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2022 Blogs, Research Gateway, Blog, DevOps Blog, Featured

Automated deployment of software makes the process faster, easier, repeatable, and more supportable. A variety of technologies are available for deployment, but you need not necessarily choose a complex automation approach to reap the benefits. In this blog, we will cover how Relevance Lab approached using automation for the deployment of their RLCatalyst Research Gateway solution.

RLCatalyst Research Gateway solution from Relevance Lab provides a next-generation cloud-based platform for collaborative scientific research on AWS with access to research tools, data sets, processing pipelines, and analytics workbenches in a frictionless manner. The solution can be used in the Software as a Service (SaaS) mode, or it can be deployed in customers’ accounts in the enterprise mode. It takes less than 30 minutes to launch a working environment for Principal Investigators and Researchers with security, scalability, and cost governance.


During the deployment of this solution, several AWS resources are created:

  • Networking (VPC, Public and private subnets, Internet and NAT Gateways, ALB)
  • Security (Security Groups, Cognito Userpool for authentication, Identity and Acess Management (IAM) Roles and Policies)
  • Database (AWS DocumentDB cluster)
  • EC2 Compute
  • EC2 Image Builder pipelines
  • S3 Buckets (storage)
  • AWS Service Catalog products and portfolios

When such a variety of resources are to be created, there are several benefits of automating the deployment.

  • Faster Deployment: It takes an engineer at least a few hours to deploy all the resources manually, assuming everything works to plan. If errors are encountered, it takes longer. With an automated deployment, the process is much quicker, and it can be done in 15-30 minutes.
  • Easier: The deployment automation encapsulates and hides a lot of the complexity of the process, and the engineer performing the task does not need to know a lot of the different technologies in depth. Also, since the automation has been hardened over time through repeated testing in the lab, much of the error handling has been codified within the scripts.
  • Repeatable: The deployment done via automation always comes out exactly as designed. Unlike manual deployment, where unforced user errors can creep in, the scripts perform each run exactly the same. Also, scripts can be coded to fix broken installs or redeploy solution software.
  • Supportable: Automation scripts can have logging, which makes it easy for support personnel to help in case things don’t go as planned.

There are many technologies that can help automate the deployment of software. These include tools like Chef and Ansible, language-specific package managers like PyPI or npm, and Infrastructure as Code (IaC) tools like CloudFormation or Terraform. For RLCatalyst Research Gateway, which is built on AWS, we picked CloudFormation Templates (CFT) for our IaC needs in combination with plain old shell scripts. Find our deployment scripts on Github.


  • Pre-requisites: We deploy Research Gateway in a standard Virtual Private Cloud (VPC) architecture with both public and private subnets. This VPC can be created using a quickstart available from AWS itself.
  • Infrastructure: The infrastructure is created as five different stacks.
    • Amazon S3 bucket: This is used to hold all the deployment artifacts like CFT templates.
    • AWS Cognito UserPool: This is used for authentication.
    • AWS DocumentDB: This is used to store all persistent data required by Research Gateway.
    • Amazon EC2 Image Builder: Pipelines are created to rebuild Amazon Machine Image (AMI) for the standard catalog items that are AMI-based. This ensures that the AMIs have the latest patches and security fixes.
    • Amazon EC2 (main stack): This hosts the Research Gateway portal.
  • Configuration: Some of the instance-specific data is part of the configuration, which is stored in one of the following ways.
    • Files: Configuration files are created during the deployment process, using data provided at the time. These files are referred by the solution software to customize its behavior. File-based configurations are easier to access for support personnel and can be easily checked in case the solution software is not behaving as expected.
    • Database Entries: A configs collection in the database hosts some of the information. Ideally, all configurations can reside in the database, but because the database is encrypted and has restricted access, we prefer to keep some of the configurations outside the DB.
    • AWS Systems Manager (SSM) Parameter Store: Some configurations, especially those related to AMIs, which are resolved by CFTs at run-time, are maintained in the AWS SSM Parameter store.
  • Research Gateway Solution Software: Distributed as docker images via AWS Elastic Container Registry (ECR). This allows us to distribute the solution software privately to the customers’ AWS accounts. Our solution software runs as a set of docker services. A variation of the deployment script can also deploy this as services into AWS Elastic Kubernetes Service.
  • Load-balancing: The EC2 instances deployed register themselves with Target Groups, and an Application Load Balancer serves the application securely over SSL using certificates hosted in AWS Certificate Manager.

