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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

Comparing sequences

Here, we will compare the sequences we aligned in the previous recipe. We will perform gene-wide and genome-wide comparisons.

Getting ready

We will use DendroPy and will require the results from the previous two recipes. As usual, this information is available in the corresponding notebook at Chapter07/Comparison.py.

How to do it...

Take a look at the following steps:

  1. Let’s start analyzing the gene data. For simplicity, we will only use data from two other species of the genus Ebola virus that are available in the extended dataset, that is, the Reston virus (RESTV) and the Sudan virus (SUDV):
    import os
    from collections import OrderedDict
    import dendropy
    from dendropy.calculate import popgenstat
    genes_species = OrderedDict()
    my_species = ['RESTV', 'SUDV']
    my_genes = ['NP', 'L', 'VP35', 'VP40']
    for name in my_genes:
        gene_name = name.split('.')[0...
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