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Genomics in the AWS Cloud: Analyzing Genetic Code Using Amazon Web Services [Pehme köide]

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  • Formaat: Paperback / softback, 336 pages, kõrgus x laius x paksus: 231x185x18 mm, kaal: 567 g
  • Ilmumisaeg: 19-Jun-2023
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119573378
  • ISBN-13: 9781119573371
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Genomics in the AWS Cloud: Analyzing Genetic Code Using Amazon Web ServicesSuurem pilt
  • Formaat: Paperback / softback, 336 pages, kõrgus x laius x paksus: 231x185x18 mm, kaal: 567 g
  • Ilmumisaeg: 19-Jun-2023
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119573378
  • ISBN-13: 9781119573371
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119573371.html
Märksõnad:

Perform genome analysis and sequencing of data with Amazon Web Services

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.

Whether you’re just getting started or have already been analyzing genomics data using the AWS Cloud, this book provides you with the information you need in order to use AWS services and features in the ways that will make the most sense for your genomic research.

Introduction xix
Chapter 1 Why Do Genome Analysis Yourself When Commercial Offerings Exist?
1(8)
Commercial Sequencing Services
2(1)
Typical Results
3(5)
Summary
8(1)
Chapter 2 A Crash Course in Molecular Biology
9(16)
DNA
9(4)
DNA at Work: RNA and Proteins
13(7)
Inheritance
20(3)
Summary
23(2)
Chapter 3 Obtaining Your Genome
25(14)
Preparing to Have Your Genome Sequenced
25(1)
Can It Affect My Insurance?
25(1)
Privacy
26(1)
Humility and Levelheadedness
26(1)
Validation with a Clinically Accredited Test
26(1)
Alternatives to Using Your Own Genome
27(1)
Specifying Lab Work
27(1)
Depth
27(1)
Sample Type
28(1)
Type of Output Files
28(1)
Sequencing Technology
28(2)
Genome vs. Exome vs. SNP Arrays
30(1)
Engaging a Laboratory
30(1)
Getting a Tissue Sample for DNA Extraction
31(1)
Rules and Regulations
32(1)
Do-It-Yourself Phlebotomy
33(1)
Legal Considerations
34(1)
Shipping the Sample
35(1)
Receiving the Results
36(1)
Sequences and Quality Control Information
36(1)
Alignment Information
37(1)
Variation Information
38(1)
Summary
38(1)
Chapter 4 The Bioinformatics Workflow
39(20)
Extraction of DNA
40(1)
Deriving Nucleated Cells from Whole Blood
40(1)
Processing Nucleated Cells
41(1)
FASTA Files
41(1)
FASTQ Files
42(2)
Phred Scores
44(1)
ASCII Encoding of Phred Scores
44(2)
Alignment to a Reference Genome
46(2)
Reference Genomes
48(1)
Quality Control
49(1)
Trimming
50(1)
The Alignment Process
51(2)
Marking Duplicates
53(1)
Recalibrating Base Quality Score
53(1)
Calling SNVs and Indel Variants
54(1)
Annotating SNVs and Indel Variants
55(1)
Prioritizing Variants
56(1)
Inheritance Analysis
56(1)
Identifying SVs and CNVs
57(1)
Bioinformatics Workflow
58(1)
Summary
58(1)
Chapter 5 AWS Services for Genome Analysis
59(18)
General Concepts
61(1)
Networking
61(1)
AWS Functionalities
61(1)
AWS Accounts
61(1)
Virtual Private Cloud
62(1)
Subnets
63(2)
Elastic IP Addresses
65(1)
Custom Environments
65(1)
Storage
66(1)
S3
67(1)
Glacier
67(1)
Computing
68(1)
Elastic Compute Cloud
68(2)
Containers
70(3)
Lambda Functions
73(1)
Workflow Management
74(1)
AWS Batch
74(1)
AWS Step Functions
74(1)
Simple Workflow Service
75(1)
Third-Party Solutions
75(1)
Summary
75(2)
Chapter 6 Building Your Environment in the AWS Cloud
77(38)
Setting Up a Virtual Private Cloud
77(5)
Setting Up and Launching an EC2 Instance
82(9)
Shutting Down an Instance to Save Money
91(1)
Setting Up S3 Buckets
91(4)
Configuring Your Account Securely
95(2)
Turning On Multifactor Authentication
97(4)
Establishing an AWS IAM Password Policy
101(1)
Creating Groups
102(3)
Creating Users
105(1)
Setting Up Your Client Environment
106(1)
Connecting to an EC2 Instance
106(2)
Connecting from macOS or Unix/Linux
108(1)