Once the solution software is deployed, and the portal is running and reachable, the first user (an Admin role) is created using a script. Using that Administrator user credentials, the rest of the onboarding process can be completed by the customer from the UI.

Summary
Using the automated deployment process, an instance of the RLCatalyst Research Gateway can be provisioned and configured in less than 30 minutes. This allows customers to start using the solution quickly and derive maximum benefits from their investment with minimum effort.

If you would like to launch your scientific research environment in less than 30 minutes with RLCatalyst Research Gateway or would like to learn more about it, write to us at marketing@relevancelab.com.

References
Architecting a Cloud-based Application with AWS Best Practices
Enabling Frictionless Scientific Research in the Cloud with a 30 Minutes Countdown Now!



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2022 Blogs, Research Gateway, Blog, Featured

As a researcher, do you want to get started in minutes to run any complex genomics pipeline with large data sets without worrying about hours to set up the environment, dealing with large data sets availability & storage, security of your cloud infrastructure, and most of all unknown expenses? RLCatalyst makes your life simpler, and in this blog, we will cover how easy it is to use publicly available Genomics pipelines from nf-co.re using Nextflow on your AWS Cloud environment with ease.

There are a number of open-source tools available for researchers driving re-use. However, what Research Institutions and Genomics companies are looking for is a right balance on three key dimensions before adopting cloud in a large scale manner for internal use:

  • Cost and Budget Governance: Strong focus on Cost Tracking of Cloud resources to track, analyze, control, and optimize budget spends.
  • Research Data & Tools Easy Collaboration: Principal Investigators and researchers need to focus on data management, governance, and privacy along with analysis and collaboration in real-time without worrying about Cloud complexity.
  • Security and Compliance: Research requires a strong focus on security and compliance covering Identity management, data privacy, audit trails, encryption, and access management.

To make sure the above functionalities do not slow down researchers from focussing on Science due to complexities of infrastructure, Research Gateway provides the reliable solution by automating cost & budget tracking with safe-guards and providing a simple self-service model for collaboration. We will demonstrate in this blog how researchers can use a vast set of publicly available tools, pipelines and data easily on this platform with tight budget controls. Here is a quick video of the ease with which researchers can get started in a frictionless manner.

nf-co.re is a community effort to collect a curated set of analysis pipelines built using Nextflow. The key aspects of these pipelines are that these pipelines adhere to strict guidelines that ensure they can be reused extensively. These pipelines have following advantages:


  • Cloud-Ready – Pipelines are tested on AWS after every release. You can even browse results live on the website and use outputs for your own benchmarking.
  • Portable and reproducible – Pipelines follow best practices to ensure maximum portability and reproducibility. The large community makes the pipelines exceptionally well tested and easy to run.
  • Packaged software – Pipeline dependencies are automatically downloaded and handled using Docker, Singularity, Conda, or others. No need for any software installations.
  • Stable releases – nf-core pipelines use GitHub releases to tag stable versions of the code and software, making pipeline runs totally reproducible.
  • CI testing – Every time a change is made to the pipeline code, nf-core pipelines use continuous integration testing to ensure that nothing has broken.
  • Documentation – Extensive documentation covering installation, usage, and description of output files ensures that you won’t be left in the dark.

Sample of commonly used pipelines that are supported out-of-box in Research Gateway to run with a few clicks and do important genomic analysis. While publicly available repos are easily accessible, it also allows private repositories and custom pipelines to run with ease.


Pipeline Name Description Commonly used for
Sarek Analysis pipeline to detect germline or somatic variants (pre-processing, variant calling, and annotation) from Whole Genome Sequencing (WGS) / targeted sequencing Variant Analysis – workflow designed to detect variants on whole genome or targeted sequencing data
RNA-Seq RNA-Sequencing analysis pipeline using STAR, RSEM, HISAT2, or Salmon with gene/isoform counts and extensive quality control Common basic analysis for RNA-Sequencing with a reference genome and annotation
Dual RNA-Seq Analysis of Dual RNA-Seq data – an experimental method for interrogating host-pathogen interactions through simultaneous RNA-Seq Specifically used for the analysis of Dual RNA-Seq data, interrogating host-pathogen interactions through simultaneous RNA-Seq
Bactopia Bactopia is a flexible pipeline for complete analysis of bacterial genomes Bacterial Genomic Analysis with focus on Food Safety
Viralrecon Assembly and intrahost/low-frequency variant calling for viral samples Supports metagenomics and amplicon sequencing data derived from the Illumina sequencing platform

*The above samples can be launched in less than 5 min and take less than $5 to run with test data and 80% productivity gains achieved.

