Osoby które kupowały "Genomics in the Cloud. Using Docker, GATK, and WDL in Terra", wybierały także:

• Docker. Kurs video. ZostaÅ„ administratorem systemów IT 119,00 zÅ‚, (53,55 zÅ‚ -55%)
• Docker. Kurs video. Praca z systemem konteneryzacji i Docker Swarm 89,00 zÅ‚, (40,05 zÅ‚ -55%)
• DevOps dla zdesperowanych. Praktyczny poradnik przetrwania 67,00 zÅ‚, (33,50 zÅ‚ -50%)
• DevOps w praktyce. Kurs video. Jenkins, Ansible, Terraform i Docker 198,98 zÅ‚, (99,49 zÅ‚ -50%)
• Kubernetes. Tworzenie niezawodnych system 69,00 zÅ‚, (34,50 zÅ‚ -50%)

Spis treści

Genomics in the Cloud. Using Docker, GATK, and WDL in Terra eBook -- spis treści

• Foreword
• Preface
  • Purpose, Scope, and Intended Audience of This Book
    • What You Will Learn from This Book
    • What Computational Experience Is Needed for the Exercises?
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introduction
  • The Promises and Challenges of Big Data in Biology and Life Sciences
  • Infrastructure Challenges
  • Toward a Cloud-Based Ecosystem for Data Sharing and Analysis
    • Cloud-Hosted Data and Compute
    • Platforms for Research in the Life Sciences
    • Standardization and Reuse of Infrastructure
  • Being FAIR
  • Wrap-Up and Next Steps
• 2. Genomics in a Nutshell: A Primer for Newcomers to the Field
  • Introduction to Genomics
    • The Gene as a Discrete Unit of Inheritance (Sort Of)
    • The Central Dogma of Biology: DNA to RNA to Protein
    • The Origins and Consequences of DNA Mutations
    • Genomics as an Inventory of Variation in and Among Genomes
    • The Challenge of Genomic Scale, by the Numbers
  • Genomic Variation
    • The Reference Genome as Common Framework
    • Physical Classification of Variants
      • Single-nucleotide variants
      • Insertions and deletions
      • Copy-number variants
      • Structural variants
    • Germline Variants Versus Somatic Alterations
      • Germline
      • Somatic
  • High-Throughput Sequencing Data Generation
    • From Biological Sample to Huge Pile of Read Data
      • High-throughput sequencing data formats
    • Types of DNA Libraries: Choosing the Right Experimental Design
      • Amplicon preparation
      • Whole genome preparation
      • Target enrichment: gene panels and exomes
      • Whole genome versus exome
  • Data Processing and Analysis
    • Mapping Reads to the Reference Genome
    • Variant Calling
    • Data Quality and Sources of Error
      • Contamination and sample swaps
      • Biochemical, physical, and software artifacts
    • Functional Equivalence Pipeline Specification
  • Wrap-Up and Next Steps
• 3. Computing Technology Basics for Life Scientists
  • Basic Infrastructure Components and Performance Bottlenecks
    • Types of Processor Hardware: CPU, GPU, TPU, FPGA, OMG
    • Levels of Compute Organization: Core, Node, Cluster, and Cloud
      • Low level: core
      • Mid level: node/machine
      • Top level: cluster and cloud
    • Addressing Performance Bottlenecks
      • Data storage and I/O operations: hard drive versus solid state
      • Memory: cache or crash
      • Specialized hardware and code optimizations: navigating the trade-offs
  • Parallel Computing
    • Parallelizing a Simple Analysis
    • From Cores to Clusters and Clouds: Many Levels of Parallelism
    • Trade-Offs of Parallelism: Speed, Efficiency, and Cost
  • Pipelining for Parallelization and Automation
    • Workflow Languages
    • Popular Pipelining Languages for Genomics
    • Workflow Management Systems
  • Virtualization and the Cloud
    • VMs and Containers
    • Introducing the Cloud
      • Clouds are not fluffy
      • Evolution of cloud infrastructure and services
      • Pros and cons of the cloud
    • Categories of Research Use Cases for Cloud Services
