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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 
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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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Dealing with low-quality genome references

Unfortunately, not all reference genomes will have the quality of P. falciparum. Apart from some model species (for example, humans or the common fruit fly Drosophila melanogaster) and a few others, most reference genomes could use some improvement. In this recipe, we will see how to deal with reference genomes with less quality.

Getting ready

In keeping with the malaria theme, here, we will use the reference genomes of two mosquitoes that are vectors of malaria: Anopheles gambiae (which is the most important vector of malaria and can be found in sub-Saharan Africa) and Anopheles atroparvus, a malaria vector in Europe (while the disease has been eradicated in Europe, this vector is still around). The An. gambiae genome is of reasonable quality. Most chromosomes have been mapped, although the Y chromosome still needs some work. There is a fairly large "unknown" chromosome, probably composed of bits X and Y chromosomes and also midgut microbiota...

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