The figure below shows the building block of this solution on AWS Cloud.


Steps for running nf-core pipeline with Nextflow on AWS Cloud


Steps Details Time Taken
1. Log into RLCatalyst Research Gateway as a Principal Investigator or Researcher profile. Select the project for running Genomics Pipelines, and first time create a new Nextflow Advanced Product. 5 min
2. Select the Input Data location, output data location, pipeline to run (from nf-co.re), and provide parameters (container path, data pattern to use, etc.). Default parameters are already suggested for use of AWS Batch with Spot instances and all other AWS complexities abstracted from end-user for simplicity. 5 min to provision new Nextflow & Nextflow Tower Server on AWS with AWS Batch setup completed with 1-Click
3. Execute Pipeline (using UI interface or by SSH into Head-node) on Nextflow Server. There is ability to run the new pipelines, monitor status, and review outputs from within the Portal UI. Pipelines can take some time to run depending on the size of data and complexity
4. Monitor live pipelines with the 1-Click launch of Nextflow Tower integrated with the portal. Also, view outputs of the pipeline in outputs S3 bucket from within the Portal. Use specialized tools like MultiQC, IGV, and RStudio for further analysis. 5 min
5. All costs related to User, Product, and Pipelines are automatically tagged and can be viewed in the Budgets screen to know the Cloud spend for pipeline execution that includes all resources, including AWS Batch HPC instances dynamically provisioned. Once the pipelines are executed, the existing Cromwell Server can be stopped or terminated to reduce ongoing costs. 5 min

The figure below shows the Nextflow Architecture on AWS.


Summary
nf-co.re community is constantly striving to make Genomics Research in the Cloud simpler. While these pipelines are easily available, running them on AWS Cloud with proper cost tracking, collaboration, data management, and integrated workbench were missing that is now solved by Research Gateway. Relevance Lab, in partnership with AWS, has addressed this need with their Genomics Cloud solution to make scientific research frictionless.

To know more about how you can start your Nextflow nf-co.re pipelines on the AWS Cloud in 30 minutes using our solution at https://research.rlcatalyst.com, feel free to contact marketing@relevancelab.com

References
Enabling Researchers with Next-Generation Sequencing (NGS) Leveraging Nextflow and AWS
Pipelining GATK with WDL and Cromwell on AWS Cloud
Genomics Cloud on AWS with RLCatalyst Research Gateway
Health Informatics and Genomics on AWS with RLCatalyst Research Gateway
Accelerating Genomics and High Performance Computing on AWS with Relevance Lab Research Gateway Solution



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2022 Blogs, Research Gateway, Blog, Featured

Developed in the Data Sciences Platform at the Broad Institute, the Genome Analysis Toolkit (GATK) offers a wide variety of tools with a primary focus on variant discovery and genotyping. Relevance Lab is pleased to offer researchers the ability to run their GATK pipelines on AWS that was missing so far with our Genomics Cloud solution and a 1-click model.

GATK is making scientific research simpler for Genomics by providing best practices workflows and docker containers. The workflows are written in Workflow Description Language (WDL), a user-friendly scripting language maintained by the OpenWDL community. Cromwell is an open-source workflow execution engine that supports WDL as well as CWL, the Common Workflow Language, and can be run on a variety of different platforms, both local and cloud-based. RLCatalyst Research Gateway added support for the Cromwell engine that enables researchers to run any popular workflows on AWS seamlessly. Some of the popular workflows that are available for a quick start are the following:



The figure below shows the building block of this solution on AWS Cloud.


