Bioinformatics with Python Cookbook - Fourth Edition: Solve advanced computational biology problems and build production pipelines with Python and AI (Paperback or Softback)

Enhance your bioinformatics toolbox with practical Python recipes, tips, and tricks for key tasks like aligning sequence data, calling variants, and building Infrastructure as Code

Key Features

• 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

Book 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.

What you will learn

• Process, analyze, and align sequencing data
• Call variants and interpret their biological meaning
• Use modern cloud infrastructure to launch bioinformatics workflows
• Ingest, clean, and transform data efficiently
• Explore how AI is shaping the future of bioinformatics
• Leverage imaging data for biological insights
• Apply single-cell sequencing to cluster and compare gene expression

Who this book is 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.

Table of Contents

1. Computer Specifications and Python Setup
2. Basics of Data Manipulation
3. Modern Coding Practices and AI-Generated Coding
4. Data Science and Graphing
5. Alignment and Variant Calling
6. Annotation and Biological Interpretation
7. Genomes and Genome Assembly
8. Accessing Public Databases
9. Protein Structure and Proteomics
10. Phylogenetics
11. Population Genetics
12. Metabolic Modeling and Other Applications
13. Genome Editing
14. Cloud Basics
15. Workflow Systems
16. More Workflow Systems
17. Deep Learning and LLMs for Nucleic Acid and Protein Design
18. Single-Cell Technology and Imaging

Shane Brubaker is a bioinformatics manager living in California. He believes in the power of bioinformatics as an interdisciplinary science to save lives and transform society. Shane has applied bioinformatics in areas ranging from synthetic biology to human health. Over the years, he has taught courses in computer science and biology, co-founded BayBifx, a leading Bay Area bioinformatics networking event, and mentored many bioinformatics professionals. Shane is passionate about training and providing opportunities for the next generation of scientists.