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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 alignment data

After you receive your data from the sequencer, you will normally use a tool such as bwa to align your sequences to a reference genome. Most users will have a reference genome for their species. You can read more on reference genomes in the next chapter.

The most common representation for aligned data is the Sequence Alignment/Map (SAM) format. Due to the massive size of most of these files, you will probably work with its compressed version (BAM). The compressed format is indexable for extremely fast random access (for example, to speedily find alignments to a certain part of a chromosome). Note that you will need to have an index for your BAM file normally created by the tabix utility of samtools. Samtools is probably the most widely used tool to manipulate SAM/BAM files.

Getting ready

As discussed in the previous recipe, we will use data from the 1000 genomes project. We will use the exome alignment for chromosome 20 of female NA18489. This is "just&quot...

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