      • Lightweight development: Google Cloud Shell
      • Intermediate development and analysis: single VM
      • Batch analysis: multiple VMs via batch services
      • Framework analysis: multiple VMs via framework services
  • Wrap-Up and Next Steps
• 4. First Steps in the Cloud
  • Setting Up Your Google Cloud Account and First Project
    • Creating a Project
    • Checking Your Billing Account and Activating Free Credits
  • Running Basic Commands in Google Cloud Shell
    • Logging in to the Cloud Shell VM
    • Using gsutil to Access and Manage Files
    • Pulling a Docker Image and Spinning Up the Container
    • Mounting a Volume to Access the Filesystem from Within the Container
  • Setting Up Your Own Custom VM
    • Creating and Configuring Your VM Instance
      • Name your VM
      • Choose a region (important!) and zone (not so important)
      • Select a machine type
      • Specify a container? (nope)
      • Customize the boot disk
    • Logging into Your VM by Using SSH
    • Checking Your Authentication
    • Copying the Book Materials to Your VM
    • Installing Docker on Your VM
    • Setting Up the GATK Container Image
    • Stopping Your VMto Stop It from Costing You Money
  • Configuring IGV to Read Data from GCS Buckets
  • Wrap-Up and Next Steps
• 5. First Steps with GATK
  • Getting Started with GATK
    • Operating Requirements
    • Command-Line Syntax
    • Multithreading with Spark
    • Running GATK in Practice
      • Docker setup and test invocation
      • Running a real GATK command
      • Running a Picard command within GATK4
  • Getting Started with Variant Discovery
    • Calling Germline SNPs and Indels with HaplotypeCaller
      • HaplotypeCaller in a nutshell
      • Running HaplotypeCaller and examining the output
      • Generating an output BAM to troubleshoot a surprising call
    • Filtering Based on Variant Context Annotations
      • Understanding variant context annotations
      • Plotting variant context annotation data
      • Applying hard filters to germline SNPs and indels
  • Introducing the GATK Best Practices
    • Best Practices Workflows Covered in This Book
      • Other Major Use Cases
  • Wrap-Up and Next Steps
• 6. GATK Best Practices for Germline Short Variant Discovery
  • Data Preprocessing
    • Mapping Reads to the Genome Reference
    • Marking Duplicates
    • Recalibrating Base Quality Scores
  • Joint Discovery Analysis
    • Overview of the Joint Calling Workflow
    • Calling Variants per Sample to Generate GVCFs
    • Consolidating GVCFs
    • Applying Joint Genotyping to Multiple Samples
    • Filtering the Joint Callset with Variant Quality Score Recalibration
    • Refining Genotype Assignments and Adjusting Genotype Confidence
    • Next Steps and Further Reading
  • Single-Sample Calling with CNN Filtering
    • Overview of the CNN Single-Sample Workflow
    • Applying 1D CNN to Filter a Single-Sample WGS Callset
    • Applying 2D CNN to Include Read Data in the Modeling
  • Wrap-Up and Next Steps
• 7. GATK Best Practices for Somatic Variant Discovery
  • Challenges in Cancer Genomics
  • Somatic Short Variants (SNVs and Indels)
    • Overview of the Tumor-Normal Pair Analysis Workflow
    • Creating a Mutect2 PoN
    • Running Mutect2 on the Tumor-Normal Pair
    • Estimating Cross-Sample Contamination
    • Filtering Mutect2 Calls
    • Annotating Predicted Functional Effects with Funcotator
  • Somatic Copy-Number Alterations
    • Overview of the Tumor-Only Analysis Workflow
    • Collecting Coverage Counts
    • Creating a Somatic CNA PoN
    • Applying Denoising
    • Performing Segmentation and Call CNAs
    • Additional Analysis Options
      • Tumor-Normal pair analysis
      • Allelic copy ratio analysis
