Python for Genomics

A comprehensive Python toolkit for genomic data analysis and bioinformatics, created as part of the Coursera "Python for Genomic Data Science" course.

📚 Overview

This repository provides everything you need to learn Python for genomics:

• Tutorials - Learn Python fundamentals with genomics examples
• Modules - Reusable tools for DNA/RNA analysis
• Examples - Complete genomics workflows
• Practice - Exercises to test your skills
• Quick Reference - Fast lookup cheat sheet

🗂️ Repository Structure

python-for-genomics/
├── tutorials/            # 📗 Python fundamentals (START HERE!)
│   ├── 01_strings_and_dna.py
│   ├── 02_lists_and_sequences.py
│   ├── 03_dictionaries_and_codons.py
│   ├── 04_conditionals_in_genomics.py
│   ├── 05_loops_and_iteration.py
│   └── 06_file_io_genomics.py
├── modules/              # Core reusable modules
│   ├── dna_tools.py
│   ├── sequence_analysis.py
│   └── file_parsers.py
├── examples/             # Complete genomics workflows
│   ├── 00_basic_operations.py
│   ├── 01_my_first_analysis.py
│   ├── 02_interactive_analyzer.py
│   ├── 03_compare_sequences.py
│   ├── 04_gc_content_analysis.py
│   ├── 05_orf_finder.py
│   └── 06_fasta_file_operations.py
├── practice/             # 💪 Test your skills
│   ├── exercises.py
│   └── solutions.py
├── data/                 # Sample data files
├── QUICK_REFERENCE.md    # 📖 Cheat sheet
└── README.md

🚀 Getting Started

For Complete Beginners

Start with the tutorials! They teach Python fundamentals using genomics examples.

cd tutorials
python3 01_strings_and_dna.py

Work through tutorials 01-06 in order. Each tutorial is a complete, runnable script with explanations.

For Those Who Know Python

Jump straight to the examples to see complete genomics workflows:

cd examples
python3 00_basic_operations.py

📚 Learning Path

Step 1: Fundamentals (tutorials/)

Learn Python basics with genomics context:

1. Strings and DNA - String operations for sequences
2. Lists - Working with multiple sequences
3. Dictionaries - Genetic code and mappings
4. Conditionals - Sequence validation
5. Loops - Iterating through data
6. File I/O - Reading and writing FASTA files

Each tutorial includes:

• Clear explanations
• Code examples
• Genomics applications
• Try-it-yourself exercises

Step 2: Examples (examples/)

See complete workflows in action:

📗 Beginner

• 00: Basic DNA operations
• 01: Your first analysis
• 02: Interactive analyzer
• 03: Compare sequences

📙 Intermediate

• 04: GC content analysis
• 05: ORF finding

📕 Advanced

• 06: Complete FASTA operations

Step 3: Practice (practice/)

Test your skills with exercises:

• 12 exercises covering all concepts
• Solutions provided
• Real genomics problems

Step 4: Reference (QUICK_REFERENCE.md)

Fast lookup for Python syntax and common patterns.

💻 Installation

git clone https://github.com/amritasule/python-for-genomics.git
cd python-for-genomics

No external dependencies! Uses only Python standard library.

📖 Module Documentation

dna_tools.py - Core DNA Functions

Function	Description
validate_dna(seq)	Check if valid DNA
gc_content(seq)	Calculate GC percentage
complement(seq)	Get DNA complement
reverse_complement(seq)	Get reverse complement
transcribe(dna)	Convert DNA to RNA
translate(dna)	Translate to protein
count_nucleotides(seq)	Count each base
has_start_codon(seq)	Check for ATG
has_stop_codon(seq)	Check for stop codons

sequence_analysis.py - Advanced Analysis

Function	Description
find_motif(seq, motif)	Find pattern occurrences
find_orfs(seq)	Find open reading frames
calculate_melting_temp(seq)	Calculate Tm
hamming_distance(seq1, seq2)	Calculate differences
gc_content_window(seq, size)	Sliding window GC
find_repeats(seq, min_len)	Find repeated sequences

file_parsers.py - File I/O

Function	Description
read_fasta(filename)	Read FASTA file
write_fasta(seqs, filename)	Write FASTA file
read_fastq(filename)	Read FASTQ file
write_fastq(records, filename)	Write FASTQ file

🎯 Quick Start Examples

Analyze a sequence

import sys
sys.path.append('modules')
import dna_tools

sequence = "ATGCGCTAGGGTAA"
print(f"GC Content: {dna_tools.gc_content(sequence):.2f}%")
print(f"Protein: {dna_tools.translate(sequence)}")

Read FASTA file

import file_parsers

sequences = file_parsers.read_fasta('data/sample_sequences.fasta')
for header, seq in sequences.items():
    print(f"{header}: {len(seq)} bp")

Find ORFs

import sequence_analysis

orfs = sequence_analysis.find_orfs("ATGCGCGCGTAGGGTAA")
for start, end, orf in orfs:
    print(f"ORF: {orf}")

✨ Features

• ✅ No external dependencies (pure Python)
• ✅ Complete tutorials with genomics examples
• ✅ Runnable code examples
• ✅ Practice exercises with solutions
• ✅ FASTA/FASTQ file support
• ✅ Complete genetic code table
• ✅ ORF finding in all reading frames
• ✅ Quick reference guide
• ✅ Portfolio-ready quality

🎓 Topics Covered

Python Fundamentals

• Variables and data types
• Strings, lists, dictionaries
• If/elif/else statements
• For and while loops
• Functions and modules
• File I/O operations

Bioinformatics Concepts

• DNA sequence manipulation
• GC content calculation
• Sequence complement
• Transcription and translation
• ORF finding
• FASTA file parsing
• Sequence comparison
• Pattern matching

🔬 Use Cases

• Education - Learn Python and bioinformatics
• Research - Quick sequence analysis
• Pipelines - Building blocks for workflows
• Prototyping - Test ideas before scaling

📝 Examples of What You Can Do

After completing the tutorials, you'll be able to:

✓ Analyze DNA sequences (GC content, composition, etc.)
✓ Read and write FASTA files
✓ Find open reading frames (ORFs)
✓ Translate DNA to protein
✓ Compare sequences
✓ Filter sequences by criteria
✓ Search for patterns and motifs
✓ Build analysis pipelines

🤝 Contributing

Contributions welcome! Feel free to:

• Add new features
• Improve documentation
• Report bugs
• Suggest enhancements

📞 Resources

• tutorials/README.md - Detailed tutorial guide
• practice/README.md - Exercise instructions
• QUICK_REFERENCE.md - Python syntax cheat sheet
• examples/ - Working code examples

🙏 Acknowledgments

Created as part of the Coursera course "Python for Genomic Data Science" offered by Johns Hopkins University.

📝 License

MIT License - feel free to use this code for learning and research.

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Ready to start? Head to tutorials/ and begin with 01_strings_and_dna.py!

Happy coding! 🧬