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 lazy programming for pipelining

Lazy programming is a strategy where we defer computation until it’s really needed. It has many advantages compared with its counterpart, eager programming, where we compute everything as soon as we invoke a computation.

Python provides many mechanisms for lazy programming – indeed, one of the biggest changes from Python 2 to Python 3 is that the language became lazier.

To understand lazy programming, we are going again to take our gene database and do an exercise with it. We are going to check whether we have at least n genes with y reads each (for example, three genes with five reads each). This can be, say, a measure of the quality of our database – that is, a measure of whether we have enough genes with a certain number of samples.

We are going to consider two implementations: one lazy and one eager. We will then compare the implications of both approaches.

Getting ready

The code for this recipe can be found...