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)

Processing NGS data with HTSeq

HTSeq (https://htseq.readthedocs.io) is an alternative library that’s used for processing NGS data. Most of the functionality made available by HTSeq is actually available in other libraries covered in this book, but you should be aware of it as an alternative way of processing NGS data. HTSeq supports, among others, FASTA, FASTQ, SAM (via pysam), VCF, General Feature Format (GFF), and Browser Extensible Data (BED) file formats. It also includes a set of abstractions for processing (mapped) genomic data, encompassing concepts such as genomic positions and intervals or alignments. A complete examination of the features of this library is beyond our scope, so we will concentrate on a small subset of features. We will take this opportunity to also introduce the BED file format.

The BED format allows for the specification of features for annotations’ tracks. It has many uses, but it’s common to load BED files into genome browsers to...