Google Life Sciences

  • Author: Ronald Fung

  • Creation Date: 9 June 2023

  • Next Modified Date: 9 June 2024

A. Introduction

Cloud Life Sciences is a suite of services and tools for managing, processing, and transforming life sciences data. It also enables advanced insights and operational workflows using highly scalable and compliant infrastructure. Cloud Life Sciences includes the Cloud Life Sciences API, the open source Variant Transforms tool, and more. Learn more

B. How is it used at Seagen

Seagen can use Google Cloud Life Sciences to perform genomics and bioinformatics analysis on their biopharma research data. Here are some steps to get started with Google Cloud Life Sciences:

  1. Create a Google Cloud account: Seagen can create a Google Cloud account in the Google Cloud Console. This will give them access to Google Cloud Life Sciences and other Google Cloud services.

  2. Create a Life Sciences project: Seagen can create a Life Sciences project in the Google Cloud Console, which represents a workspace for genomics and bioinformatics analysis. They can specify the project name, region, and other project settings.

  3. Import the research data: Seagen can import the research data into Google Cloud Life Sciences, using a variety of data sources, such as FASTQ files, BAM files, or VCF files. They can specify the data source location, format, and schema.

  4. Perform genomics analysis: Seagen can perform genomics analysis on the research data using Google Cloud Life Sciences’ powerful tools and services, such as Google Genomics, DeepVariant, or GATK. They can analyze the genomic data to identify variants, mutations, and other genetic information.

  5. Perform bioinformatics analysis: Seagen can perform bioinformatics analysis on the research data using other Google Cloud services, such as Google Cloud AI Platform or Google Cloud BigQuery. They can perform data analytics, machine learning, and predictive modeling on the research data, to gain insights and drive business decisions.

  6. Store and manage the data: Seagen can store and manage the genomics and bioinformatics data using Google Cloud Life Sciences’ powerful tools and services, such as Google Cloud Storage or Cloud SQL. They can configure the data storage and access policies, set up data backups, and monitor the data usage.

Overall, by using Google Cloud Life Sciences, Seagen can perform genomics and bioinformatics analysis on their biopharma research data quickly and efficiently, and gain insights into the genetics and biology of their research targets. With its support for different genomics and bioinformatics data formats, powerful data management capabilities, and easy-to-use interface, Google Cloud Life Sciences is an excellent choice for businesses and individuals who need to perform large-scale genomics and bioinformatics analysis.

C. Features

Google Cloud Life Sciences is a powerful platform for genomics and bioinformatics analysis that offers a range of key features. Here are some of the key features of Google Cloud Life Sciences:

  1. Scalability: Google Cloud Life Sciences is highly scalable, allowing users to perform genomics and bioinformatics analysis on large-scale datasets quickly and efficiently.

  2. Genomics analysis tools: Google Cloud Life Sciences offers a range of genomics analysis tools, including Google Genomics, DeepVariant, and GATK, that allow users to analyze genomic data to identify variants, mutations, and other genetic information.

  3. Bioinformatics analysis tools: Google Cloud Life Sciences offers a range of bioinformatics analysis tools, including Google Cloud AI Platform and Google Cloud BigQuery, that allow users to perform data analytics, machine learning, and predictive modeling on the research data.

  4. Data management: Google Cloud Life Sciences offers powerful data management tools, including Google Cloud Storage and Cloud SQL, that allow users to store and manage their genomics and bioinformatics data securely and efficiently.

  5. Collaboration: Google Cloud Life Sciences offers collaboration tools, including Google Drive and Google Docs, that allow users to collaborate on research projects in real-time.

  6. Compliance: Google Cloud Life Sciences is compliant with healthcare regulations, such as HIPAA, and offers a range of security and compliance controls, including encryption, auditing, and monitoring.

Overall, Google Cloud Life Sciences is a powerful platform for genomics and bioinformatics analysis that offers a range of key features, including scalability, genomics and bioinformatics analysis tools, data management, collaboration, and compliance. By using Google Cloud Life Sciences, businesses and individuals can perform large-scale genomics and bioinformatics analysis quickly and efficiently, and gain insights into the genetics and biology of their research targets.

