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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
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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
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

Installing the required software with Docker

Docker is the most widely used framework that implements operating system-level virtualization. This technology allows you to have an independent container: a layer that is lighter than a virtual machine, but still allows you to compartmentalize software. This mostly isolates all processes, making it feel like each container is a virtual machine.

Docker works quite well at both extremes of the development spectrum: it's an expedient way to set up the content of this book for learning purposes and may be your platform to deploy your applications in complex environments. This recipe is an alternative to the previous recipe. However, for long-term development environments, something along the lines of the previous recipe is probably your best route, although it can entail a more laborious initial setup.

Getting ready

If you are on Linux, the first thing you have to do is to install Docker. The safest solution is to get the latest version from https://www.docker.com/. While your Linux distribution may have a Docker package, it may be too old and buggy (remember the "advancing at breakneck speed" thingy?).

If you are on Windows or Mac, do not despair; boot2docker (http://boot2docker.io/) is here to save you. Boot2docker will install VirtualBox and Docker for you, which allows you to run Docker containers in a virtual machine. Note that a fairly recent computer (well, not that recent, as the technology was introduced in 2006) is necessary to run our 64-bit virtual machine. If you have any problems, reboot your machine and make sure that on the BIOS, VT-X or AMD-V is enabled. At the very least, you will need 6 GB of memory, preferably more.

Note that this will require a very large download from the Internet, so be sure that you have a big network pipe. Also, be ready to wait for a long time.

How to do it…

These are the steps to be followed:

  1. Use the following command on the Linux shell or in boot2docker:
    docker build -t bio https://raw.githubusercontent.com/tiagoantao/bioinf-python/master/docker/2/Dockerfile
    • If you want the Python 3 version, replace the 2 with 3 versions on the URL. After a fairly long wait, all should be ready.
    • Note that on Linux, you will either require to have root privileges or be added to the Docker Unix group.
  2. Now, you are ready to run the container, as follows:
    docker run -ti -p 9875:9875 -v YOUR_DIRECTORY:/data bio
  3. Replace YOUR_DIRECTORY with a directory on your operating system. This will be shared between your host operating system and the Docker container. YOUR_DIRECTORY will be seen in the container on /data and vice versa.
    • The -p 9875:9875 will expose the container TCP port 9875 on the host computer port 9875.
  4. If you are using boot2docker, the final configuration step will be to run the following command in the command line of your operating system, not in boot2docker:
    VBoxManage controlvm boot2docker-vm natpf1 "name,tcp,127.0.0.1,9875,,9875"

    Note

    On Windows, this binary will probably be in C:\Program Files\Oracle\VirtualBox.

    On a native Docker installation, you do not need to do anything.

  5. If you now start your browser pointing at http://localhost:9875, you should be able to get the IPython Notebook server running. Just choose the Welcome notebook to start!

See also

  • Docker is the most widely used containerization software and has seen enormous growth in usage in recent times. You can read more about it at https://www.docker.com/.
  • You will find a paper on arXiv, which introduces Docker with a focus on reproducible research at http://arxiv.org/abs/1410.0846.
End of Section 4
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