DNA

Background

DNA, the carrier of genetic information, has been a cornerstone in forensic science for decades. This project demonstrates how DNA profiling works by identifying to whom a given DNA sequence belongs using Short Tandem Repeats (STRs).

STRs are short sequences of DNA bases that repeat consecutively at specific locations in a genome. The number of repeats varies among individuals, providing a unique identifier when analyzed. By using multiple STRs, the likelihood of accurately identifying a match increases significantly.

Features

• Parses a CSV database containing individuals' STR counts.
• Reads a DNA sequence from a text file.
• Computes the longest run of consecutive STR repeats in the DNA sequence.
• Matches the STR counts against a database to identify the individual or determine if no match exists.

Usage

Run the program as follows:

python3 dna.py <database.csv> <sequence.txt>

Examples:

$ python3 dna.py databases/small.csv sequences/1.txt
Bob

$ python3 dna.py databases/small.csv sequences/2.txt
No match

$ python3 dna.py databases/large.csv sequences/5.txt
Lavender

Error Handling

If the incorrect number of arguments is provided, the program will display an error message:

$ python dna.py
Usage: python dna.py <database.csv> <sequence.txt>

Project Structure

|── dna/
│   ├── databases/
│   │   ├── small.csv
│   │   └── large.csv
│   ├── dna.py
│   └── sequences/
│       ├── 1.txt
│       ├── 2.txt
│       ├── ...
│       └── 20.txt
└── README.md

Implementation Details

1. Input:

   • A CSV file containing individuals' STR counts.
   • A text file containing a DNA sequence.

2. Output:

   • The name of the individual whose STR counts match the DNA sequence.
   • "No match" if no individual matches the DNA sequence.

3. Steps:

   • Parse the CSV file to extract STRs and their counts.
   • Analyze the DNA sequence to calculate the longest consecutive repeats for each STR.
   • Compare the computed STR counts against the database.
   • Print the matching individual's name or "No match."

Example Data

Database (CSV)

name,AGAT,AATG,TATC
Alice,28,42,14
Bob,17,22,19
Charlie,36,18,25

DNA Sequence (Text)

AAGATAGATAGATAGATAATGTATC

Sample Output

$ python dna.py databases/small.csv sequences/4.txt
Alice

Development Notes

• The program leverages Python's csv module for handling CSV files and efficient data processing.
• String slicing is used to identify and count STR sequences within the DNA string.
• A dictionary is used to store STR counts for easy comparison with the database.

Requirements

• Python 3.x

Authors

Developed by Shahir Ahmed.

License

This project is licensed under the MIT License.