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)

Reading genomics data with Zarr

Zarr (https://zarr.readthedocs.io/en/stable/) stores array-based data—such as NumPy —in a hierarchical structure on disk and cloud storage. The data structures used by Zarr to represent arrays are not only very compact but also allow for parallel reading and writing, something we will see in the next recipes. In this recipe, we will be reading and processing genomics data from the Anopheles gambiae 1000 Genomes project (https://malariagen.github.io/vector-data/ag3/download.html). Here, we will simply do sequential processing to ease the introduction to Zarr; in the following recipe, we will do parallel processing. Our project will be computing the missingness for all genomic positions sequenced for a single chromosome.

Getting ready

The Anopheles 1000 Genomes data is available from Google Cloud Platform (GCP). To download data from GCP, you will need gsutil, available from https://cloud.google.com/storage/docs/gsutil_install. After...