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)

Aligning genetic and genomic data

Before we can perform any phylogenetic analysis, we need to align our genetic and genomic data. Here, we will use MAFFT ( to perform the genome analysis. The gene analysis will be performed using MUSCLE (

Getting ready

To perform the genomic alignment, you will need to install MAFFT. Additionally, to perform the genic alignment, MUSCLE will be used. Also, we will use trimAl ( to remove spurious sequences and poorly aligned regions in an automated manner. All packages are available from Bioconda:

conda install –c bioconda mafft trimal muscle=3.8

As usual, this information is available in the corresponding Jupyter Notebook file at Chapter07/ You will need to run the previous notebook beforehand, as it will generate the files that are required here. In this chapter, we will use Biopython.

How to do it...

Take a look...