  • Wrap-Up and Next Steps
• 8. Automating Analysis Execution with Workflows
  • Introducing WDL and Cromwell
  • Installing and Setting Up Cromwell
  • Your First WDL: Hello World
    • Learning Basic WDL Syntax Through a Minimalist Example
    • Running a Simple WDL with Cromwell on Your Google VM
    • Interpreting the Important Parts of Cromwells Logging Output
    • Adding a Variable and Providing Inputs via JSON
    • Adding Another Task to Make It a Proper Workflow
  • Your First GATK Workflow: Hello HaplotypeCaller
    • Exploring the WDL
    • Generating the Inputs JSON
    • Running the Workflow
    • Breaking the Workflow to Test Syntax Validation and Error Messaging
  • Introducing Scatter-Gather Parallelism
    • Exploring the WDL
    • Generating a Graph Diagram for Visualization
  • Wrap-Up and Next Steps
• 9. Deciphering Real Genomics Workflows
  • Mystery Workflow #1: Flexibility Through Conditionals
    • Mapping Out the Workflow
      • Generating the graph diagram
      • Identifying the code that corresponds to the diagram components
    • Reverse Engineering the Conditional Switch
      • How is the conditional logic set up?
      • Does the conditional interfere with any assumptions were making anywhere else?
      • How does the next task know what to run on?
      • Can we use conditionals to manage default settings?
  • Mystery Workflow #2: Modularity and Code Reuse
    • Mapping Out the Workflow
      • Generating the graph diagram
      • Identifying the code that corresponds to the diagram components
    • Unpacking the Nesting Dolls
      • What is the structure of a subworkflow?
      • Where are the tasks defined?
      • How is the subworkflow wired up?
  • Wrap-Up and Next Steps
• 10. Running Single Workflows at Scale with Pipelines API
  • Introducing the GCP Genomics Pipelines API Service
    • Enabling Genomics API and Related APIs in Your Google Cloud Project
  • Directly Dispatching Cromwell Jobs to PAPI
    • Configuring Cromwell to Communicate with PAPI
    • Running Scattered HaplotypeCaller via PAPI
    • Monitoring Workflow Execution on Google Compute Engine
  • Understanding and Optimizing Workflow Efficiency
    • Granularity of Operations
    • Balance of Time Versus Money
    • Suggested Cost-Saving Optimizations
      • Dynamic sizing for resource allocation
      • File streaming to GATK4 tools
      • Preemptible VM instances
    • Platform-Specific Optimization Versus Portability
  • Wrapping Cromwell and PAPI Execution with WDL Runner
    • Setting Up WDL Runner
    • Running the Scattered HaplotypeCaller Workflow with WDL Runner
    • Monitoring WDL Runner Execution
  • Wrap-Up and Next Steps
• 11. Running Many Workflows Conveniently in Terra
  • Getting Started with Terra
    • Creating an Account
    • Creating a Billing Project
    • Cloning the Preconfigured Workspace
  • Running Workflows with the Cromwell Server in Terra
    • Running a Workflow on a Single Sample
    • Running a Workflow on Multiple Samples in a Data Table
    • Monitoring Workflow Execution
    • Locating Workflow Outputs in the Data Table
    • Running the Same Workflow Again to Demonstrate Call Caching
  • Running a Real GATK Best Practices Pipeline at Full Scale
    • Finding and Cloning the GATK Best Practices Workspace for Germline Short Variant Discovery
    • Examining the Preloaded Data
    • Selecting Data and Configuring the Full-Scale Workflow
    • Launching the Full-Scale Workflow and Monitoring Execution
    • Options for Downloading Output Dataor Not
  • Wrap-Up and Next Steps
• 12. Interactive Analysis in Jupyter Notebook
  • Introduction to Jupyter in Terra
    • Jupyter Notebooks in General
    • How Jupyter Notebooks Work in Terra
      • Overview
