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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

Get in touch

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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Studying genome accessibility and filtering SNP data

While the previous recipes were focused on giving an overview of Python libraries to deal with alignment and variant call data, in this recipe, we will concentrate on actually using them with a clear purpose in mind.

If you are using NGS data, chances are that your most important file to analyze is a VCF file, which is produced by a genotype caller such as SAMtools, mpileup, or GATK. The quality of your VCF calls may need to be assessed and filtered. Here, we will put in place a framework to filter SNP data. Rather than giving you filtering rules (an impossible task to be performed in a general way), we will give you procedures to assess the quality of your data. With this, you can devise your own filters. Be sure to check Chapter 11, Advanced NGS Processing for more tips on filtering.

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