Mini-Projet 3 — GC Content Analysis

Period : 2025-11-08 → 2025-11-15
Approx. duration : 8 days
Status : Completed ✅

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Overview

This mini-project implements a complete and personal pipeline for analyzing DNA sequences stored in FASTA format.
The goal is to read multiple sequences, compute their GC percentage, and produce:

• a CSV file summarizing the results
• a bar chart visualizing GC% for each sequence

Through this project, I practiced essential bioinformatics skills:

• writing a custom FASTA parser
• manipulating strings and dictionaries
• implementing a biological calculation (GC%)
• exporting structured data (CSV)
• creating scientific graphics with Matplotlib
• organizing clean and reusable code in functions

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Features

1. Custom FASTA Reader

• Supports multi-line sequences
• Handles UTF-8 BOM automatically
• Skips empty lines
• Returns a dictionary like: {"seq1": "ATGC...", "seq2": "GGGCCC...", ...}

2. GC Content Calculation

The GC% is computed using the formula: GC% = (G + C) / (A + T + G + C) × 100

3. CSV Export

Creates a file containing:

• Sequence ID
• Length
• GC percentage

File generated:
results_gc_content.csv

4. Bar Chart

Generates a PNG figure showing GC% per sequence.
File generated:
gc_content_bar.png

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How to Run

python -m src_perso.test

Outputs:

• GC% printed in the terminal
• results_gc_content.csv generated
• gc_content_bar.png saved

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Tools Used

• Python 3
• Matplotlib
• CSV module
• Visual Studio Code

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Résultats obtenus / Results

• File: results_gc_content.csv → contains the GC% for each sequence
• Chart: results/gc_bar.png → bar plot of GC content

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Notes personnelles / Personal Notes

Ce projet m’a aidée à mieux comprendre :

• la lecture et la structure d’un fichier FASTA
• la logique d’une fonction bioinformatique (gc_percent)
• le flux complet : lecture → calcul → sauvegarde → visualisation
• l’organisation d’un projet Python en modules (src/)

Projet réalisé dans le cadre de mon apprentissage guidé de la bioinformatique, en cherchant à comprendre chaque étape pas à pas.
Project completed as part of my guided learning journey in bioinformatics, aiming to understand each step step-by-step.

Notes

Code inspired and adapted from learning sessions with AI and Coursera.
All scripts are fully understood, rewritten by me, tested, and commented by me.

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Structure du dossier / Folder Structure

GCContent/
│
├── data/
│ ├── data.fasta # Main FASTA file used for GC analysis
│ └── toy.fasta # Small FASTA file used for testing
│
├── results/
│ ├── gc_bar.png # Plot generated from earlier versions
│ ├── gc_PERSO_bar.png # Plot generated with my own final code
│ ├── results_gc_content.csv # CSV from earlier versions
│ └── results_gc_content_PERSO.csv # CSV generated with my own code
│
├── src_learning/ # (Optional) Exploratory / learning scripts
│
├── src_perso/
│ ├── init.py # Marks this directory as a Python module
│ ├── gc_content_PERSO.py # Final personal implementation
│ └── test.py # Main script that runs the full analysis pipeline
│
└── README.md # Project documentation (English)