Book Image

Bioinformatics with Python Cookbook - Third Edition

By : Tiago Antao
Book Image

Bioinformatics with Python Cookbook - Third Edition

By: Tiago Antao

Overview of this book

Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data, and this book will show you how to manage these tasks using Python. This updated third edition of the Bioinformatics with Python Cookbook begins with a quick overview of the various tools and libraries in the Python ecosystem that will help you convert, analyze, and visualize biological datasets. Next, you'll cover key techniques for next-generation sequencing, single-cell analysis, genomics, metagenomics, population genetics, phylogenetics, and proteomics with the help of real-world examples. You'll learn how to work with important pipeline systems, such as Galaxy servers and Snakemake, and understand the various modules in Python for functional and asynchronous programming. This book will also help you explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks, including Dask and Spark. In addition to this, you’ll explore the application of machine learning algorithms in bioinformatics. By the end of this bioinformatics Python book, you'll be equipped with the knowledge you need to implement the latest programming techniques and frameworks, empowering you to deal with bioinformatics data on every scale.
Table of Contents (15 chapters)

Extracting more information from a PDB file

Here, we will continue our exploration of the record structure produced by Bio.PDB from PDB files.

Getting ready

For general information about the PDB models that we are using, refer to the previous recipe.

You can find this content in the Chapter08/ Notebook file.

How to do it...

We’ll get started, using the following steps:

  1. First, let’s retrieve 1TUP, as follows:
    from Bio import PDB
    repository = PDB.PDBList()
    parser = PDB.PDBParser()
    repository.retrieve_pdb_file('1TUP', pdir='.', file_format='pdb') p53_1tup = parser.get_structure('P 53', 'pdb1tup.ent')
  2. Then, extract some atom-related statistics:
    from collections import defaultdict
    atom_cnt = defaultdict(int)
    atom_chain = defaultdict(int)
    atom_res_types = defaultdict(int)
    for atom in p53_1tup.get_atoms():
        my_residue = atom.parent
        my_chain = my_residue...