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)

Finding genomic features from sequencing annotations

We will conclude this chapter and this book with a simple recipe that suggests that sometimes you can learn important things from simple unexpected results, and that apparent quality issues might mask important biological questions.

We will plot read depth – DP – across chromosome arm 2L for all the parents on our crosses. The recipe can be found in Chapter04/2L.py.

How to do it…

We’ll get started with the following steps:

  1. Let’s start with the usual imports:
    from collections import defaultdict
    import gzip
    import numpy as np
    import matplotlib.pylab as plt
  2. Let’s load the data that we saved in the first recipe:
    num_parents = 8
    dp_2L = np.load(gzip.open('DP_2L.npy.gz', 'rb'))
    print(dp_2L.shape)
  3. And let’s print the median DP for the whole chromosome arm, and a part of it in the middle for all parents:
    for i in range(num_parents):
       ...