Bioinformatics Toolkit with Biopython

A Python-based bioinformatics toolkit demonstrating practical applications of Biopython for sequence analysis, biological file parsing, motif discovery, protein structure analysis, and access to public biological databases.

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Project Highlights

✔ DNA, RNA, and protein sequence analysis

✔ FASTA and GenBank parsing

✔ GC content calculation and visualization

✔ Pairwise sequence alignment

✔ Motif discovery and consensus sequence generation

✔ NCBI Entrez database integration

✔ Protein structure analysis using PDB files

✔ Unit testing with pytest

✔ Reproducible bioinformatics workflows

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Features

• DNA sequence analysis
• FASTA and GenBank parsing
• Protein sequence analysis
• GC content calculation and visualization
• Pairwise sequence alignment
• Motif detection and consensus sequences
• NCBI Entrez database access
• Protein structure parsing using PDB files
• Interactive Jupyter Notebook examples
• Unit tests with pytest

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Project Structure

bioinformatics-toolkit-biopython/
│
├── data/
├── docs/
├── examples/
├── images/
├── notebooks/
├── src/
├── tests/
├── README.md
├── requirements.txt
└── main.py

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Skills Demonstrated

Bioinformatics

• Sequence analysis
• Biological file parsing
• Protein sequence analysis
• Motif discovery
• Sequence alignment
• Protein structure analysis
• Biological database access

Python Development

• Object-oriented programming
• Modular software design
• Unit testing
• Data visualization
• Command-line applications

Libraries & Tools

• Biopython
• pytest
• Jupyter Notebook
• NCBI Entrez
• Protein Data Bank (PDB)

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Modules

Module	Description
sequence_analyzer.py	DNA sequence analysis
fasta_parser.py	FASTA and GenBank parsing
protein_analyzer.py	Protein sequence analysis
gc_plot.py	GC content visualization
alignment_tools.py	Pairwise sequence alignment
motif_tools.py	Motif analysis
entrez_tools.py	NCBI Entrez search and retrieval
pdb_tools.py	Protein structure parsing

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Installation

python -m venv .venv

# Linux / macOS
source .venv/bin/activate

# Windows
.venv\Scripts\activate

pip install -r requirements.txt

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Running the Application

python main.py

The menu-driven application allows users to perform sequence analysis, FASTA parsing, protein analysis, GC content plotting, sequence alignment, motif analysis, Entrez queries, and PDB structure parsing.

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Screenshots

DNA Sequence Analysis

Analysis of DNA sequences using Biopython Seq and SeqIO modules.

Sequence Alignment and Motif Analysis

Pairwise sequence alignment and motif consensus generation.

NCBI Entrez Search

Searching the PubMed database programmatically through Biopython's Entrez module.

Protein Structure Analysis

Parsing a real Protein Data Bank (PDB) structure and extracting structural statistics from human hemoglobin (PDB ID: 1A3N).

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Example Usage

DNA Analysis

from src.sequence_analyzer import analyze_dna

result = analyze_dna(
    "ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG"
)

print(result)

FASTA Parsing

from src.fasta_parser import parse_sequences

records = parse_sequences(
    "data/example.fasta",
    "fasta"
)

print(records)

Protein Analysis

from src.protein_analyzer import analyze_protein

print(analyze_protein("MAIVMGRWKGAR"))

GC Content Plot

from src.gc_plot import plot_gc_content

plot_gc_content(
    "data/example.fasta",
    "outputs/gc_content.png"
)

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Biopython Concepts

Seq

The Seq object represents DNA, RNA, or protein sequences and provides methods for common biological operations.

SeqRecord

SeqRecord stores biological sequences together with metadata such as identifiers, descriptions, annotations, and sequence information.

SeqIO

SeqIO enables reading and writing biological sequence formats including FASTA and GenBank.

FASTA vs GenBank

• FASTA contains sequence identifiers and sequence data.
• GenBank contains sequence data together with biological annotations and metadata.

Common Operations

• Complement
• Reverse Complement
• GC Content
• DNA → RNA Transcription
• RNA/DNA → Protein Translation
• Sequence Alignment
• Motif Discovery
• Protein Structure Parsing
• NCBI Database Searching

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Testing

Run all tests using:

pytest

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Future Improvements

• Increase unit test coverage
• Add multiple sequence alignment (MSA)
• Add advanced motif analysis
• Add interactive protein structure visualization
• Add GitHub Actions CI/CD workflow
• Add support for additional biological file formats

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Author

Agata Gabara

Incoming MSc Bioinformatics Student

Research Interests:

• Cancer Genomics
• Transcriptomics
• Computational Biology
• Bioinformatics Software Development
• Machine Learning for Genomics

GitHub: https://github.com/ag48665

LinkedIn: https://www.linkedin.com/in/agatha-gabara-06494a37/

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License

This project is intended for educational and learning purposes.