Steps for running GATK with WDL and Cromwell on AWS Cloud


Steps Details Time Taken
1. Log into RLCatalyst Research Gateway as a Principal Investigator or Researcher profile. Select the project for running Genomics Pipelines, and first time create a new Cromwell Advanced Product. 5 min
2. Select the Input Data location, output data location, pipeline to run (from GATK), and provide parameters (input.json). Default parameters are already suggested for the use of AWS Batch with Spot instances and all other AWS complexities, abstracted from the end-user, for simplicity. 5 min to provision new Cromwell Server on AWS with AWS Batch setup completed with 1-Click
3. Execute Pipeline (using UI interface or by SSH into Head-node) on Cromwell Server. There is ability to run the new pipelines, monitor status, and review outputs from within the Portal UI. Pipelines can take some time to run depending on size of data and complexity
4. View outputs of the Pipeline in Outputs S3 bucket from within the Portal. Use specialized tools like MultiQC, Integrative Genomics Viewer (IGV), and RStudio for further analysis. 5 min
5. All costs related to User, Product, and Pipelines are automatically tagged and can be viewed in the budgets screen to know the cloud spend for pipeline execution that consists of all resources, including AWS Batch HPC instances dynamically provisioned. Once the pipelines are executed, the existing Cromwell Server can be stopped or terminated to reduce ongoing costs. 5 min


The figure below shows the ability to select Cromwell Advanced to provision and run any pipeline.


The following picture shows the architecture of Cromwell on AWS.


Summary
GATK community is constantly striving to make Genomics Research in the cloud simpler. So far, the support for AWS Cloud was still missing and was a key ask from multiple online research communities. Relevance Lab, in partnership with AWS, has addressed this need with their Genomics Cloud solution to make scientific research frictionless.

To know more about how you can start your GATK pipelines with WDL and Cromwell on the AWS Cloud in just 30 minutes using our solution at https://research.rlcatalyst.com, feel free to write to marketing@relevancelab.com

References
Accelerating Analytics for the Future of Genomics
Cromwell on AWS
Leveraging AWS HPC for Accelerating Scientific Research on Cloud
Cromwell Documentation
Artificial Intelligence, Machine Learning and Genomics
Accelerating Genomics and High Performance Computing on AWS with Relevance Lab Research Gateway Solution



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2022 Blogs, Research Gateway, Blog, Featured

The pandemic worldwide has highlighted the need for advancing human health faster and new drugs discovery advancement for precision medicines leveraging Genomics. We are building a Genomics Cloud on AWS leveraging convergence of Big Compute, Large Data Sets, AI/ML Analytics engines, and high-performance workflows to make drug discovery more efficient, combining cloud & open source with our products.

Relevance Lab (RL) has been collaborating with AWS Partnership teams over the last one year to create Genomics Cloud. This is one of the dominant use cases for scientific research in the cloud, driven by healthcare and life sciences groups exploring ways to make Genomics analysis better, faster, and cheaper so that researchers can focus on science and not complex infrastructure.

RL offers a product RLCatalyst Research Gateway that facilitates Scientific Research with easier access to big compute infrastructure, large data sets, powerful analytics tools, a secure research environment, and the ability to drive self-service research with tight cost and budget controls.

The top use cases for AWS Genomics in the Cloud are implemented by this product and provide an out-of-the-box solution, significantly saving cost and effort for customers.


Key Building Blocks for Genomics Cloud Architecture
The solution for supporting easy use of Genomics Cloud supports the following key components to meet the need of researchers, scientists, developers, and analysts to efficiently run their experiments without the need for deep expertise in the backend computing capabilities.

Genomics Pipeline Processing Engine
The researchers’ community uses popular open-source tools like NextFlow and Cromwell for large data sets by leveraging HPC systems, and the orchestration layer is managed by tools like Nextflow and Cromwell.

Nextflow is a bioinformatics workflow manager that enables the development of portable and reproducible workflows. It supports deploying workflows on a variety of execution platforms, including local, HPC schedulers, AWS Batch, Google Cloud Life Sciences, and Kubernetes.

Cromwell is a workflow execution engine that simplifies the orchestration of computing tasks needed for Genomics analysis. Cromwell enables Genomics researchers, scientists, developers, and analysts to efficiently run their experiments without the need for deep expertise in the backend computing capabilities.

Many organizations also use commercial tools like Illumina DRAGEN and NVidia Parabricks for similar solutions that are more optimized in reducing processing timelines but also come with a price.

