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  • Book Overview & Buying Bioinformatics with Python Cookbook
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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

By : Tiago R Antao, Tiago Antao
4.7 (6)
Buy this Book
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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)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Sections
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Conventions
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Reader feedback
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Customer support
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1
1. Python and the Surrounding Software Ecology
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1. Python and the Surrounding Software Ecology
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Introduction
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Installing the required software with Anaconda
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Installing the required software with Docker
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Interfacing with R via rpy2
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Performing R magic with IPython
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2. Next-generation Sequencing
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2. Next-generation Sequencing
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Introduction
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Accessing GenBank and moving around NCBI databases
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Performing basic sequence analysis
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Working with modern sequence formats
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Working with alignment data
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Analyzing data in the variant call format
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Studying genome accessibility and filtering SNP data
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3. Working with Genomes
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3. Working with Genomes
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Introduction
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Working with high-quality reference genomes
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Dealing with low-quality genome references
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Traversing genome annotations
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Extracting genes from a reference using annotations
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Finding orthologues with the Ensembl REST API
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Retrieving gene ontology information from Ensembl
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4. Population Genetics
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4. Population Genetics
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Introduction
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Managing datasets with PLINK
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Introducing the Genepop format
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Exploring a dataset with Bio.PopGen
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Computing F-statistics
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Performing Principal Components Analysis
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Investigating population structure with Admixture
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5. Population Genetics Simulation
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5. Population Genetics Simulation
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Introduction
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Introducing forward-time simulations
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Simulating selection
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Simulating population structure using island and stepping-stone models
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Modeling complex demographic scenarios
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Simulating the coalescent with Biopython and fastsimcoal
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6. Phylogenetics
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6. Phylogenetics
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Introduction
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Preparing the Ebola dataset
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Aligning genetic and genomic data
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Comparing sequences
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Reconstructing phylogenetic trees
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Playing recursively with trees
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Visualizing phylogenetic data
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7. Using the Protein Data Bank
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7. Using the Protein Data Bank
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Introduction
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Finding a protein in multiple databases
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Introducing Bio.PDB
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Extracting more information from a PDB file
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Computing molecular distances on a PDB file
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Performing geometric operations
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Implementing a basic PDB parser
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Animating with PyMol
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Parsing mmCIF files using Biopython
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8. Other Topics in Bioinformatics
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8. Other Topics in Bioinformatics
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Introduction
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Accessing the Global Biodiversity Information Facility
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Geo-referencing GBIF datasets
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Accessing molecular-interaction databases with PSIQUIC
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Plotting protein interactions with Cytoscape the hard way
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9. Python for Big Genomics Datasets
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9. Python for Big Genomics Datasets
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Introduction
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Setting the stage for high-performance computing
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Designing a poor human concurrent executor
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Performing parallel computing with IPython
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Computing the median in a large dataset
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Optimizing code with Cython and Numba
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Programming with laziness
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Thinking with generators
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Index
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Index

Working with modern sequence formats

Here, we will work with FASTQ files, the standard format output by modern sequencers. You will learn how to work with quality scores per base and also consider the variations in output coming from different sequencing machines and databases. This is the first recipe that will use real data (big data) from the human 1000 genomes project. We will start with a brief description of the project.

Getting ready

The human 1000 genomes project aims to catalog world-wide human genetic variation and takes advantage of modern sequencing technology to do WGS. This project makes all data publicly available, which includes output from sequencers, sequence alignments, and SNP calls, among many other artifacts. The name 1000 genomes is actually a misnomer because it currently includes more than 2500 samples. These samples are divided into 26 populations, spanning the whole planet. We will mostly use data from four populations: African Yorubans (YRI), Utah Residents with...

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