Sign In Start Free Trial

Book Overview & Buying
Table Of Contents
Feedback & Rating

Bioinformatics with Python Cookbook

By : Tiago R Antao, Tiago Antao

4.7 (6)

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.

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. 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. 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. 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. 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. 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. 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. 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. 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. 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

Index

Index

Customer Reviews

4.7 (6)

5 star

66.7%

4 star

33.3%

3 star

0

2 star

0

1 star

0

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, filenames, file extensions, URLs, user input, are shown as follows: "If you are using notebooks, open the 00_Intro/Interfacing_R notebook.ipynb and just execute the wget command on top."

A block of code is set as follows:

from collections import OrderedDict
import simuPOP as sp
pop_size = 100
pops = sp.Population(pop_size, loci=[1] * num_loci)
num_loci = 10
init_ops['Freq'] = sp.InitGenotype(freq=[0.5, 0.5])

Any command-line input or output is written as follows:

conda create -n bioinformatics biopython=1.65 python=3.4

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "The top line is without migration, the middle line with 0.005 migration and the bottom line with 0.1."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

Search

Your notes and bookmarks