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Full Description
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 DescriptionIf 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 forThis 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.
Contents
Table of Contents
Computer Specification and Python Setup
Basics of Data Manipulation
Modern Coding Practices and AI generated Coding
Data Science and Graphing
Alignment and Variant Calling
Annotation and Biological Interpretation
Genomes and Genome Assembly
Nucleic Acid Database
Protein Databases
Phylogenetics
Population Genomics
Metabolic Modeling and Other Applications
Genome Editing
Cloud Basics, Infrastructure as Code, and Containers
Workflow Systems
Machine Learning, Deep Learning, and LLMs for Nucleic Acid and Protein Design
Single Cell Technology and Imaging Data
