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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

By : Tiago Antao
4 (8)
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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

4 (8)
By: Tiago Antao

Overview of this book

Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data, and this book will show you how to manage these tasks using Python. This updated third edition of the Bioinformatics with Python Cookbook begins with a quick overview of the various tools and libraries in the Python ecosystem that will help you convert, analyze, and visualize biological datasets. Next, you'll cover key techniques for next-generation sequencing, single-cell analysis, genomics, metagenomics, population genetics, phylogenetics, and proteomics with the help of real-world examples. You'll learn how to work with important pipeline systems, such as Galaxy servers and Snakemake, and understand the various modules in Python for functional and asynchronous programming. This book will also help you explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks, including Dask and Spark. In addition to this, you’ll explore the application of machine learning algorithms in bioinformatics. By the end of this bioinformatics Python book, you'll be equipped with the knowledge you need to implement the latest programming techniques and frameworks, empowering you to deal with bioinformatics data on every scale.
Table of Contents (15 chapters)
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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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