Textbook in PDF format

Perform genome analysis and sequencing of data with Amazon Web Services.
From its title, you can conclude that this book is about two things: genomics (the science of sequencing and interpreting genetic data) and Amazon Web Services (one of the three big hosted computing platforms). Genomics in the AWS Cloud, therefore, is meant to appeal either to people from a biology background who want to learn how to do genomics work with AWS or to people with a computer background who want to find out how to apply their skills to genomics.
Genomics in the AWS Cloud: Analyzing Genetic Code Using Amazon Web Services enables a person who has moderate familiarity with AWS Cloud to perform full genome analysis and research. Using the information in this book, you'll be able to take a FASTQ file containing raw data from a lab or a BAM file from a service provider and perform genome analysis on it. You'll also be able to identify potentially pathogenic gene sequences.
Get an introduction to Whole Genome Sequencing (WGS)
Make sense of WGS on AWS
Master AWS services for genome analysis
Some key advantages of using AWS for genomic analysis is to help researchers utilize a wide choice of compute services that can process diverse datasets in analysis pipelines. Genomic sequencers that generate raw data files are located in labs on premises and AWS provides solutions to make it easy for customers to transfer these files to AWS reliably and securely. Storing Genomics and Medical (e.g., imaging) data at different stages requires enormous storage in a cost-effective manner. Amazon Simple Storage Service (Amazon S3), Amazon Glacier, and Amazon Elastics Block Store (Amazon EBS) provide the necessary solutions to securely store, manage, and scale genomic file storage. Moreover, the storage services can interface with various compute services from AWS to process these files.
Amazon EC2 (and other, similar services) probably represent the ultimate evolution of the virtual machine idea. Not only does EC2 support images that can be stored as files (AMI images) and brought online as live virtual machines as required, but the whole infrastructure for hosting virtual machines is abstracted from the AWS user's point of view. Obviously, there are physical machines running EC2 instances in AWS data centers, but the average person who brings up an EC2 instance via the AWS console or command line is not (indeed, cannot be) concerned with how that works behind the scenes. Containers—there are many kinds, but Docker containers are the de facto standard—implement what is OS-level virtualization, which is to say virtualization of applications at the level of the operating system. One instance of an operating system can run many different containers. Applications running inside the containers “perceive” that they are running on their own computers, complete with processors, memory, peripherals, and storage. In fact, those physical resources (and the operating system on top of them) are potentially shared across many containerized applications.
Introduction
CHAPTER 1: Why Do Genome Analysis Yourself When Commercial Offerings Exist?
CHAPTER 2: A Crash Course in Molecular Biology
CHAPTER 3: Obtaining Your Genome
CHAPTER 4: The Bioinformatics Workflow
CHAPTER 5: AWS Services for Genome Analysis
CHAPTER 6: Building Your Environment in the AWS Cloud
CHAPTER 7: Linux and AWS Command-Line Basics for Genomics
CHAPTER 8: Processing theSequencing Data
CHAPTER 9: Visualizing the Genome
CHAPTER 10: Containerizing Your Workflow on the Desktop
CHAPTER 11: Variants and Applications
CHAPTER 12: Cancer Genomics