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)

Functional Programming for Bioinformatics

Python is a multi-paradigm language that allows you to express computations in multiple different ways. It is sometimes called an object-oriented (OO) language: sure, you can write code in an OO dialect, but you can also use other styles. Most code in Python is written in an imperative style: there is no structuring along a class hierarchy, as typical of the OO paradigm, and most code changes state that, for example, if you write x = x + 1, you are changing the state of variable x.

If you write complex code, especially code that requires parallel processing, imperative and OO paradigms will hit some limits. For simple scripts that run on a single machine, imperative and OO styles will do fine, but bioinformatics is a big-data enterprise, and you will often need to scale up and scale out, as there is lots of information to process, and many algorithms are computationally heavy.

Functional programming is quite useful for the complex and...