High-throughput genomics has revolutionized molecular biology research and its applications in human health. The demand for advanced computing infrastructure, the immense data size, the spirit of collaboration, and the need for long-term storage have compelled organizations to embrace cloud computing. Initial adoption was slow because of technical issues, latency, network glitches, and data security and privacy concerns. However, recent advancements in cloud technologies have simplified cloud migration and kept pace with developments in genomics. When it comes to genomics applications, several public cloud vendors offer different features and capabilities tailored to genomics research. In order to fully leverage the benefits of cloud computing while maintaining optimal performance and minimizing time and resource investment, it is imperative to possess a comprehensive understanding of cloud capabilities and limitations and select the most suitable solution. This article provides a detailed guide for migrating your genomics applications to the cloud.

Understand the broader capabilities of public cloud vendors

All three major public cloud vendors - Google (GCP), Amazon (AWS), and Microsoft (Azure) - offer similar cloud services. General service offerings include scalable data storage services, computing servers, data querying capabilities such as BigQuery (GCP) and Athena (AWS) for querying large genomics datasets, and Machine Learning features for building predictive models. Additionally, users can utilize serverless computing options, such as AWS Lambda or GCP Cloud Functions, to run small, event-driven genomics tasks. These features automatically handle scaling of the number of execution environments for efficient resource allocations.

Understand your data

Understanding the data is crucial in determining the necessary resources, establishing guardrails, and optimizing usage and costs. Genomics data comes in various formats, such as sequencing files (fastq), variant formats (BED, VCF), and sizes (ranging from small MBs to TBs). First, map different data formats to usage – determine if the files are used for routine analysis, quick access, or long-term storage. Be aware that some public repository data, including annotations, pathways, and gene targets, is available in the public clouds (such as AWS/GCP), and duplicating it into your account is unnecessary. Second, understand the regional government and regulatory guidelines that dictate data storage requirements in public/private clouds or on-premises. Finally, comprehending the purpose of the data generation - whether it's for research use, product development, drug development, or clinical trials - helps in determining data encoding and anonymization requirements and obtaining the appropriate permissions from the primary data source prior to the cloud migration.

Evaluate your computing infrastructure needs exhaustively

Although cloud infrastructure offers numerous capabilities for data storage, computing, workflow management, and application development, users must ascertain their reasons for cloud migration.

If you view the cloud as a data storage resource, it can be a perfect scalable solution, delivering high reliability (99.99%), especially for storing voluminous genome data files. As a data custodian, there are several factors to consider before making a choice.

  1. Data Access: Cloud vendors determine costs based on data access frequency. Consider deep storage options versus frequent usage like Amazon S3 if you need a long-term data storage solution.
  2. Back-up: Despite the cloud's reliability, it's advisable to back up data in multiple locations and potentially on-premises if you are handling sensitive data or to meet regulatory obligations.

Genomic data processing requires Linux capabilities. If you seek to scale up computing power, cloud providers offer a range of options - such as general-purpose, memory-optimized, and compute-optimized servers. If your tools are compatible, consider using highly powerful GPU nodes, which can significantly enhance performance. Bioinformaticians should balance costs and requirements by optimizing computing usage.

Beyond data storage and computing needs, bioinformaticians can utilize a host of other services like private git repositories for version control, container registry services for managing containers, and serverless workflow orchestrations.

Consider data Security and privacy issues

Genomics data is sensitive, can be traced to the origin of the biological entity, and can be exploited for illegal activities. Furthermore, human genomic data must be protected according to country-specific regulations such as the EU's GDPR and the USA's HIPAA. Non-human genomic data should also be safeguarded to protect proprietary and intellectual property rights.

As an initial line of defense, take advantage of the wide range of security features public cloud vendors offer. However, remember that the ultimate data security responsibility lies with the data custodians and the organizations handling the data. Here are a few simple rules to help mitigate data security concerns:

  1. Implement technical solutions such as homomorphic encryption and public-private allele masking.
  2. Store genomics data and metadata separately.
  3. Set up data access controls to limit data exposure to authorized technicians.

Look beyond the free tier options and get familiar with hidden charges

Unlike local hardware, every cloud service incurs charges. Most services offer a free tier option that enables you to familiarize yourself with the service, assess its performance, and understand its relevance to your use case. However, to fully explore the cloud's salient capability - scalability - you may need to operate beyond the free tier. These costs can be significantly higher than on-premises solutions because the charges are calculated at the smallest possible units.

Further, other unexpected and hidden charges could impact your monthly budget planning. For instance, AWS EBS needs to be mounted along with the computing servers (EC2), and data transfers between different storage services and downloads incur charges. These costs are just two examples of potential surprises.

Data compression for migration and storage

Uploading large volumes of genomics data to the cloud takes significant time and computing resources to store. Data compression can mitigate both problems. Raw genomic and intermediate data files are seldom useful after analysis completion, and hence, we suggest deleting these files or compressing them to minimize the storage needs. Depending on the requirements, genomic data can be compressed many fold without losing the vital information. Several data compression tools are available, and it’s crucial to test and verify them on a test dataset to confirm the functionality before applying them to your entire dataset.

Data migration between services requires careful monitoring to ensure data completeness. Cloud vendors provide default features to ensure complete data migration and allow multipart upload to speed up data migration. As genomics data custodians, we highly encourage bioinformaticians to use md5 checksums to confirm data integrity.

Explore the prebuilt options

The public cloud vendors (e.g., Amazon Omics) offer prebuilt solutions for specific genomics applications, and several third-party companies offer SaaS solutions built on public cloud services. However, it’s important to evaluate all the available options before making the choices, as these solutions require long-term commitments and potentially cost significantly. An ideal prebuilt solution should include features for data management, workflow management, analytics integrity, data validations, and visualizations and should minimize the overall resource and computing footprint. Caution must be observed as all solutions are not equal - some are visually appealing but could require significant time and resources to develop custom applications.

Cost effectiveness strategies

Familiarize yourself with cloud infrastructure or consult an expert to explore opportunities to minimize the establishment and operating costs. Here are a few simple ways to optimize the costs.

  1. Move data to cold storage services frequently.
  2. Reserve computing power with annual pre-commitments.
  3. Use spot virtual machines when appropriate.
  4. Monitor and fine-tune your application's performance regularly to identify potential bottlenecks and optimize resource utilization.
  5. Importantly, analyze the breakdown of costs by services. This practice will help you better understand where your money is being spent.

Conclusion

It is crucial to thoroughly evaluate the factors discussed above. Doing so will enable you to make a well-informed decision when choosing the right cloud solutions for your genomics applications. This thoughtful approach will empower you to harness the full potential of cloud infrastructure, resulting in accelerated analysis, enhanced scalability, and optimal cost-efficiency.

Stanome offers a comprehensive range of cloud solutions, enabling organizations to construct and oversee their tailored platforms effortlessly. Our primary objective is simplifying and optimizing genomics workflows, empowering researchers to unearth groundbreaking discoveries. Let us join forces and unlock the full potential of genomics in the cloud. Contact our expert teams today at info@stanome.com to discover how we can fulfill your specific needs.