Open Source Repositories for Common Genomics Workflows
The solution needs to allow researchers to leverage work done by different communities and tools to reuse existing available workflows and containers easily. Researchers can leverage any of the existing pipelines & containers or can also create their own implementations by leveraging existing standards.

GATK4 is a Genome Analysis Toolkit for Variant Discovery in High-Throughput Sequencing Data. Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size.

BioContainers – A community-driven project to create and manage bioinformatics software containers.

Dockstore – Dockstore is a free and open source platform for sharing reusable and scalable analytical tools and workflows. It’s developed by the Cancer Genome COLLABORATORY and used by the GA4GH.

nf-core Pipelines – A community effort to collect a curated set of analysis pipelines built using Nextflow.

Workflow Description Language (WDL) is a way to specify data processing workflows with a human-readable and -writeable syntax.

AWS Batch for High Performance Computing
AWS has many services that can be used for Genomics. In this solution, the core architecture is with AWS Batch, a managed service that is built on top of other AWS services, such as Amazon EC2 and Amazon Elastic Container Service (ECS). Also, proper security is provided with Roles via AWS Identity and Access Management (IAM), a service that helps you control who is authenticated (signed in) and authorized (has permissions) to use AWS resources.

Large Data Sets Storage and Access to Open Data Sets
AWS cloud is leveraged to deal with the needs of large data sets for storage, processing, and analytics using the following key products.

Amazon S3 for high-throughput data ingestion, cost-effective storage options, secure access, and efficient searching

AWS DataSync for secure, online service that automates and accelerates moving data between on premises and AWS storage services

AWS Open Datasets Program houses openly available, with 40+ open Life Sciences data repositories

Outputs Analysis and Monitoring Tools
One of the key building blocks for Genomic Data Analysis needs access to common tools like the following integrated into the solution.

MultiQC reports MultiQC searches a given directory for analysis logs and compiles an HTML report. It’s a general-use tool, perfect for summarising the output from numerous bioinformatics tools.

IGV (Integrative Genomics Viewer) is a high-performance, easy-to-use, interactive tool for the visual exploration of genomic data.

RStudio for Genomics since R is one of the most widely-used and powerful programming languages in bioinformatics. R especially shines where a variety of statistical tools are required (e.g., RNA-Seq, population Genomics, etc.) and in the generation of publication-quality graphs and figures.

Genomics Data Lake
AWS Data Lake for creating Genomics data lake for tertiary processing. Once the Secondary analysis generates outputs typically in Variant Calling Format (VCF) for further analysis, there is a need to move such data into a Genomics Data Lake for tertiary processing. Leveraging standard AWS tools and solution framework, a Genomics Data Lake is implemented and integrated with the end-to-end sequencing processing pipeline.

Variant Calling Format specification is used in bioinformatics for storing gene sequence variations, typically in a compressed text file. According to the VCF specification, a VCF file has meta-information lines, a header line, and data lines. Compressed VCF files are indexed for fast data retrieval (random access) of variants from a range of positions.

VCF files, though popular in bioinformatics, are a mixed file type that includes a metadata header and a more structured table-like body. Converting VCF files into the Parquet format works excellently in distributed contexts like a Data Lake.

Cost Analysis of Workflows
One of the biggest concerns for users of Genomic Cloud is control on budget and cost that is provided by RLCatalyst Research Gateway by tracking spends across Projects, Researchers, Workflow runs at a granular level and allowing for optimizing spends by using techniques like Spot instances and on-demand compute. There are guardrails built-in for appropriate controls and corrective actions. Users can run sequencing workflows using their own AWS Accounts, allowing for transparent control and visibility.

Summary
To make large-scale genomic processing in the cloud easier for institutions, principal investigators, and researchers, we provide the fundamental building blocks for Genomics Cloud. The integrated product covers large data sets access, support for popular pipeline engines, access to open source pipelines & containers, AWS HPC environments, analytics tools, and cost tracking that takes away the pains of managing infrastructure, data, security, and costs to enable researchers to focus on science.

To know more about how you can start your Genomic Cloud in the AWS cloud in 30 minutes using our solution at https://research.rlcatalyst.com, feel free to contact marketing@relevancelab.com.

