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)

Python and the Surrounding Software Ecology

We will start by installing the basic software that is required for most of this book. This will include the Python distribution, some fundamental Python libraries, and external bioinformatics software. Here, we will also look at the world outside of Python. In bioinformatics and big data, R is also a major player; therefore, you will learn how to interact with it via rpy2, which is a Python/R bridge. Additionally, we will explore the advantages that the IPython framework (via Jupyter Lab) can give us in order to efficiently interface with R. Given that source management with Git and GitHub is pervasive, we will make sure that our setup plays well with them. This chapter will set the stage for all of the computational biologies that we will perform in the remainder of this book.

As different users have different requirements, we will cover two different approaches for installing the software. One approach is using the Anaconda Python ...