🧬 BioSequence Visualizer

A web-based DNA sequence analysis tool built with Python and Streamlit. Upload a FASTA file and get an instant, comprehensive bioinformatics report — no installation required.

🔗 Live Demo: biovisualizer.streamlit.app

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Features

Section	What it does
📊 Sequence Statistics	Sequence length, GC content, melting temperature (Tm), AT/GC ratio
🧪 GC Content Meter	Color-coded visual bar — red (low), green (normal), blue (high)
🔢 Nucleotide Counts	Individual A, T, G, C counts with purine/pyrimidine totals
📈 Frequency Chart	Bar chart of nucleotide distribution
📉 GC Along Sequence	Sliding window line graph showing GC% across the sequence
🔍 Motif Detection	Search for any pattern (supports regex) and get all match positions
🧫 Annotated Features	Auto-detects start codons (ATG) and stop codons (TAA, TAG, TGA)
🔗 Strand Information	Original, complement, and reverse complement with one-click copy
⬇️ Download	Export all three strands as a .txt file

How to Use

1. Go to biovisualizer.streamlit.app
2. Upload a .fasta, .fa, or .txt file containing a valid DNA sequence
3. Click Analyze
4. View your full analysis report

Sample FASTA format

>gene_name
ATGCGATCGATCGTAGCTAGCATGCATGCATGCGCTAGCTAGC

Run Locally

Clone the repo

git clone https://github.com/YOUR_USERNAME/biosequence-visualizer.git
cd biosequence-visualizer

Install dependencies

pip install -r requirements.txt

Run the app

streamlit run app.py

Project Structure

biosequence-visualizer/
├── app.py            # Streamlit UI and layout
├── visualize.py      # All bioinformatics logic and functions
├── requirements.txt  # Python dependencies
└── README.md

Logic separation

• visualize.py handles all biological computation — validation, nucleotide counting, GC content, melting temperature, complement generation, motif detection, sliding window analysis, and codon annotation
• app.py handles only the UI — importing functions from visualize.py and rendering results

Science Behind the Features

GC Content — percentage of guanine and cytosine bases. Values between 40–60% are typical for most organisms. Higher GC content means more thermally stable DNA.

Melting Temperature (Tm) — calculated using the Wallace Rule: Tm = 2(A+T) + 4(G+C). Commonly used in primer design and PCR experiments.

Sliding Window GC — GC% calculated in overlapping windows of 10 bases across the sequence. Used to identify GC-rich or AT-rich regions in a genome.

Motif Detection — uses Python's re.finditer() to find all occurrences of a pattern including overlapping matches. Supports full regex syntax (e.g. AT[GC]).

Codon Annotation — detects start codon ATG and stop codons TAA, TAG, TGA and displays their positions in a sortable table.

Tech Stack

• Python 3
• Streamlit — web interface
• Matplotlib — bar chart and sliding window line chart
• Pandas — annotation features table
• re — regex-based motif detection

Built By

Joyeeta Sarkar Puja LinkedIn · GitHub