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Bioinformatics with Python Cookbook

You're reading from   Bioinformatics with Python Cookbook Use modern Python libraries and applications to solve real-world computational biology problems

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781803236421
Length 360 pages
Edition 3rd Edition
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Author (1):
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Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
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Toc

Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Python and the Surrounding Software Ecology 2. Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib FREE CHAPTER 3. Chapter 3: Next-Generation Sequencing 4. Chapter 4: Advanced NGS Data Processing 5. Chapter 5: Working with Genomes 6. Chapter 6: Population Genetics 7. Chapter 7: Phylogenetics 8. Chapter 8: Using the Protein Data Bank 9. Chapter 9: Bioinformatics Pipelines 10. Chapter 10: Machine Learning for Bioinformatics 11. Chapter 11: Parallel Processing with Dask and Zarr 12. Chapter 12: Functional Programming for Bioinformatics 13. Index 14. Other Books You May Enjoy

Avoiding mutability as a robust development pattern

The previous recipe introduced the concept of immutable data structures. In this recipe, we are going to discuss a design pattern that avoids persistent database mutability in your code until the very end. In terms of pseudocode, most applications in a long script work as follows:

Do computation 1
Write computation 1 to disk or database
Do computation 2
Write computation 2 to disk or database
….
Do computation n
Write computation n to disk or database

Here, we are going to present an alternative paradigm and discuss why it is generally better from a resilience point of view:

Do computation 1
Write computation 1 to temporary storage
Do computation 2
Write computation 2 to temporary storage
...
Do computation n
Write computation n to temporary storage
Take all temporary data and write it to definitive disk and database

First, we will show the code for both approaches, and then discuss why, for complex and sophisticated...

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