Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Bioinformatics with Python Cookbook
  • Table Of Contents Toc
  • Feedback & Rating feedback
Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

By : Tiago R Antao, Tiago Antao
4.7 (6)
Buy this Book
close
close
Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

4.7 (6)
By: Tiago R Antao, Tiago Antao
Buy this Book

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

Analyzing data in the variant call format

After running a genotype caller (for example, GATK or samtools), you will have a variant call format (VCF) file reporting on genomic variations, such as single-nucleotide polymorphisms (SNPs), Insertions/Deletions (INDELs), copy number variation (CNVs), and so on. In this recipe, we will discuss VCF processing with the PyVCF module.

Getting ready

While next-generation sequencing is all about big data, there is a limit to how much I can ask you to download as a dataset for this book. I believe that 2 to 20 GB of data for a tutorial is asking too much. While the 1000 genomes' VCF files with realistic annotations are in this order of magnitude, we will want to work with much less data here. Fortunately, the bioinformatics community has developed tools to allow partial download of data. As part of the samtools/htslib package (http://www.htslib.org/), you can download tabix and bgzip, which will take care of data management (on Debian, Ubuntu, and...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
Start a 7-day FREE trial
End of Section 7
Next Section

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY