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)

Exploring a dataset with sgkit

In this recipe, we will perform an initial exploratory analysis of one of our generated datasets. Now that we have some basic knowledge of xarray, we can actually try to do some data analysis. In this recipe, we will ignore population structure, an issue we will return to in the following one.

Getting ready

You will need to have run the first recipe and should have the hapmap10_auto_noofs_ld files available. There is a Notebook file with this recipe called Chapter06/ You will need the software that you installed for the previous recipe.

How to do it...

Take a look at the following steps:

  1. We start by loading the PLINK data with sgkit, exactly as in the previous recipe:
    import numpy as np
    import xarray as xr
    import sgkit as sg
    from import plink
    data = plink.read_plink(path='hapmap10_auto_noofs_ld', fam_sep='\t')
  2. Let’s ask sgkit for variant_stats:
    variant_stats = sg.variant_stats...