      • Accessing data
      • Saving, stopping, and restarting
      • Customizing your notebooks computing environment
      • Sharing and collaboration
  • Getting Started with Jupyter in Terra
    • Inspecting and Customizing the Notebook Runtime Configuration
    • Opening Notebook in Edit Mode and Checking the Kernel
    • Running the Hello World Cells
      • Python Hello World
      • R Hello World using Python magic methods
      • Command-line tool Hello World using Python magic methods
    • Using gsutil to Interact with Google Cloud Storage Buckets
    • Setting Up a Variable Pointing to the Germline Data in the Book Bucket
    • Setting Up a Sandbox and Saving Output Files to the Workspace Bucket
  • Visualizing Genomic Data in an Embedded IGV Window
    • Setting Up the Embedded IGV Browser
    • Adding Data to the IGV Browser
    • Setting Up an Access Token to View Private Data
  • Running GATK Commands to Learn, Test, or Troubleshoot
    • Running a Basic GATK Command: HaplotypeCaller
    • Loading the Data (BAM and VCF) into IGV
    • Troubleshooting a Questionable Variant Call in the Embedded IGV Browser
  • Visualizing Variant Context Annotation Data
    • Exporting Annotations of Interest with VariantsToTable
    • Loading R Script to Make Plotting Functions Available
    • Making Density Plots for QUAL by Using makeDensityPlot
    • Making a Scatter Plot of QUAL Versus DP
    • Making a Scatter Plot Flanked by Marginal Density Plots
  • Wrap-Up and Next Steps
• 13. Assembling Your Own Workspace in Terra
  • Managing Data Inside and Outside of Workspaces
    • The Workspace Bucket as Data Repository
    • Accessing Private Data That You Manage Outside of Terra
    • Accessing Data in the Terra Data Library
  • Re-Creating the Tutorial Workspace from Base Components
    • Creating a New Workspace
    • Adding the Workflow to the Methods Repository and Importing It into the Workspace
    • Creating a Configuration Quickly with a JSON File
    • Adding the Data Table
    • Filling in the Workspace Resource Data Table
    • Creating a Workflow Configuration That Uses the Data Tables
    • Adding the Notebook and Checking the Runtime Environment
    • Documenting Your Workspace and Sharing It
  • Starting from a GATK Best Practices Workspace
    • Cloning a GATK Best Practices Workspace
    • Examining GATK Workspace Data Tables to Understand How the Data Is Structured
    • Getting to Know the 1000 Genomes High Coverage Dataset
    • Copying Data Tables from the 1000 Genomes Workspace
    • Using TSV Load Files to Import Data from the 1000 Genomes Workspace
    • Running a Joint-Calling Analysis on the Federated Dataset
  • Building a Workspace Around a Dataset
    • Cloning the 1000 Genomes Data Workspace
    • Importing a Workflow from Dockstore
    • Configuring the Workflow to Use the Data Tables
  • Wrap-Up and Next Steps
• 14. Making a Fully Reproducible Paper
  • Overview of the Case Study
    • Computational Reproducibility and the FAIR Framework
    • Original Research Study and History of the Case Study
    • Assessing the Available Information and Key Challenges
    • Designing a Reproducible Implementation
  • Generating a Synthetic Dataset as a Stand-In for the Private Data
    • Overall Methodology
    • Retrieving the Variant Data from 1000 Genomes Participants
    • Creating Fake Exomes Based on Real People
    • Mutating the Fake Exomes
    • Generating the Definitive Dataset
  • Re-Creating the Data Processing and Analysis Methodology
    • Mapping and Variant Discovery
    • Variant Effect Prediction, Prioritization, and Variant Load Analysis
    • Analytical Performance of the New Implementation
  • The Long, Winding Road to FAIRness
  • Final Conclusions
• Glossary
• Index