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)

Parsing mmCIF files using Biopython

The mmCIF file format is probably the future. Biopython doesn’t have full functionality to work with it yet, but we will take a look at what currently exists.

Getting ready

As Bio.PDB is not able to automatically download mmCIF files, you need to get your protein file and rename it to 1tup.cif. This can be found at under 1TUP.cif.

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

How to do it...

Take a look at the following steps:

  1. Let’s parse the file. We just use the MMCIF parser instead of the PDB parser:
    from Bio import PDB
    parser = PDB.MMCIFParser()
    p53_1tup = parser.get_structure('P53', '1tup.cif')
  2. Let’s inspect the following chains:
    def describe_model(name, pdb):
        for model in p53_1tup: