Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook: Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology , Second Edition

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We will start by installing the required software. This will include the Python distribution, some fundamental Python libraries, and external bioinformatics software. Here, we will also be concerned with the world outside Python. In bioinformatics and big data, R is also a major player; therefore, you will learn how to interact with it via rpy2, which is a Python/R bridge. We will also explore the advantages that the IPython framework (via Jupyter Notebook) can give us in order to efficiently interface with R. This chapter will set the stage for all of the computational biology that we will perform in the rest of this book.

As different users have different requirements, we will cover two different approaches for installing the software. One approach is using the Anaconda Python (http://docs.continuum.io/anaconda/) distribution, and another approach to install the software is via Docker (a server virtualization method based on containers sharing the same operating system kernel—https://www.docker.com/). If you are using a Windows-based operating system, you are strongly encouraged to consider changing your operating system or use Docker via some of the existing options on Windows. On macOS, you might be able to install most of the software natively, though Docker is also available.

Key benefits

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

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.

Who is this book for?

This book is for Data data Scientistsscientists, Bioinformatics bioinformatics analysts, researchers, and Python developers who want to address intermediate-to-advanced biological and bioinformatics problems using a recipe-based approach. Working knowledge of the Python programming language is expected.

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

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