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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

By : Tiago Antao
4 (8)
Buy this Book
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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

4 (8)
By: 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 (15 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Download the color images
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Conventions used
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Sections
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Get in touch
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1
Chapter 1: Python and the Surrounding Software Ecology
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Chapter 1: Python and the Surrounding Software Ecology
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Installing the required basic 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 Jupyter
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Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib
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Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib
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Using pandas to process vaccine-adverse events
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Dealing with the pitfalls of joining pandas DataFrames
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Reducing the memory usage of pandas DataFrames
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Accelerating pandas processing with 
Apache Arrow
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Understanding NumPy as the engine behind Python data science and bioinformatics
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Introducing Matplotlib for chart generation
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Chapter 3: Next-Generation Sequencing
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Chapter 3: Next-Generation Sequencing
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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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Extracting data from VCF files
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Studying genome accessibility and filtering SNP data
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Processing NGS data with HTSeq
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Chapter 4: Advanced NGS Data Processing
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Chapter 4: Advanced NGS Data Processing
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Preparing a dataset for analysis
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Using Mendelian error information for quality control
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Exploring the data with standard statistics
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Finding genomic features from sequencing annotations
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Doing metagenomics with QIIME 2 Python API
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Chapter 5: Working with Genomes
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Chapter 5: Working with Genomes
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Technical requirements
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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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Chapter 6: Population Genetics
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Chapter 6: Population Genetics
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Managing datasets with PLINK
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Using sgkit for population genetics analysis with xarray
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Exploring a dataset with sgkit
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Analyzing population structure
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Performing a PCA
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Investigating population structure with admixture
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Chapter 7: Phylogenetics
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Chapter 7: Phylogenetics
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Preparing a dataset for phylogenetic analysis
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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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Chapter 8: Using the Protein Data Bank
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Chapter 8: Using the Protein Data Bank
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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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Animating with PyMOL
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Parsing mmCIF files using Biopython
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Chapter 9: Bioinformatics Pipelines
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Chapter 9: Bioinformatics Pipelines
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Introducing Galaxy servers
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Accessing Galaxy using the API
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Deploying a variant analysis pipeline with Snakemake
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Deploying a variant analysis pipeline with Nextflow
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Chapter 10: Machine Learning for Bioinformatics
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Chapter 10: Machine Learning for Bioinformatics
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Introducing scikit-learn with a PCA example
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Using clustering over PCA to classify samples
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Exploring breast cancer traits using Decision Trees
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Predicting breast cancer outcomes using Random Forests
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Chapter 11: Parallel Processing with Dask and Zarr
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Chapter 11: Parallel Processing with Dask and Zarr
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Reading genomics data with Zarr
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Parallel processing of data using Python multiprocessing
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Using Dask to process genomic data based on NumPy arrays
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Scheduling tasks with dask.distributed
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Chapter 12: Functional Programming for Bioinformatics
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Chapter 12: Functional Programming for Bioinformatics
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Understanding pure functions
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Understanding immutability
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Avoiding mutability as a robust development pattern
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Using lazy programming for pipelining
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The limits of recursion with Python
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A showcase of Python’s functools module
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Index
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Index
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Why subscribe?
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Other Books You May Enjoy
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Other Books You May Enjoy
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Accessing GenBank and moving around NCBI databases

Although you may have your own data to analyze, you will probably need existing genomic datasets. Here, we will see how to access these databases at the National Center for Biotechnology Information (NCBI). We will not only discuss GenBank, but also other databases at NCBI. Many people refer (wrongly) to the whole set of NCBI databases as GenBank, but NCBI includes the nucleotide database and many others, for example, PubMed. As sequencing analysis is a long subject and this book targets intermediate to advanced users, we will not be very exhaustive with a topic that is, at its core, not very complicated. Nonetheless, it's a good warm-up for more complex recipes at the end of this chapter.

Getting ready

We will use Biopython, which you installed in Chapter 1, Python and the Surrounding Software Ecology. Biopython provides an interface to Entrez, the data retrieval system made available by NCBI. This recipe is made available in the 01_NGS...

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