Connecting from Windows
109(1)
Making S3 Buckets Available Locally
110(1)
Mounting an S3 Bucket as a Windows Drive
111(1)
Mounting an S3 Bucket Under macOS and Linux
111(2)
Summary
113(2)
Chapter 7 Linux and AWS Command-Line Basics for Genomics
115(28)
Selecting a Linux Distribution
115(3)
Accessing Your AWS Linux Instance from Your Local Computer
118(1)
From Windows
118(2)
FrommacOS
120(1)
Options for Setting Up Linux on Your Personal Computer
120(3)
Getting Familiar with the Command Line
123(1)
Absolute and Relative References
124(2)
Manipulating Files
126(1)
Transferring Files to and from Your AWS Instance
127(1)
Keyboard Shortcuts
128(1)
Running Programs in the Background
128(1)
Understanding File Permissions
129(1)
Compressing and Archiving Files
130(1)
Compression
131(1)
Grep
132(1)
Pipes and Redirection Operators
132(1)
Text Processing Utilities: awk and sed
133(2)
Managing Linux
135(1)
Package Management Systems
135(1)
The AWS Command-Line Interface
135(1)
Installing the AWS CLI Environment
136(1)
Windows
136(1)
macOS and Linux
137(1)
Configuring the AWS CLI
137(1)
Setting the Configuration at the Command Line
138(1)
Storing the Configuration in the Configuration File
138(1)
Testing Your Installation
139(1)
AWS CLI Essentials
139(1)
An Alternative Approach: AWS Systems Manager
140(1)
Summary
141(2)
Chapter 8 Processing the Sequencing Data
143(68)
Getting from Data to Information
143(2)
Aligning to the Reference Genome
145(5)
Making Adjustments and Refinements to the Aligned Reads in the BAM File
150(5)
Identifying the Small Differences and Recording Them in the VCF File
155(2)
Making Adjustments and Refinements to the Variants in the VCF File
157(3)
Annotating the SNVs and Indels
160(2)
Prioritizing the Variants to Identify the Most Consequential Ones
162(2)
Trio Analysis and Inheritance Analysis
164(3)
Identifying and Annotating SVs and CNVs
167(5)
Setting Up AWS Services and Data Storage
172(24)
Copying the FASTQ Files
196(1)
Installing Docker and Containers
197(13)
Summary
210(1)
Chapter 9 Visualizing the Genome
211(24)
Introducing Genome Visualizers
211(3)
Installing the IGV Desktop Visualizer
214(2)
Connecting the IGV Visualizer to Our AWS Data
216(4)
Loading Data into the IGV Visualizer
220(6)
Visualizing Aligned Sequencing Reads in IGV
226(3)
Have a CIGAR
229(1)
Analyzing Variants in IGV
230(3)
Summary
233(2)
Chapter 10 Containerizing Your Workflow on the Desktop
235(14)
Introducing Containerization
235(4)
Understanding and Using Docker
239(1)
Installing Docker on Your Local Machine
240(1)
Downloading a Docker Image
241(1)
Viewing Available Docker Images
242(1)
Running a Docker Container Interactively
242(1)
Removing a Docker Image
243(1)
More on Using the Docker Hub
244(1)
Containers for Genomics Work
244(4)
Summary
248(1)
Chapter 11 Variants and Applications
249(18)
Polygenic Risk Scores
249(1)
Genome-wide Association Studies
249(2)
Calculating a Polygenic Score
251(3)
Metagenomics
254(1)
AlphaFold
255(1)
Predicting Protein Structure from Protein Sequence--A 50-Year Puzzle -
256(2)
Installing and Running AlphaFold
258(3)
Viewing and Comparing AlphaFold Results
261(5)
Summary
266(1)
Chapter 12 Cancer Genomics
267(24)
Somatic Genomes
267(1)
Cancer
268(1)
Oncogenes
268(1)
Tumor Suppressors
269(1)
The Promise and Reality of Cancer Precision Medicine
270(3)
Somatic or Gerrnline? Cancer Predisposition
273(1)
Chromothripsis
274(1)
Epigenetics of Cancer
275(1)
Mechanisms of Cancer
276(3)
Samples
279(1)
Somatic Variant Analysis
279(5)
Copy Number Changes
284(3)
Measuring Tumor Genomic Instability
287(1)
Summary
288(1)
Notes
289(2)
Index 291
Catherine Vacher, PhD, is a professional genomics researcher with a focus on supercomputer genome analysis. She has done extensive research into the genetic aspects of various cancers.

David Wall is a consulting engineer. He designs, builds, and supports hardware, software, and business processes. He is an AWS Certified Solution Architect.