Bioinformatics with Python Cookbook: Solve advanced computational biology problems and build production pipelines with Python and AI tools , Fourth Edition

Shane Brubaker

€18.99 per month

Paperback Dec 2025 618 pages 4th Edition

Shane Brubaker

€18.99 per month

Paperback Dec 2025 618 pages 4th Edition

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Bioinformatics with Python Cookbook

Installing the required software with Docker

Docker is the most widely used framework for implementing operating system-level virtualization. This technology allows you to have an independent container: a layer that is lighter than a virtual machine but still allows you to compartmentalize software. This mostly isolates all processes, making it feel like each container is a virtual machine. Containers will be discussed in more detail in Chapter 14, Cloud Basics.Docker works quite well at both extremes of the development spectrum: it’s an expedient way to set up the content of this book for learning purposes and could become your platform of choice for deploying your applications in complex environments.Conda and Docker are key tools to help maintain software compatibility and reproducibility across different systems and libraries. We’ll discuss reproducibility more in Chapter 15, Workflow Systems.

Note

This recipe is an alternative to the previous recipe. Normally, if...

Introduction to Jupyter Notebook

All of our work will be developed inside Jupyter Notebook. Jupyter has become the de facto standard for writing interactive data analysis scripts. Unfortunately, the default format for Jupyter notebooks is based on JSON. JSON is JavaScript Object Notation (https://www.json.org/json-en.html). This format is difficult to read, difficult to compare, and needs exporting to be fed into a normal Python interpreter. To obviate that problem, we will extend Jupyter with jupytext (https://jupytext.readthedocs.io/), which allows us to save Jupyter notebooks as normal Python programs. We will start with an overview of Jupyter Notebook, and then look into jupytext. Recall that we installed Jupyter Notebook in the first recipe of this chapter, when we installed the jupyterlab package using conda.

How to do it…

To run Jupyter, on the Terminal, type the following:

jupyter notebook

This will open the Jupyter browser, and you will see a home page that looks...

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Key benefits

Perform sequence analysis at primary, secondary, and tertiary levels using Python libraries
Solve real-world problems in the fields of phylogenetics, protein design, and annotation
Use language models and other AI techniques to work with multimodal bioinformatics data
Purchase of the print or Kindle book includes a free PDF eBook

Description

If you've ever felt overwhelmed by the vast number of Python tools available for bioinformatics, you're not alone. The Bioinformatics with Python Cookbook is a recipe-based guide that explores practical approaches for solving classic bioinformatics challenges, showing you which Python packages work best for each task. You’ll start with the essential Python libraries for data science and bioinformatics, then move through key workflows in sequencing analysis, quality control, alignment, and variant calling. Along the way, you’ll pick up modern coding practices, explore recent advances in bioinformatics research, and gain hands-on experience with libraries such as NumPy, pandas, and sci-kit learn. This book walks you through core bioinformatics tasks such as phylogenetic analysis and population genomics while familiarizing you with the wealth of modern public bioinformatics databases. You’ll learn cloud computing approaches used by researchers, set up workflow orchestration systems for controlling bioinformatics pipelines, and see how AI and the use of large language models (LLMs) are reshaping the field–right down to designing proteins and DNA. By the end of this book, you’ll be ready to apply Python for real bioinformatics work and launch bioinformatics pipelines for your research.

Who is this book for?

This book is for early- to mid-level practitioners in bioinformatics, data science, and software engineering who want to improve their skills and apply practical solutions to real-world problems. You should have a basic understanding of biology, including DNA, proteins, and cell structure, as well as Python programming and software engineering techniques. While prior exposure to machine learning with Python is not essential, experience with a cloud computing platform (AWS, GCP, or Azure) will be helpful.