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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

Visualizing phylogenetic data

In this recipe, we will discuss how to visualize phylogenetic trees. DendroPy only has simple visualization mechanisms based on drawing textual ASCII trees, but Biopython has quite a rich infrastructure, which we will leverage here.

Getting ready

This will require you to have completed all of the previous recipes. Remember that we have the files for the whole genus of the Ebola virus, including the RAxML tree. Furthermore, a simplified genus version will have been produced in the previous recipe. As usual, you can find this content in the Chapter07/Visualization.py notebook file.

How to do it...

Take a look at the following steps:

  1. Let’s load all of the phylogenetic data:
    from copy import deepcopy
    from Bio import Phylo
    ebola_tree = Phylo.read('my_ebola.nex', 'nexus')
    ebola_tree.name = 'Ebolavirus tree'
    ebola_simple_tree = Phylo.read('ebola_simple.nex', 'nexus')
    ebola_simple_tree...
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