21 min to read

Unlocking Python Modules

Modules will enable you to access millions and billions of useful functions.

Hello, bioinformatics enthusiasts!

Welcome back to the Python for Bioinformatics series. In today’s post, we’ll explore the concept of Python modules—a powerful feature that extends Python’s capabilities by providing pre-defined functions and methods. Whether you’re analyzing DNA sequences, simulating mutations, or working with complex datasets, understanding Python modules will elevate your coding skills.

What Are Python Modules?

A Python module is essentially a script file that contains Python code, such as functions, classes, or variables. Modules are designed to promote code reuse, making your scripts more organized and efficient.

Built-in vs. External Modules

Using Python Modules

Importing Modules

Instead of importing everything, you can selectively import functions, classes, or variables from a module.

from module_name import name1, name2, name3

To use a module in Python, you simply use the import statement:

import os
import math
from random import randint

For example, importing specific elements from the math module:

from math import pi, remainder
print(pi)  # Prints the value of π
print(remainder(10, 3))  # Computes the remainder of 10 divided by 3

Using Aliases for Imported Items

You can also rename a module for convenience:

from math import factorial as fac
import random as rnd

Importing Everything from a Module

To bring all items from a module into the namespace:

from math import *
print(sqrt(16))  # Prints 4.0

⚠️ Caution: Avoid using * in larger projects to prevent name clashes.

Using the sys Module to Access System Information

The sys module provides system-specific parameters and functions:

from sys import version
print(version)  
# Prints the Python version for example see below:
# '3.8.5 (default, Jul 28 2020, 12:59:40) \n[GCC 9.3.0]'

Or you can import the entire module:

import sys
print(sys.version)  # Same output as above

Random Number Generation with random

from random import randint

def random_base(RNAFlag=False):
    return ("UCGA" if RNAFlag else "TCGA")[randint(0, 3)]

print(random_base(True))  # Example output: U

def random_codon(RNAFlag=False):
    return random_base(RNAFlag) + random_base(RNAFlag) + random_base(RNAFlag)

print(random_codon(True))  # Example output: UCC

Random Base Replacement in DNA

Here are three approaches to randomly replace a base in a DNA sequence:

1. Using Temporary Variables

def replace_base_randomly_using_names(base_seq):
    position = randint(0, len(base_seq) - 1)
    base = base_seq[position]
    bases = "TCGA".replace(base, "")
    new_base = bases[randint(0, 2)]
    return base_seq[:position] + new_base + base_seq[position+1:]

print(replace_base_randomly_using_names("ATGCTA"))  # Example: 'ATGCCA'

2. Using a Single Expression

def replace_base_randomly_using_expression(base_seq):
    position = randint(0, len(base_seq) - 1)
    return (base_seq[:position] +
            "TCGA".replace(base_seq[position], "")[randint(0, 2)] +
            base_seq[position+1:])

print(replace_base_randomly_using_expression("ATGCTA"))  # Example: 'CTGCTA'

3. Simplified Version

def replace_base_randomly(base_seq):
    position = randint(0, len(base_seq) - 1)
    bases = "TCGA".replace(base_seq[position], "")
    return base_seq[:position] + bases[randint(0, 2)] + base_seq[position+1:]

print(replace_base_randomly("TTTTTTTTT"))  # Example: 'TTTTTTTCT'

Working with DNA Sequences Using BioPython

BioPython is a powerful library for biological computations. Let’s explore sequence manipulation:

from Bio.Seq import Seq

my_random_seq = Seq("ATGCTATATATATGCGCGCGCG")
print(my_random_seq)  # Original sequence: 'ATGCTATATATATGCGCGCGCG'
print(my_random_seq.complement())  # Complement: 'TACGATATATATACGCGCGCGC'
print(my_random_seq.reverse_complement())  # Reverse complement: 'CGCGCGCGCATATATATAGCAT'

Summary

In this blog post, we covered:

Modules are a core feature of Python, enabling modular and maintainable code. Experiment with these examples to deepen your understanding of Python’s module system!

Thank you for reading! In our next post, we’ll explore Importing .py Python Files, where we’ll learn how to create, import and use our own Python modules and packages. We’ll cover different import methods, package structures, and best practices for organizing larger Python projects.

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