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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

Preparing a dataset for phylogenetic analysis

In this recipe, we will download and prepare the dataset to be used for our analysis. The dataset contains complete genomes of the Ebola virus. We will use DendroPy to download and prepare the data.

Getting ready

We will download complete genomes from GenBank; these genomes were collected from various Ebola outbreaks, including several from the 2014 outbreak. Note that there are several virus species that cause the Ebola virus disease; the species involved in the 2014 outbreak (the EBOV virus, which was formally known as the Zaire Ebola virus) is the most common, but this disease is caused by more species of the genus Ebolavirus; four others are also available in a sequenced form. You can read more at https://en.wikipedia.org/wiki/Ebolavirus.

If you have already gone through the previous chapters, you might panic looking at the potential data sizes involved here; this is not a problem at all because these are genomes of viruses...

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