Book Image

Bioinformatics with Python Cookbook - Second Edition

By : Tiago Antao
Book Image

Bioinformatics with Python Cookbook - Second Edition

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 (16 chapters)
Title Page
About Packt
Contributors
Preface
Index

Reconstructing phylogenetic trees


Here, we will construct phylogenetic trees for the aligned dataset for all Ebola species. We will follow a procedure that's quite similar to the one used in the paper.

Getting ready

This recipe requires RAxML, a program for maximum likelihood-based inference of large phylogenetic trees, which you can check out at http://sco.h-its.org/exelixis/software.html. Bioconda includes it, where it is named raxml. Note that the binary is called raxmlHPC.

The code here is simple, but it will take time to execute because it will call RAxML (which is computationally intensive). If you opt to use the DendroPy interface, it may also become memory-intensive. We will interact with RAxML, DendroPy, and Biopython, leaving you with a choice of which interface to use; DendroPy gives you an easy way to access results, whereas Biopython is less memory-intensive. Although there is a recipe for visualization later in this chapter, we will nonetheless plot one of our generated trees...