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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook - Fourth Edition

By : Shane Brubaker
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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

By: Shane Brubaker

Overview of this book

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.
Table of Contents (22 chapters)
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Analyzing single-cell data

In this recipe, we’ll look at the exciting world of single-cell analysis. We’ve seen that microfluidics technology is poised to transform the throughput and accuracy with which we can perform biological experiments. This has powered the rise of single-cell technologies in which individual cells can be sorted, classified, and then handled for further analysis.

Before this technology came about, most experiments were performed using bulk cell analysis. This refers to the fact that large numbers of cells are mixed together and analyzed as a group, meaning that we are averaging out their properties. But individual cells each have different transcriptional programs, proteomic states, and metabolic profiles at any given time. To really understand biology, we need the ability to look at cells one at a time. This is where single-cell analysis comes in.

Here are some of the main types of single-cell technology:

  • Genomics: Analyzes the DNA...
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Bioinformatics with Python Cookbook
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