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Bioinformatics with Python Cookbook

By : Tiago Antao

3.5 (4)

Bioinformatics with Python Cookbook

3.5 (4)

By: Tiago Antao

Overview of this book

Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data. This book covers next-generation sequencing, genomics, metagenomics, population genetics, phylogenetics, and proteomics. You'll learn modern programming techniques to analyze large amounts of biological data. With the help of real-world examples, you'll convert, analyze, and visualize datasets using various Python tools and libraries. This book will help you get a better understanding of working with a Galaxy server, which is the most widely used bioinformatics web-based pipeline system. This updated edition also includes advanced next-generation sequencing filtering techniques. You'll also explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks such as Dask and Spark. By the end of this book, you'll be able to use and implement modern programming techniques and frameworks to deal with the ever-increasing deluge of bioinformatics data.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

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Free Chapter

Python and the Surrounding Software Ecology

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 Jupyter Notebook

Next-Generation Sequencing

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 VCF

Studying genome accessibility and filtering SNP data

Processing NGS data with HTSeq

Working with Genomes

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

Population Genetics

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

Population Genetics Simulation

Population Genetics Simulation

Introduction

Introducing forward-time simulations

Simulating selection

Simulating population structure using island and stepping-stone models

Modeling complex demographic scenarios

Phylogenetics

Phylogenetics

Introduction

Preparing a dataset for phylogenetic analysis

Aligning genetic and genomic data

Comparing sequences

Reconstructing phylogenetic trees

Playing recursively with trees

Visualizing phylogenetic data

Using the Protein Data Bank

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

Animating with PyMOL

Parsing mmCIF files using Biopython

Bioinformatics Pipelines

Bioinformatics Pipelines

Introduction

Introducing Galaxy servers

Accessing Galaxy using the API

Developing a Galaxy tool

Using generic pipelines with bioinformatics data

Deploying a variant analysis pipeline with Airflow

Python for Big Genomics Datasets

Python for Big Genomics Datasets

Introduction

Using high-performance data formats – HDF5

Doing parallel computing with Dask

Using high-performance data formats – Parquet

Computing sequencing statistics using Spark

Optimizing code with Cython and Numba

Other Topics in Bioinformatics

Other Topics in Bioinformatics

Introduction

Doing metagenomics with the QIIME 2 Python API

Inferring shared chromosomal segments with Germline

Accessing the Global Biodiversity Information Facility via REST

Georeferencing GBIF datasets

Plotting protein interactions with Cytoscape the hard way

Advanced NGS Processing

Advanced NGS Processing

Introduction

Preparing the dataset for analysis

Using Mendelian error information for quality control

Using decision trees to explore the data

Exploring the data with standard statistics

Finding genomic features from sequencing annotations

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Performing Principal Components Analysis

PCA is a statistical procedure that's used to perform a reduction of the dimension of a number of variables to a smaller subset that is linearly uncorrelated. Its practical application in population genetics is assisting with the visualization of the relationships between the individuals that are being studied.

While most of the recipes in this chapter make use of Python as a glue language (Python calls external applications that actually do most of the work), with PCA, we have an option: we can either use an external application (for example, EIGENSOFT SmartPCA), or use scikit-learn and perform everything on Python. We will perform both.

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