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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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Scheduling tasks with dask.distributed

Dask is extremely flexible in terms of execution: we can execute locally, on a scientific cluster, or on the cloud. That flexibility comes at a cost: it needs to be parameterized. There are several alternatives to configure a Dask schedule and execution, but the most generic is dask.distributed as it is able to manage different kinds of infrastructure. Because I cannot assume you have access to a cluster or a cloud such as Amazon Web Services (AWS) or GCP, we will be setting up computation on your local machine, but remember that you can set up dask.distributed on very different kinds of platforms.

Here, we will again compute simple statistics over variants of the Anopheles 1000 Genomes project.

Getting ready

Before we start with dask.distributed, we should note that Dask has a default scheduler that actually can change depending on the library you are targeting. For example, here is the scheduler for our NumPy example:

import dask...

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