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 the data with standard statistics

Now that we have the insights for our Mendelian error analysis, let’s explore the data in order to get more insights that might help us to better filter the data. You can find this content in Chapter04/

How to do it…

  1. We start, as usual, with the necessary imports:
    import gzip
    import pickle
    import random
    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
    from pandas.plotting import scatter_matrix
  2. Then we load the data. We will use pandas to navigate it:
    fit = np.load('balanced_fit.npy.gz', 'rb'))
    ordered_features = np.load(open('ordered_features', 'rb'))
    num_features = len(ordered_features)
    fit_df = pd.DataFrame(fit, columns=ordered_features + ['pos', 'error'])
    num_samples = 80
    del fit
  3. Let’s ask pandas to show a histogram of all annotations:
    fig,ax = plt.subplots(figsize=(16,9))