English | 2025 | ISBN: 978-1836642756 | 618 Pages | PDF, EPUB | 43 MB
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 datak
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
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