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2022 Blog, Blog, Featured

In recent times, Next Generation Sequencing (NGS) has transformed from being solely a research tool to be routinely applied in many fields, including diagnostics, outbreak disease investigations, antimicrobial resistance, forensics, and food authenticity. The use of cloud and modern open source tools is helping advancement at a rapid pace, with continuous improvement in quality and cost reduction, and is having a major influence on food microbiology. Public health labs and food regulatory agencies globally are embracing Whole Genome Sequencing (WGS) as a revolutionary new method. In this blog, we try to introduce this interesting use case and cover a common use case of Bacterial Genome Analysis in the cloud using our Research Gateway. We will show how to run powerful tools like Bactopia, a Flexible Pipeline for Complete Analysis of Bacterial Genomes in a few minutes.

What is Bactopia?
Sequencing of bacterial genomes is gathering momentum for greater adoption. Bactopia, developed by Robert A. Petit III, was created with a new series of pipelines (acknowledgements) built using Nextflow workflow software to provide efficient comparative genomic analyses for bacterial species or genera. This pipeline has more advanced features than many others in a similar space.

The image below shows the High Level Components of Bactopia Pipeline.


What Makes Bactopia More Powerful Compared to Other Similar Solutions?
The following data shared by the authors of this pipeline highlights the key strengths.



Usually, for researchers to get started with setting up a secure environment, accessing the data, big compute, and analytics tools can be a significant effort. With Research Gateway built on AWS, we make it extremely simple to get started.

An Introduction to Running Bactopia on AWS Cloud
Bactopia is a software pipeline for the complete analysis of bacterial genomes. Bactopia is based on the Nextflow bioinformatic workflow software. Research Gateway supports Nextflow based pipelines to be run with great ease, and we will show you how the same can be achieved with Bactopia.

Steps for Running Bactopia Pipeline on AWS Cloud
Step-1: Using the publicly available Bactopia repository on Github, a new AWS AMI is created by installing Bactopia software on Nextflow advanced product available as part of Research Gateway standard catalog. This step is needed since Bactopia contains a large number of specialized tools integrated and embeds Nextflow internally for its execution. Once the new AMI of Bactopia is ready, it is added to AWS Service Catalog and imported into Research Gateway to be used by Researchers. The product is available in the standard products category to be launched with 1-Click, as shown below.


Step-2: Once the Bactopia product is ordered using a simple screen as shown above in about 10 minutes, the setup with all the tools, Nextflow & Nextflow Tower are all provisioned and ready to be used. The user can log in to the Bactopia server using the SSH key-pair available from within the Portal UI using the “SSH/RDP Connect” action, as shown below.

Step-3: Copy data to the Bactopia server based on samples to be used for processing and start the execution of workflow as per available documentation. In our case, we tried with a smaller set of sample data sets, and it took us 15 min to run the pipeline and view outputs in the console window.

Step-4: When the pipeline is being executed using Nextflow Tower, details of the jobs and all key metrics can be viewed by the user from within the Research Gateway by selecting the “Monitor Pipeline” action. The entire complexity of different tools integrated on the platform is invisible to the user making it a seamless experience.

Step-5: The outputs generated by the Bactopia pipeline can be viewed from within the Portal using the “View Outputs” action that allows users to view the outputs in a simple browser, and actions can be taken to view the same with specialized tools like Integrative Genomics Viewer (IGV) or MultiQC reports, etc.

All the products that are used in Research Gateway have automatic tagging and tracking for cost purposes, and it can be easily verified by project, researchers, and product type on the total consumption providing a powerful cost management and budget tracking tool.

Summary
As the usage of Genomics adoption grows and new use cases emerge for leveraging the power of this technology, focus on food safety is a growing need with an ability to Bacterial Genome analysis using advanced pipelines popularly available in the open source community. To help researchers use such power tools with speed on the cloud without getting into the complexity of infrastructure, networks, security, and focus on science, we have demonstrated in this blog the ability to use Research Gateway to run your first pipeline in less than 60 minutes.

