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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

By : Tiago Antao
4 (8)
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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

4 (8)
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)
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Understanding pure functions

A pure function has a couple of important properties: for the same inputs, it produces the same outputs, and it has no side effects (it doesn’t change global variables and doesn’t do I/O). In this recipe, we are going to introduce a few simple examples to make the concept clear. We are mostly – but not exclusively – interested in the second property: the lack of side effects. In later recipes, it will be made clear why pure functions can be quite useful.

We are going to develop a very simple example where we are counting the genes sequenced per sample. We will have a database on a text file with counts for genes. For example, we might have sequenced LCT and TP53 on a sample, and LCT, MRAP2, and POMC on another. The total count would be: TP53: 1, LCT: 2, MRPA2: 1, and POMC: 1. We will be using a CSV file that can be easily read with Pandas or even just the CSV module.

Getting ready

We will be using a simple CSV file...

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