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)

Retrieving gene ontology information from Ensembl

In this recipe, you will learn how to use gene ontology information again by querying the Ensembl REST API. Gene ontologies are controlled vocabularies for annotating genes and gene products. These are made available as trees of concepts (with more general concepts near the top of the hierarchy). There are three domains for gene ontologies: the cellular component, the molecular function, and the biological process.

Getting ready

As with the previous recipe, we do not require any pre-downloaded data, but since we are using web APIs, internet access will be needed. The amount of data that will be transferred will be limited.

As usual, you can find this content in the Chapter05/Gene_Ontology.py notebook file. We will make use of the do_request function, which was defined in Step 1 of the previous recipe (Finding orthologues with the Ensembl REST API). To draw GO trees, we will use pygraphviz, a graph-drawing library:

conda...