Bioinformatics with Python Cookbook
Learn How to Use Modern Python Bioinformatics Libraries and Applications to Do Cutting-Edge Research in Computational Biology
1. Auflage
Erschienen am 25. Juni 2015
306 Seiten
978-1-78355-865-0 (ISBN)
Beschreibung
If you are either a computational biologist or a Python programmer, you will probably relate to the expression "e;explosive growth, exciting times"e;. Python is arguably the main programming language for big data, and the deluge of data in biology, mostly from genomics and proteomics, makes bioinformatics one of the most exciting fields in data science.Using the hands-on recipes in this book, you'll be able to do practical research and analysis in computational biology with Python. We cover modern, next-generation sequencing libraries and explore real-world examples on how to handle real data. The main focus of the book is the practical application of bioinformatics, but we also cover modern programming techniques and frameworks to deal with the ever increasing deluge of bioinformatics data.
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Inhalt
- Cover
- Copyright
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Table of Contents
- Preface
- Chapter 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
- Chapter 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 variant call format
- Studying genome accessibility and filtering SNP data
- Chapter 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
- Chapter 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
- Chapter 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
- Chapter 6: Phylogenetics
- Introduction
- Preparing the Ebola dataset
- Aligning genetic and genomic data
- Comparing sequences
- Reconstructing phylogenetic trees
- Playing recursively with trees
- Visualizing phylogenetic data
- Chapter 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
- Chapter 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
- Chapter 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
