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)

Performing R magic with Jupyter

Jupyter provides quite a few extra features compared to standard Python. Among those features, it provides a framework of extensible commands called magics (actually, this only works with the IPython kernel of Jupyter since it is actually an IPython feature, but that is the one we are concerned with). Magics allow you to extend the language in many useful ways. There are magic functions that you can use to deal with R. As you will see in our example, it makes R interfacing much easier and more declarative. This recipe will not introduce any new R functionalities, but hopefully, it will make it clear how IPython can be an important productivity boost for scientific computing in this regard.

Getting ready

You will need to follow the previous Getting ready steps of the Interfacing with R via rpy2 recipe. The notebook is Chapter01/R_magic.py. The notebook is more complete than the recipe presented here and includes more chart examples. For brevity...