Genomics in the Azure Cloud

Spis treści ebooka

• Preface
  • Who Should Read This Book
  • How the Book Is Organized
  • Software and Hardware Requirements
  • Code Conventions and Downloads
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Essentials of Cloud Architecture
  • Cloud Horsepower
    • Considerations for the Cloud
      • I have to move everything to the cloud at once.
      • The cloud is always cheaper/more expensive.
      • Our IT security team can manage security better.
    • Three Benefits of the Cloud
      • Collaboration
      • Scalability
      • Automation
  • Types of Cloud Services
    • Infrastructure Services
      • Example: Genomics Data Science Virtual Machine
    • Platform Services
      • Example: Azure database for PostgreSQL
    • Software Services
  • Azure Environment Organization
  • Getting an Azure Account
  • Welcome to the Azure Portal
    • Setting Up a Resource Group
    • Creating Resources
    • Free Services
  • Basics of the Bioinformatics Workflow
    • Primary Analysis
      • FASTA
      • FASTQ
    • Secondary Analysis
      • SAM (and BAM)
      • VCF
    • Tertiary Analysis
    • Other Analyses
    • Other File Formats
      • GEN (and BGEN)
      • GFF
      • PDB
• 2. Organizing Genomics Data with Data Lakes
  • Organizing Your Genomics Data
    • Going for Bronze, Silver, and Gold
      • Bronze (raw)
      • Silver (staging/intermediate)
      • Gold (curated)
    • Letting Your Bioinformatics Workflow Dictate Your Data Lake Organization
    • Planning for -omics and Non-omics Data Together
      • Study, subject, and sample directories
  • Creating a Data Lake with Azure Storage
    • Blob Storage Versus Data Lake Storage
      • About the hierarchical namespace
  • Balancing Costs Versus Performance in Data Storage
    • The Goldilocks Method of Storage Tiers
      • Cost breakdown
    • Genomics Data Lifecycle
      • Using Azure Storage Explorer
      • Lifecycle rules
  • Managing Access Inside the Lake
    • Role-Based Access Control
    • Access-Control Lists
  • Azure Open Datasets for Genomics
• 3. Querying Variant Data in SQL
  • Building a Genomics Data Warehouse
    • Example: Lab Results
    • Data Warehouse Architecture for Genomics
      • Variant data warehouse
  • Azure Synapse Analytics
    • Creating an Azure Synapse Analytics Workspace
    • Registering Services in Subscriptions
    • Getting to Work in the Synapse Workspace
    • Using Open Row Sets
    • Creating External Tables
    • Did Someone Say Pool Party?
      • Serverless SQL pools
      • Dedicated SQL pools
      • Serverless Spark pools
      • Pool cost considerations
  • Connecting to More Data Sources
  • Azure SQL DB
    • Creating a Database in Azure SQL DB
      • Elastic pools
      • Provisioned and Serverless compute
  • Relaxing at Your Genomics Data Lakehouse
    • Efficient File Formats
      • Parquet to the floor
      • ACIDity
      • Changing the tides with Delta
• 4. Orchestrating Data Movement and Transformation
  • Creating Your Data Factory
  • Getting Started with Data Movement
    • Getting Data into Your Data Lake Using the Copy Data Tool
    • Linking to NCBIs FTP Server
    • Transforming Data Using Data Flows
      • Parsing a VCF file with a data flow
    • Building and Triggering Pipelines for Automation
• 5. Azure Databricks (and Apache Spark)
  • Introduction to Apache Spark and Databricks
  • Setting Up an Azure Databricks Workspace
    • Connecting Databricks to Your Data Lake
  • Processing Variant Data with the Glow Package
    • Exploring DataFrames
    • Filtering to Chromosome Coordinates
      • Count of variants by chromosome
  • Automating Variant Data Processing
    • Orchestrating a Databricks Notebook from Data Factory
      • Access tokens
      • Creating the pipeline
    • A Brief Interlude About Distributed File Formats
      • Parquet
      • Delta
  • Using Other Tools in Databricks
    • Single-Node Bioinformatics Tools
    • Koalas
      • Pandas on Spark
    • Hail
• 6. Azure Machine Learning
  • How to Scale Machine Learning Tasks
  • Creating an Azure Machine Learning Workspace
  • Training a Drug Sensitivity Model
    • Creating a Compute Instance in Azure Machine Learning Studio
    • Datastores and Datasets
      • About the data
    • Experimenting with Cluster-Based Training
  • Automating Model Training with AutoML
    • Explainable Machine Learning
  • Using Azure Machine Learning Not for Machine Learning
    • Performing Alignment in a Notebook
    • Custom Docker Images for Bioinformatics
• 7. High-Performance Computing and Other Compute Services
  • Bring Your Own Pipeline (BYOP)
    • Why Azure for HPC?
  • Azure Batch
    • Scaling Workloads with Cromwell
      • Running your first workflow
  • Azure CycleCloud
    • Setting Up CycleCloud Clusters
      • Creating a cluster for bioinformatics
  • Microsoft Genomics
    • Alignment and Variant Calling with the msgen Package
• 8. Deployment, Security, Compliance, and Potpourri
  • Automating the Deployment of Cloud Resources
    • Dev, Staging, and Prod
    • Lifting Your Deployment with ARMs and Biceps
      • Bicep
  • Security Planning
    • Azure Active Directory
      • Roles and groups
    • Role-Based Access Controls and Access-Control Lists
  • Compliance
    • HIPAA, HITECH, and HITRUST
      • GDPR
    • Azure Blueprints
  • Cost Considerations
    • Azure Pricing Calculator
    • Retail Pricing Versus Enterprise Agreements
    • Budgeting Examples
  • Quota Problems
    • Please, Sir, Can I Have Some More (vCPUs)?
  • Getting General Support
• Conclusion
  • Looking Backward
    • Baby Azure
  • What Else?
    • Using Other Web-Based Bioinformatics Platforms
  • Looking Forward
    • Cheaper Sequencing = More Data
• Index

pokaż cały spis treści

ukryj recenzje