Book Image

Bioinformatics with Python Cookbook - Third Edition

By : Tiago Antao
Book Image

Bioinformatics with Python Cookbook - Third Edition

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, and this book will show you how to manage these tasks using Python. This updated third edition of the Bioinformatics with Python Cookbook begins with a quick overview of the various tools and libraries in the Python ecosystem that will help you convert, analyze, and visualize biological datasets. Next, you'll cover key techniques for next-generation sequencing, single-cell analysis, genomics, metagenomics, population genetics, phylogenetics, and proteomics with the help of real-world examples. You'll learn how to work with important pipeline systems, such as Galaxy servers and Snakemake, and understand the various modules in Python for functional and asynchronous programming. This book will also help you explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks, including Dask and Spark. In addition to this, you’ll explore the application of machine learning algorithms in bioinformatics. By the end of this bioinformatics Python book, you'll be equipped with the knowledge you need to implement the latest programming techniques and frameworks, empowering you to deal with bioinformatics data on every scale.
Table of Contents (15 chapters)

Extracting data from VCF files

After running a genotype caller (for example, GATK or SAMtools), you will have a VCF file reporting on genomic variations, such as SNPs, insertions/deletions (INDELs), copy number variations (CNVs), and so on. In this recipe, we will discuss VCF processing with the cyvcf2 module.

Getting ready

While NGS is all about big data, there is a limit to how much I can ask you to download as a dataset for this book. I believe that 2 to 20 GB of data for a tutorial is asking too much. While the 1,000 Genomes VCF files with realistic annotations are in this OOM, we will want to work with much less data here. Fortunately, the bioinformatics community has developed tools to allow for the partial download of data. As part of the SAMtools/htslib package (http://www.htslib.org/), you can download tabix and bgzip, which will take care of data management. On the command line, perform the following operation:

tabix -fh ftp://ftp-
trace.ncbi.nih.gov/1000genomes...