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)

Investigating population structure with admixture

A typical analysis in population genetics was the one popularized by the program structure (https://web.stanford.edu/group/pritchardlab/structure.html), which is used to study population structure. This type of software is used to infer how many populations exist (or how many ancestral populations generated the current population), and to identify potential migrants and admixed individuals. The structure was developed quite some time ago, when far fewer markers were genotyped (at that time, this was mostly a handful of microsatellites), and faster versions were developed, including one from the same laboratory called fastStructure (http://rajanil.github.io/fastStructure/). Here, we will use Python to interface with a program of the same type that was developed at UCLA, called admixture (https://dalexander.github.io/admixture/download.html).

Getting ready

You will need to run the first recipe in order to use the hapmap10_auto_noofs_ld...