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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook - Third Edition

By : Tiago Antao
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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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Parallel processing of data using Python multiprocessing

When dealing with lots of data, one strategy is to process it in parallel so that we make use of all available central processing unit (CPU) power, given that modern machines have many cores. In a theoretical best-case scenario, if your machine has eight cores, you can get an eight-fold increase in performance if you do parallel processing.

Unfortunately, typical Python code only makes use of a single core. That being said, Python has built-in functionality to use all available CPU power; in fact, Python provides several avenues for that. In this recipe, we will be using the built-in multiprocessing module. The solution presented here works well in a single computer and if the dataset fits into memory, but if you want to scale it in a cluster or the cloud, you should consider Dask, which we will introduce in the next two recipes.

Our objective here will again be to compute some statistics around missingness and heterozygosity...

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