References
High-performance genetic datastore on AWS S3 using Parquet and Arrow
Parallelizing Genome Variant Analysis
Pipelining GATK with WDL and Cromwell
Accelerating Genomics and High Performance Computing on AWS with Relevance Lab Research Gateway Solution



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2021 Blog, Research Gateway, Blog, Featured

We aim to enable the next-generation cloud-based platform for collaborative research on AWS with access to research tools, data sets, processing pipelines, and analytics workbench in a frictionless manner. It takes less than 30 minutes to launch a “MyResearchCloud” working environment for Principal Investigators and Researchers with security, scalability, and cost governance. Using the Software as a Service (SaaS) model is a preferable option for Scientific research in the cloud with tight control on data security, privacy, and regulatory compliances.

Typical top-5 use cases we have found for MyResearchCloud as a suitable solution for unlocking your Scientific Research needs:

  • Need an RStudio solution on AWS Cloud with an ability to connect securely (using SSL) without having to worry about managing custom certificates and their lifecycle
  • Genomic pipeline processing using Nextflow and Nextflow Tower (open source) solution integrated with AWS Batch for easy deployment of open source pipelines and associated cost tracking per researcher and per pipeline
  • Enable researchers with EC2 Linux and Windows servers to install their specific research tools and software. Ability to add AMI based researcher tools (both private and from AWS Marketplace) with 1-click on MyResearchCloud
  • Using SageMaker AI/ML Workbench drive Data research (like COVID-19 impact analysis) with available public data sets already on AWS cloud and create study-specific data sets
  • Enable a small group of Principal Investigator and researchers to manage Research Grant programs with tight budget control, self-service provisioning, and research data sharing

MyResearchCloud is a solution powered by RLCatalyst Research Gateway product and provides the basic environment with access to data, workspaces, analytics workbench, and cloud pipelines, as explained in the figure below.


Currently, it is not easy for research institutes, their IT staff, and a group of principal investigators & researchers to leverage the cloud easily for their scientific research. While there are constraints with on-premise data centers and these institutions have access to cloud accounts, converting a basic account to one with a secured network, secured access, ability to create & publish product/tools catalog, ingress & egress of data, sharing of analysis, tight budget control and other non-trivial tasks divert the attention away from ‘Science’ to ‘Servers’.

We aim to provide a standard catalog for researchers out-of-the-box solution with an ability to also bring your own catalog, as explained in the figure below.


Based on our discussions with research stakeholders, especially small & medium ones, it was clear that the users want something as easy to consume as other consumer-oriented activities like e-shopping, consumer banking, etc. This led to the simplified process of creating a “MyResearchCloud” with the following basic needs:


  • This “MyResearchCloud” is more suitable for smaller research institutions with a single or a few groups of Principal Investigators (PI) driving research with few fellow researchers.
  • The model to set up, configure, collaborate, and consume needs to be extremely simple and comes with pre-built templates, tools, and utilities.
  • PI’s should have full control of their cloud accounts and cost spends with dynamic visibility and smart alerts.
  • At any point, if the PI decides to stop using the solution, there should be no loss to productivity and preservation of existing compute & data.
  • It should be easy to invite other users to collaborate while still controlling their access and security.
  • Users should not be loaded with technical jargon while ordering simple products for day-to-day research using computation servers, data repositories, analysis IDE tools, and Data processing pipelines.

Based on the above ask, the following simple steps have been enabled:


Steps to Launch Activity Total time from Start
Step-1 As a Principal Investigator, create your own “MyResearchCloud” by using your Email ID or Google ID to login the first time on Research Gateway. 1 min
Step-2 If using a personal email ID, get an activation link and login for the first time with a secure password. 4 min
Step-3 Use your own AWS account and provide secure credentials for “MyResearchCloud” consumption. 10 min
Step-4 Create a new Research Project and set up your secure environment with default networking, secure connections, and a standard catalog. You can also leverage your existing setup and catalog. 13 min
Step-5 Invite new researchers or start using the new setup to order your products to get started with a catalog covering data, compute, analytic tools, and workflow pipeline. 15 min
Step-6 Order the necessary products – EC2, S3, Sagemaker/RStudio, Nextflow pipelines. Use the Research Gateway to interact with these tools without the need to access AWS Cloud console for PI and Researchers. 30 min


The picture below shows the easy way to get started with the new Launchpad and 30 minutes countdown.


