🔬 GC Content Analysis of a Genome

🧬 Overview

This project calculates and visualizes the GC content across a genome using a sliding window approach. GC content is an important genomic feature that influences DNA structure, gene expression, and evolutionary adaptations.

By analyzing a genomic FASTA file (e.g., from Escherichia coli), the script computes GC percentage in fixed-length windows (e.g., 1000 base pairs) and plots the variation across the genome. This helps identify GC-rich and GC-poor regions, which may correspond to coding areas, structural domains, or regulatory elements.

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📌 Key Features

• Parses FASTA genome files using Biopython
• Calculates GC content using a sliding window (default: 1000 bp)
• Exports results to a CSV file
• Generates a plot showing GC variation across the genome
• Highlights biologically significant regions with high/low GC content

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🛠️ Technologies & Libraries Used

• Python 3.x
• Biopython – for FASTA file parsing
• Matplotlib – for plotting
• CSV – for storing tabular GC content output

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📦 Installation & Setup

🔁 Step 1: Clone the Repository

git clone https://github.com/jahnavigbedre/gc_content.git
cd gc_content

💾 Step 2: Install Required Packages

We recommend using a virtual environment (optional):

# Create virtual environment
python -m venv env

# Activate it
# On Linux/macOS
source env/bin/activate
# On Windows
env\Scripts\activate

# Install required packages
pip install biopython matplotlib

📥 Step 3: Add a Genome File

Download a genome FASTA file from:

• NCBI Genome Database
• Ensembl

Place the file in the project directory, for example: Ecoli.fna

▶️ Step 4: Run the Script

Edit the FASTA file path if needed in the script, then run:

python gc_content.py

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📜 Script: gc_content.py

from Bio import SeqIO
import matplotlib.pyplot as plt

# ========== CONFIGURATION ==========
fasta_file = "Ecoli.fna"       # Change this to your FASTA filename
window_size = 1000             # Size of the sliding window
output_csv = "gc_content.csv"  # Output CSV file
# ===================================

def calculate_gc(seq):
    """Calculate GC content of a DNA sequence."""
    gc_count = seq.count("G") + seq.count("C")
    return (gc_count / len(seq)) * 100 if len(seq) > 0 else 0

# Process each sequence in the FASTA file
for record in SeqIO.parse(fasta_file, "fasta"):
    sequence = str(record.seq).upper()
    length = len(sequence)

    print("========= GC CONTENT ANALYSIS =========")
    print(f"Genome Name: {record.id}")
    print(f"Genome Length: {length} bp")
    print(f"Window Size: {window_size} bp\n")

    gc_values = []
    positions = []

    for i in range(0, length, window_size):
        window_seq = sequence[i:i+window_size]
        gc = calculate_gc(window_seq)
        gc_values.append(gc)
        positions.append(i)

    # Save to CSV
    with open(output_csv, "w") as out:
        out.write(f"# Genome: {record.id}, Window Size: {window_size} bp\n")
        out.write("Start_Position,gc_content\n")
        for pos, gc in zip(positions, gc_values):
            out.write(f"{pos},{gc:.2f}\n")

    # Plot
    plt.figure(figsize=(12, 5))
    plt.plot(positions, gc_values, linewidth=1.2, color='green')
    plt.xlabel("Genome Position (bp)")
    plt.ylabel("GC Content (%)")
    plt.title(f"GC Content Across {record.id} (Window = {window_size} bp)")
    plt.grid(True)
    plt.tight_layout()
    plt.savefig("gc_plot.png")  # Save the plot as PNG
    plt.show()

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📊 Example Output

🖥️ Terminal Output:

========= GC CONTENT ANALYSIS =========
Genome Name: NC_000913.3
Genome Length: 4641652 bp
Window Size: 1000 bp

📁 CSV Output (gc_content.csv):

Start_Position,gc_content
0,51.60
1000,52.40
2000,48.70
...

• Start_Position: The starting base pair of each 1000 bp window
• gc_content: The GC percentage in that window

📈 Output Plot:

🔍 Interpretation:

• X-axis: Genome position (in base pairs)
• Y-axis: GC content (%) in each 1000 bp window
• The plot shows GC content variation across the genome
• Peaks represent GC-rich regions (>60%), valleys indicate GC-poor regions (<40%)
• These variations may relate to gene density, functional domains, or repeats

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📂 Project Structure

gc_content/
├── Ecoli.fna                # Input FASTA genome file
├── gc_content.py            # Main Python script
├── gc_content.csv           # Output GC% per 1000 bp window
├── gc_plot.png              # GC content plot image
└── README.md                # Project documentation

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🧠 Learning Outcomes

• Learn to parse biological FASTA files using Biopython
• Apply sliding window statistical analysis to real genomic data
• Export and handle genomic data in CSV format
• Visualize biological patterns using Matplotlib
• Understand genome structure through GC content variation

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👩‍💻 Author

Jahnavi G Bedre
M.Sc. Bioinformatics – Personal Genomics Project

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🔗 GitHub Repository

👉 https://github.com/jahnavigbedre/gc_content