GCanner

An Interactive Tool for Genome-Wide GC Analysis

Authors: Virginia Balouz and Carlos A. Buscaglia

GCanner is a Python-based interactive tool designed for the genome-wide analysis of GC composition. It enables users to identify and classify genomic compartments in Trypanosoma cruzi and other organisms based on their GC content.

🌍 Online Version: GCanner Web App

The user-friendly GCanner app is available here

💡 Quick Tutorial

1️⃣ Upload a FASTA File

• Click the 'Browse files' button to upload a FASTA file for analysis.

• The default page allows uploading files up to 200 MB.

• To modify this restriction, run the following command (available only for the locally-run option) :

  streamlit run GC-content-streamlit.py --server.maxUploadSize 2000

  this setting will allow the user to upload files up to 2GB

2️⃣ Modify Analysis Parameters

GC scanning and smoothing parameters are initially set to default values to determine core and disruptive compartments in T. cruzi but can be adjusted:

1. On the left panel, locate the 'General Settings' section.
2. Adjust the parameters according to your needs:
   • Minimum sequence length for analysis.
   • Window size.
   • Step size.
   • Number of points for Lowess smoothing.
   • Threshold cutoff for GC-low and GC-rich regions.
   • Number of GC content plots to display.

3️⃣ GOI-based search

To identify whether a Gene of Interest (GOI) is located in a core or disruptive compartment in addition to uploading the FASTA file (as described in 1️⃣) follow these steps:

1. Toggle the button for GOI-based analysis.
2. Upload a GFF file with annotated features. **Note:**Ensure the sequence names in both files match exactly for accurate mapping.
3. Enter the GOI keyword: In the box Search by gene of interest (keyword), specify the keyword associated with your GOI to search in the GFF file.

📊 Outputs

1️⃣ Table of Mapped Regions

This table contains six columns: an index column enumerating each row, the 'Sequence_name' column for the sequence's name, 'Start' and 'End' columns indicating the beginning and end of each region, and the 'Region type' and 'Region Length' columns, which specify whether the regions were classified as core or disruptive and their corresponding lengths. The table can be downloaded as a CSV file by clicking the 📥 in the top right corner.

2️⃣ Compartment Proportion Plot

This barplot summarizes the proportion of bases classified as core (green) or disruptive (pink). The total count of bases for each compartment (in Kb) and their proportion are displayed at the top of each column.

3️⃣ GC Content Plots

These plots display the GC content (blue lines), Lowess smoothing (red line), and the predicted regions shaded in green for core regions and pink for disruptive regions along the DNA sequence. Each plot corresponds to one sequence in the FASTA file.

3️⃣ Keyword Match Compartment Proportion Plot (for GOI-based searches)

A bar plot summarizing the frequency of GOI occurrences within each compartment. Matches at compartment boundaries will be categorized as "Edge".

📥 Downloading Results

By clicking the ‘Download Figures as CSV’ button at the bottom part of the screen, all figures seen in the page will be saved separately in a compressed file named ‘plots_svg.zip’ to the default folder.

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🚀 How to Run GCanner Locally

1. Download the script GC-content-streamlit.py and save it.

2. Open a terminal and navigate to the folder where the script is saved.

3. Run the following command:

   streamlit run GC-content-streamlit.py

The message 'You can now view your Streamlit app in your browser.' will be printed on the command line, and the application will open in a new browser tab.

📦 Dependencies

• Python 3.7+
• Biopython
• NumPy and math
• Pandas
• Matplotlib and Seaborn
• Statsmodels
• Streamlit
• Empfile, Zipfile, and OS modules for temporary file handling and exporting outputs

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✨ Enjoy using GCanner for your genome-wide GC analysis!🚀

📖 Please cite Balouz et al.(2025)