D. Where Implemented

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E. How it is tested

Testing Google Cloud Life Sciences involves ensuring that the genomics and bioinformatics analysis functions are working correctly, and that the data is properly secured and compliant with healthcare regulations. Here are some steps to test Google Cloud Life Sciences:

  1. Create a test research data source: Create a test research data source that mimics the production data source as closely as possible, including the data format, schema, and metadata.

  2. Create a test Life Sciences project: Create a test Life Sciences project that mimics the production project as closely as possible, including the project configuration, region, and other settings.

  3. Import the research data: Import the test research data into Google Cloud Life Sciences, using the same data source location, format, and schema as the production data.

  4. Perform genomics analysis: Perform genomics analysis on the test research data using Google Cloud Life Sciences’ powerful tools and services, such as Google Genomics, DeepVariant, or GATK. Analyze the genomic data to identify variants, mutations, and other genetic information.

  5. Perform bioinformatics analysis: Perform bioinformatics analysis on the test research data using other Google Cloud services, such as Google Cloud AI Platform or Google Cloud BigQuery. Perform data analytics, machine learning, and predictive modeling on the research data to gain insights and drive business decisions.

  6. Verify the data compliance: Verify that the test research data is compliant with healthcare regulations, such as HIPAA. Ensure that the data is properly secured, encrypted, and accessible only to authorized users.

  7. Repeat the process: Repeat the process as needed, creating additional test research data sources and analysis scenarios to test different data formats or to simulate different research data analysis scenarios.

Overall, by thoroughly testing Google Cloud Life Sciences, users can ensure that their genomics and bioinformatics analysis pipeline is reliable, scalable, and compliant with healthcare regulations. Additionally, users can reach out to Google Cloud support for help with any technical challenges they may encounter.

F. 2023 Roadmap

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G. 2024 Roadmap

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H. Known Issues

While Google Cloud Life Sciences is a reliable and powerful platform for genomics and bioinformatics analysis, there are some known issues that users may encounter. Here are some of the known issues for Google Cloud Life Sciences:

  1. Performance issues: Users may encounter performance issues with Google Cloud Life Sciences, such as slow data processing times or high resource utilization. These issues can often be resolved by optimizing the project configuration, such as using the appropriate machine types or adjusting the project settings.

  2. Data consistency issues: Users may encounter data consistency issues with Google Cloud Life Sciences, such as data corruption or data loss. These issues can often be resolved by using the appropriate data sources, such as durable storage systems, and implementing data validation and error handling mechanisms.

  3. Access control issues: Users may encounter access control issues with Google Cloud Life Sciences, such as unauthorized access or data breaches. These issues can often be resolved by configuring the appropriate access control policies, such as role-based access control or attribute-based access control.

  4. Compliance issues: Users may encounter compliance issues with Google Cloud Life Sciences, such as non-compliance with healthcare regulations, such as HIPAA. These issues can often be resolved by implementing the appropriate security and compliance controls, such as encryption, auditing, and monitoring.

  5. Integration issues: Users may encounter integration issues with Google Cloud Life Sciences, such as interoperability issues or compatibility issues with other genomics and bioinformatics systems. These issues can often be resolved by using the appropriate integration standards, such as FHIR or HL7, and ensuring that the research data is compatible with other genomics and bioinformatics systems.

Overall, while these issues may impact some users, Google Cloud Life Sciences remains a reliable and powerful platform for genomics and bioinformatics analysis that is widely used by researchers, scientists, and healthcare providers. By monitoring their Google Cloud Life Sciences usage and reviewing their usage reports and logs, users can ensure that their research data is secure and accessible, and that they are complying with healthcare regulations and standards. Additionally, users can reach out to Google Cloud support for help with any known issues or other technical challenges they may encounter.

[x] Reviewed by Enterprise Architecture

[x] Reviewed by Application Development

[x] Reviewed by Data Architecture