Bioinformatics with Python Cookbook: Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology, 2nd Edition

Bioinformatics with Python Cookbook: Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology, 2nd Edition

<p><b>Discover modern, next-generation sequencing libraries from Python ecosystem to analyze large amounts of biological data</b></p> <h4>Key Features</h4> <ul><li>Perform complex bioinformatics analysis using the most important Python libraries and applications </li> <li>Implement next-generation sequencing, metagenomics, automating analysis, population genetics, and more </li> <li>Explore various statistical and machine learning techniques for bioinformatics data analysis</li></ul> <h4>Book Description</h4> <p>Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data. </p> <p>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. </p> <p>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. </p> <p>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.</p> <h4>What you will learn</h4> <ul><li>Learn how to process large next-generation sequencing (NGS) datasets </li> <li>Work with genomic dataset using the FASTQ, BAM, and VCF formats </li> <li>Learn to perform sequence comparison and phylogenetic reconstruction </li> <li>Perform complex analysis with protemics data </li> <li>Use Python to interact with Galaxy servers </li> <li>Use High-performance computing techniques with Dask and Spark </li> <li>Visualize protein dataset interactions using Cytoscape </li> <li>Use PCA and Decision Trees, two machine learning techniques, with biological datasets</li></ul> <h4>Who this book is for</h4> <p>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.</p><h4>Table of Contents</h4> <ol><li>Python and the Surrounding Software Ecology</li> <li>Next-generation Sequencing</li> <li>Working with Genomes</li> <li>Population Genetics</li> <li>Population Genetics Simulation</li> <li>Phylogenetics</li> <li>Using the Protein Data Bank</li> <li>Bioinformatics pipelines</li> <li>Python for Big Genomics Datasets</li> <li>Other Topics in Bioinformatics</li> <li>Machine learning in Bioinformatics</li></ol>