Discover modern, next-generation sequencing libraries from Python ecosystem to analyze large amounts of biological data

Key Features


Perform complex bioinformatics analysis using the most important Python libraries and applications

Implement next-generation sequencing, metagenomics, automating analysis, population genetics, and more

Explore various statistical and machine learning techniques for bioinformatics data analysis




Book Description

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.

What you will learn


Learn how to process large next-generation sequencing (NGS) datasets

Work with genomic dataset using the FASTQ, BAM, and VCF formats

Learn to perform sequence comparison and phylogenetic reconstruction

Perform complex analysis with protemics data

Use Python to interact with Galaxy servers

Use High-performance computing techniques with Dask and Spark

Visualize protein dataset interactions using Cytoscape

Use PCA and Decision Trees, two machine learning techniques, with biological datasets