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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

By : Tiago R Antao, Tiago Antao
4.7 (6)
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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

4.7 (6)
By: Tiago R Antao, Tiago Antao

Overview of this book

If you have intermediate-level knowledge of Python and are well aware of the main research and vocabulary in your bioinformatics topic of interest, this book will help you develop your knowledge further.
Table of Contents (11 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Python and the Surrounding Software Ecology
1. Python and the Surrounding Software Ecology
Introduction
Installing the required software with Anaconda
Installing the required software with Docker
Interfacing with R via rpy2
Performing R magic with IPython
2
2. Next-generation Sequencing
2. Next-generation Sequencing
Introduction
Accessing GenBank and moving around NCBI databases
Performing basic sequence analysis
Working with modern sequence formats
Working with alignment data
Analyzing data in the variant call format
Studying genome accessibility and filtering SNP data
3
3. Working with Genomes
3. Working with Genomes
Introduction
Working with high-quality reference genomes
Dealing with low-quality genome references
Traversing genome annotations
Extracting genes from a reference using annotations
Finding orthologues with the Ensembl REST API
Retrieving gene ontology information from Ensembl
4
4. Population Genetics
4. Population Genetics
Introduction
Managing datasets with PLINK
Introducing the Genepop format
Exploring a dataset with Bio.PopGen
Computing F-statistics
Performing Principal Components Analysis
Investigating population structure with Admixture
5
5. Population Genetics Simulation
5. Population Genetics Simulation
Introduction
Introducing forward-time simulations
Simulating selection
Simulating population structure using island and stepping-stone models
Modeling complex demographic scenarios
Simulating the coalescent with Biopython and fastsimcoal
6
6. Phylogenetics
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6. Phylogenetics
Introduction
Preparing the Ebola dataset
Aligning genetic and genomic data
Comparing sequences
Reconstructing phylogenetic trees
Playing recursively with trees
Visualizing phylogenetic data
7
7. Using the Protein Data Bank
7. Using the Protein Data Bank
Introduction
Finding a protein in multiple databases
Introducing Bio.PDB
Extracting more information from a PDB file
Computing molecular distances on a PDB file
Performing geometric operations
Implementing a basic PDB parser
Animating with PyMol
Parsing mmCIF files using Biopython
8
8. Other Topics in Bioinformatics
8. Other Topics in Bioinformatics
Introduction
Accessing the Global Biodiversity Information Facility
Geo-referencing GBIF datasets
Accessing molecular-interaction databases with PSIQUIC
Plotting protein interactions with Cytoscape the hard way
9
9. Python for Big Genomics Datasets
9. Python for Big Genomics Datasets
Introduction
Setting the stage for high-performance computing
Designing a poor human concurrent executor
Performing parallel computing with IPython
Computing the median in a large dataset
Optimizing code with Cython and Numba
Programming with laziness
Thinking with generators
10
Index
Index

Reconstructing phylogenetic trees

Here, we will construct phylogenetic trees for the aligned dataset for all Ebola species. We will follow the procedure 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 at http://sco.h-its.org/exelixis/software.html. With Ubuntu Linux, you can simply apt-get install 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 via DendroPy and Biopython, leaving you with a choice of which interface to use; DendroPy gives 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 here...

End of Chapter 6
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