Bioinformatics with Python Cookbook - Second Edition: Over 70 recipes to implement modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology

Tiago Antao

立即出貨 (庫存=1)


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 analyses, population genetics and more
  • Explore and analyze bioinformatics data using standard statistics and Machine Learning approach

Book Description

With increasing biological data, there's a need for bioinformaticians in order to analyze them and extract insights. This book uses a unique recipe based approach to address the key building blocks in bioinformatics domain using Python ecosystem.

This book is packed with all the emerging topics ranging from Metagenomics, Transcription of DNA to RNA, Bioinformatics shell scripting, Web interaction, Automating analyses and more. You will get to work with different set of data such as sequence data, alignment data, range data, genome data and more for analyzing a large amount of biological data. You will get a better understanding of working with Galaxy server, which are the widely used bioinformatics web-interfaces. This book will also show you GPU computing for computationally complex problems via OpenCL and PyOpenCL. This second edition will also cover Machine Learning techniques in Bioinformatics. You will explore topics like SNP discovery using SVMs and exploratory analysis of data using standard statistic and ML approaches.

By the end of this book, you will be able to 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 FASTQ, BAM and VCF formats from Python interface
  • Explore the steps to perform sequence comparison and phylogenetic reconstruction
  • Perform geometric operations with atom and molecule data
  • Discover vilsualization techniques inside a galaxy server
  • Learn GPU computing for computationally complex problems via OpenCL and PyOpenCL
  • Visualise protein dataset interactions using Cytoscape
  • Understand the steps involved in training an SVM for SNP discovery

Who This Book Is For

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