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Bioinformatics with Python Cookbook

You're reading from   Bioinformatics with Python Cookbook Use modern Python libraries and applications to solve real-world computational biology problems

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781803236421
Length 360 pages
Edition 3rd Edition
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Author (1):
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Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
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Toc

Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Python and the Surrounding Software Ecology 2. Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib FREE CHAPTER 3. Chapter 3: Next-Generation Sequencing 4. Chapter 4: Advanced NGS Data Processing 5. Chapter 5: Working with Genomes 6. Chapter 6: Population Genetics 7. Chapter 7: Phylogenetics 8. Chapter 8: Using the Protein Data Bank 9. Chapter 9: Bioinformatics Pipelines 10. Chapter 10: Machine Learning for Bioinformatics 11. Chapter 11: Parallel Processing with Dask and Zarr 12. Chapter 12: Functional Programming for Bioinformatics 13. Index 14. Other Books You May Enjoy

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 the data, we will use Biopython and the REST API (we used a similar approach in Chapter 5, 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 Chapter08/Intro.py 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 = ''
        ...
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