Architecture Details
To balance the needs of Speed with Compliance, we have designed a unique model to allow Researchers to “Bring your own License” while leveraging the benefits of SaaS in a unique hybrid approach. Our solution provides a “Gateway” model of hub-and-spoke design where we provide and operate the “Hub” while enabling researchers to connect their own AWS Research accounts as a “Spoke”.

Security is a critical part of the SaaS architecture with a hub-and-spoke model where the Research Gateway is hosted in our AWS account using best practices of Cloud Management & Governance controlled by AWS Control Tower while each tenant is created using AWS security best practices of minimum privileges access and role-based access so that no customer-specific keys or data are maintained in the Research Gateway. The architecture and SaaS product are validated as per AWS ISV Path program for Well-Architected principles and data security best practices.

The following diagram explains in more detail the hub-and-spoke design for the Research Gateway.


This de-coupled design makes it easy to use a Shared Gateway while connecting your own AWS Account for consumption with full control and transparency in billing & tracking. For many small and mid-sized research teams, this is the best balance between using a third-party provider-hosted account and having their own end-to-end setup. This structure is also useful for deploying a hosted solution covering multiple group entities (or conglomerates), typically covering a collaborative network of universities working under a central entity (usually funded by government grants) in large-scale genomics grants programs. For customers who have more specific security and regulatory needs, we do allow both the hub-and-spoke deployment accounts to be self-hosted. The flexible architecture can be suitable for different deployment models.


AWS Services that MyResearchCloud uses for each customer:


Service Needed for Secure Research Solution Provided Run Time Costs for Customers
Need for DNS-based friendly URL to access MyResearchCloud SaaS RLCatalyst Research Gateway No additional costs
Secure SSL-based connection to my resources AWS ACM Certificates used and AWS ALB created for each Project Tenant AWS ALB implemented smartly to create and delete based on dependent resources to avoid fixed costs
Network Design Default VPC created for new accounts to save users trouble of network setups No additional costs
Security Role-based access provided to RLCatalyst Research Gateway with no keys stored locally No additional costs. Users can revoke access to RLCatalyst Research Gateway anytime.
IAM Roles AWS Cognito based model for Hub No additional costs for customers other than SaaS user-based license
AWS Resources Consumption Directly consumed based on user actions. Smart features are available by default with 15 min auto-stop for idle resources to optimize spends. Actual usage costs that is also suggested for optimization based on Spot instances for large workloads
Research Data Storage Default S3 created for Projects with the ability to have shared Project Data and also create private Study Data. Ability to auto-mount storage for compute instances with easy access, backup, and sync with base AWS costs
AWS Budgets and Cost Tracking Each project is configured to track budget vs. actual costs with auto-tagging for researchers. Notification and control to pause or stop consumption when budgets are reached. No additional costs.
Audit Trail All user actions are tracked in a secure audit trail and are visible to users. No additional costs
Create and use a Standard Catalog of Research Products Standard Catalog provided and uploaded to new projects. Users can also bring their own catalogs No additional costs.
Data Ingress and Egress for Large Data Sets Using standard cloud storage and data transfer features, users can sync data to Study buckets. Small set of files can also be uploaded from the UI. Standard cloud data transfer costs apply

In our experience, research institutions can enable new groups to use MyResearchCloud with small monthly budgets (starting with US $100 a month) and scale their cloud resources with cost control and optimized spendings.

Summary
With an intent to make Scientific Research in the cloud very easy to access and consume like typical Business to Consumer (B2C) customer experiences, the new “MyResearchCloud” model from Relevance Lab enables this ease of use with the above solution providing flexibility, cost management, and secure collaborations to truly unlock the potential of the cloud. This provides a fully functional workbench for researchers to get started in 30 minutes from a “No-Cloud” to a “Full-Cloud” launch.

If this seems exciting and you would like to know more or try this out, do write to us at marketing@relevancelab.com.

Reference Links
Driving Frictionless Research on AWS Cloud with Self-Service Portal
Leveraging AWS HPC for Accelerating Scientific Research on Cloud
RLCatalyst Research Gateway Built on AWS
Health Informatics and Genomics on AWS with RLCatalyst Research Gateway
How to speed up the GEOS-Chem Earth Science Research using AWS Cloud?
RLCatalyst Research Gateway Demo
AWS training pathway for researchers and research IT



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