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Bioinformatics with Python Cookbook

By : Tiago R Antao, Tiago Antao
Book Image

Bioinformatics with Python Cookbook

By: Tiago R Antao, Tiago Antao

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Bioinformatics with Python Cookbook
Tiago R Antao | Tiago Antao
Jun, 2015 | 306 pages
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Table of Contents (16 chapters)
Bioinformatics with Python Cookbook
Bioinformatics with Python Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Python and the Surrounding Software Ecology
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
Next-generation Sequencing
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
Working with Genomes
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
Population Genetics
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
Population Genetics Simulation
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
Phylogenetics
Phylogenetics
Introduction
Preparing the Ebola dataset
Aligning genetic and genomic data
Comparing sequences
Reconstructing phylogenetic trees
Playing recursively with trees
Visualizing phylogenetic data
7
Using the Protein Data Bank
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
Other Topics in Bioinformatics
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
Python for Big Genomics Datasets
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
Index
Index

Computing the median in a large dataset

As you have seen in the first recipe, computing the median requires having all the values available. With something like a mean, we just need an accumulator and a counter. The fundamental point of this recipe is to introduce the idea of approximate computing; with big data, it may not always be the best strategy to get the precise value (of course, this should be evaluated on a case-by-case basis).

Getting ready

We will require the first recipe to have been fully run.

Here, we will take two different strategies to compute the median: approximating the data points in a way that allows compression of data and subsampling of data.

As usual, this is available in the 08_Advanced/Median.ipynb notebook.

How to do it...

Take a look at the following steps:

  1. Our first approach will be to use approximations of all values, starting with creating a dictionary. This code should be run where the first recipe was run:

    from __future__ import division, print_function
import...
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