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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

By : Tiago Antao
3.5 (4)
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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

3.5 (4)
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. This book covers next-generation sequencing, genomics, metagenomics, population genetics, phylogenetics, and proteomics. You'll learn modern programming techniques to analyze large amounts of biological data. With the help of real-world examples, you'll convert, analyze, and visualize datasets using various Python tools and libraries. This book will help you get a better understanding of working with a Galaxy server, which is the most widely used bioinformatics web-based pipeline system. This updated edition also includes advanced next-generation sequencing filtering techniques. You'll also explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks such as Dask and Spark. By the end of this book, you'll be able to use and implement modern programming techniques and frameworks to deal with the ever-increasing deluge of bioinformatics data.
Table of Contents (12 chapters)
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Finding a protein in multiple databases


Before we start performing some more structural biology, we will look at how we can access existing proteomic databases, such as UniProt. We will query UniProt for our gene of interest, TP53, and take it from there.

Getting ready

To access data, we will use Biopython and the REST API (we used a similar approach in Chapter 3, Working with Genomes) with the requests library to access web APIs. The requests API is an easy-to-use wrapper for web requests that can be installed using standard Python mechanisms (for example, pip and conda). You can find this content in the Chapter07/Intro.ipynb Notebook file.

How to do it...

Take a look at the following steps:

  1. First, let's define a function to perform REST queries on UniProt, as follows:
import requests
server = 'http://www.uniprot.org/uniprot'
def do_request(server, ID='', **kwargs):
    params = ''
    req = requests.get('%s/%s%s' % (server, ID, params), params=kwargs)
    if not req.ok:
        req.raise_for_status...

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