Book Image

Bioinformatics with Python Cookbook - Third Edition

By : Tiago Antao
Book Image

Bioinformatics with Python Cookbook - Third Edition

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)

Using Dask to process genomic data based on NumPy arrays

Dask is a library that provides advanced parallelism that can scale from a single computer to very large clusters or a cloud operation. It also provides the ability to process datasets that are larger than memory. It is able to provide interfaces that are similar to common Python libraries such as NumPy, Pandas, or scikit-learn.

We are going to repeat a subset of the example from previous recipes—namely, compute missingness for the SNPs in our dataset. We will be using an interface similar to NumPy that is offered by Dask.

Before we start, be aware that the semantics of Dask are quite different from libraries such as NumPy or Pandas: it is a lazy library. For example, when you specify a call equivalent to—say—np.sum, you are not actually calculating a sum, but adding a task that in the future will eventually calculate it. Let’s get into the recipe to make things clearer.

Getting ready