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 sgkit for population genetics analysis with xarray

Sgkit is the most advanced Python library for doing population genetics analysis. It’s a modern implementation, leveraging almost all of the fundamental data science libraries in Python. When I say almost all, I am not exaggerating; it uses NumPy, pandas, xarray, Zarr, and Dask. NumPy and pandas were introduced in Chapter 2. Here, we will introduce xarray as the main data container for sgkit. Because I feel that I cannot ask you to get to know data engineering libraries to an extreme level, I will gloss over the Dask part (mostly by treating Dask structures as equivalent NumPy structures). You can find more advanced details about out-of-memory Dask data structures in Chapter 11.

Getting ready

You will need to run the previous recipe because its output is required for this one: we will be using one of the PLINK datasets. You will need to install sgkit.

As usual, this is available in the Chapter06/ Notebook...