using Pkg

Visualizing Sequence Data

Nucleotide Composition

One of the basic visualizations in bioinformatics is analyzing the nucleotide composition of DNA sequences. This can be visualized using bar plots.

using Plots
using BioSequences

sequence = dna"AGCTAGCTAAGCTT"

# Count nucleotides
nucleotide_counts = countmap(sequence)

# Create a bar plot
bar(["A", "C", "G", "T"], [nucleotide_counts[n] for n in dna"A", dna"C", dna"G", dna"T"], xlabel="Nucleotide", ylabel="Count", title="Nucleotide Composition")

Visualizing Expression Data

Expression data, usually represented in a matrix where rows are genes and columns are samples, can be visualized using heatmaps.

using Random

# Generate random expression data
expression_data = rand(100, 5)

# Create a heatmap
heatmap(expression_data, xlabel="Samples", ylabel="Genes", title="Gene Expression")

Visualizing Phylogenetic Trees

Phylogenetic trees represent evolutionary relationships among biological entities. Although Plots.jl doesn’t have specialized support for phylogenetic trees, you can use basic plotting functions to create them.

Visualizing Protein Structures

Visualizing protein structures is essential in understanding their function. While Julia’s plotting libraries don’t specialize in 3D protein structures, you can use the RCall package to call R and utilize its specialized libraries for protein visualization.

using RCall

# Example: Load and visualize a protein structure using bio3d R package
pdb <- read.pdb("4q21")

Tips for Effective Visualizations

  1. Keep It Simple: Avoid unnecessary complexity in your visualizations.
  2. Use Labels and Legends: Make sure that your visualizations are self-explanatory.
  3. Pay Attention to Colors: Use color schemes that are easily distinguishable and consider color blindness.


Julia offers a wide range of tools for visualizing biological data. From sequence data to expression matrices, the flexibility and performance of Julia make it an excellent choice for bioinformatics. Whether you’re a biologist, a data scientist, or someone with an interest in the field, mastering data visualization in Julia is a valuable skill. Dive into the rich ecosystem of Julia, and unlock the stories hidden in your biological data!