Member-only story
In the rapidly evolving field of computational biology, Python has emerged as a powerful tool for researchers and scientists. Its versatility, ease of use, and extensive library ecosystem make it an ideal choice for tackling complex biological data analysis and modeling tasks.
In this article, we’ll explore the applications of Python in computational biology and provide practical code examples to help you get started.
Python’s popularity in the scientific community is largely due to its extensive ecosystem of libraries and frameworks specifically designed for data analysis, visualization, and modeling. Libraries like NumPy, SciPy, Pandas, Matplotlib, and Biopython have become indispensable tools in the computational biologist’s toolkit.
Let’s start by importing some essential libraries:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from Bio import SeqIOOne of the most common tasks in computational biology is sequence analysis. Biopython, a Python library for bioinformatics, provides a range of tools for working with biological sequences. Here’s an example of reading and parsing a FASTA file:
