🧬 DNA Sequence Analysis – Final Exam Project

Course: Python for Genomic Data Science Institution: Johns Hopkins University & Coursera

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📖 Project Description

This repository contains a Python-based analytical pipeline developed for the final exam of a bioinformatics course focused on genomic data processing using Python. The program parses a multi-FASTA file, performs key biological analyses such as open reading frame (ORF) detection and repeat identification, and provides structured responses to biological questions.

This activity demonstrates a comprehensive understanding of:

• Nucleotide sequence parsing
• ORF logic and biological reading frames
• Motif/repeat detection using sliding windows
• Efficient data structures and control flow in Python

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📊 Project Objectives

The program answers 10 exam-style questions on genomic features derived from real DNA sequences in a multi-FASTA file (dna2.fasta). These include:

1. Total number of sequences
2. Longest and shortest sequence lengths
3. ORF detection across all three forward reading frames
4. Repeat analysis for motifs of various lengths
5. Specific query-based searches using identifiers

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✅ Final Exam Answers

#	Topic	Answer
1	Total number of FASTA records	18
2	Longest sequence length	4894
3	Shortest sequence length	115
4	Longest ORF in reading frame 2	1458 bp
5	Start position of the longest ORF in reading frame 3	636
6	Longest ORF across all forward reading frames	2394 bp
7	Longest ORF in sequence `gi	142022655	gb	EQ086233.1	16`	1644 bp
8	Most frequent repeat (6-mer) count	194
9	Number of distinct 12-mer sequences occurring the maximum number of times	5
10	Most frequent 7-mer among listed options	GCGCGCA

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🛠 Technologies & Skills Demonstrated

🔬 Bioinformatics Skills

• Parsing and processing FASTA-formatted sequence data
• Open Reading Frame (ORF) detection using biological reading frames
• Motif discovery and repeat quantification
• Biological indexing and start/stop codon handling

💻 Programming Skills

• Python scripting from scratch
• Use of collections, pathlib, and string manipulation
• Sliding window techniques for repeat detection
• 1-based biological indexing vs 0-based Python indexing
• Clean modular code structure with reusable functions

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📁 Project Structure

📦 DNA-Sequence-Analysis
├── dna2.fasta            # Input FASTA file (exam dataset)
├── dna_analysis.py       # Main script with all functions and logic
└── README.md             # Project documentation

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🚀 How to Run

Requirements

• Python 3.7 or higher
• No external libraries required

Usage

python dna_analysis.py

Make sure dna2.fasta is located in the same directory as the script.

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🧠 Learning Outcomes

This project represents a capstone for understanding core Python bioinformatics principles, including:

• Translating biological requirements into algorithmic logic
• Efficiently traversing DNA sequences for meaningful features
• Handling biological sequence complexity through scripting
• Applying foundational programming tools in genomic contexts

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📜 Course Credit

This project was completed as part of:

📘 Python for Genomic Data Science

• 💼 Offered by: Johns Hopkins University

• 🧑‍🏫 Instructor: Dr. Jeff Leek

• 🧠 Skills Gained:

  • Python Programming & Scripting
  • Bioinformatics Workflows
  • Data Structures & File Parsing
  • ORF & Motif Analysis
  • Jupyter Notebooks & Scripting Languages

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📝 License

This project is academic in nature and was developed for educational purposes. Please use it for reference or learning with proper attribution.

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🙌 Acknowledgments

Special thanks to the instructors of Johns Hopkins University and the Coursera Team for creating a structured and highly practical course in Python for Genomic Data Science.