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)

Comparing sequences

Here, we will compare the sequences we aligned in the previous recipe. We will perform gene-wide and genome-wide comparisons.

Getting ready

We will use DendroPy and will require the results from the previous two recipes. As usual, this information is available in the corresponding notebook at Chapter07/Comparison.py.

How to do it...

Take a look at the following steps:

  1. Let’s start analyzing the gene data. For simplicity, we will only use data from two other species of the genus Ebola virus that are available in the extended dataset, that is, the Reston virus (RESTV) and the Sudan virus (SUDV):
    import os
    from collections import OrderedDict
    import dendropy
    from dendropy.calculate import popgenstat
    genes_species = OrderedDict()
    my_species = ['RESTV', 'SUDV']
    my_genes = ['NP', 'L', 'VP35', 'VP40']
    for name in my_genes:
        gene_name = name.split('.')[0...