To know more about how you can start your Bacterial Genome analysis pipelines on the AWS Cloud in less than 60 minutes using our solution at https://research.rlcatalyst.com, feel free to contact marketing@relevancelab.com

References
An introduction to running Bactopia on Amazon Web Services (May 2021)
Using AWS Batch to process 67,000 genomes with Bactopia (December 2020)
Accelerating Genomics and High Performance Computing on AWS with Relevance Lab Research Gateway Solution



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2022 Blog, Blog, Featured

Research Gateway SaaS 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. 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 consuming functionality but in the area of scientific research, it is equally critical to have tight control on data security, privacy, and regulatory compliances.

One of the growing needs from customers is to use the solution for their online training needs and specialized use cases on Bioinformatics courses. With the pandemic, there is tremendous new interest in students to pursue life sciences courses and specialize in Bioinformatics streams. At the same time, education institutions are struggling to move their internal Training Labs infrastructure from data centers to the cloud. As an AWS specialized partner for Higher Education, we are working with a number of universities to understand their needs better and provide solutions to address the same in an easy + cost-effective manner.

The Top-5 use cases shared by customers to set up their Virtual Cloud Labs for courses like Bioinformatics are the following:


  • Enterprise Needs: Ability to move from Data Center based physical labs to cloud-based Virtual Labs using their Corporate Cloud accounts easily without compromising on security, tight cost controls, and a self-service portal for Instructors and Students. Enterprise-grade controls on Budget, Students/Instructors Access, Data Security, and Approved Products Catalog.
  • Business Needs: The setup of a New Virtual Training Lab should support the key learning and research needs of the students.
    • Programs available to provide labs access to students based on calendar programs for the duration of the full semester.
    • Longer-term projects and programs accessible for labs based on research grants and associated budgets/time constraints.
  • IT Department Needs: From University Corporate IT to be able to allow specific departments (like Bioinformatics) to have their own Programs and Projects with self-service without compromising on Enterprise Security and Compliance Needs.
  • Curriculum Department Needs: From different Department Heads (like Bioinformatics) and Instructors be able to define learning curriculum and associated training programs with access to Classroom and Research Labs. Departments also need tight control on budgets and student access management.
  • Student Needs: The ability for students to access cloud-based Training Labs is a very easy and simple manner without requiring deep access to cloud knowledge. Also having pre-build solutions for basic needs covering Analytics Tools like RStudio/Jupyter, access to secure data repositories, open-source tools/containers access, and collaboration portal.

The following picture describes the basic organization and roles setup in a university.



To balance the needs of speed with compliance, we have designed a unique model to allow Universities to “Bring your own License” while leveraging the benefits of SaaS in a unique hybrid approach. Our solution provides a “Gateway Model” of Hub-n-Spoke design where we provide and operate the “Hub” while enabling universities and their departments to connect their own AWS Research accounts as a “Spoke” and get started within 30 min with full access to a complete Classroom Toolkit. A sample of out-of-the-box Bioinformatics Lab tools available as a standard catalog is shown below.


Professors can add more tools to the standard catalog by importing their own AMIs using AWS Service Catalog. It is also very simple to create new course material and support additional tools using the base building blocks provided out-of-the-box.

Currently, it is not easy for universities, their IT staff, professors, students, and research groups to leverage the cloud easily for their scientific research. There are constraints with on-premise data centers and these institutions have access to Cloud accounts. However converting a basic account to a secure network, secure access, ability to create & publish product/tools catalog, ingress & egress of data, sharing of analysis, enforce tight budget control are non-trivial tasks that divert attention away from education to infrastructure.

Based on our discussions with stakeholders it was clear that the users want something that is as easy to consume as other consumer-oriented activities like e-shopping, consumer banking, etc. This led to the simplified process of creating a “My -Bioinformatics-Cloud-Lab” with the following basic needs:

1. A university can decide to sign up with Research Gateway (SaaS) to enable their different departments for using this software to enable online training and research needs. Such a university-level adoption is recommended to be an enterprise version of the software (hosted by us or by the university themselves) and used for different departments (called Organization or Business Units).
2. Another simpler way is to use our hosted version of Research Gateway by a particular department to create a tenant in Research Gateway with no overheads to maintain a university-specific deployment.
3. A Head of Department (HOD) can sign-up to create a new Tenant on Research Gateway and configure their own AWS Billing account to create Projects. Each Project can then invite other professors to be part of the online Training Labs. Projects can be aligned with semester-based classroom lab needs or can be part of ongoing research projects. Each project has a budget assigned along with associated professors and students, who have access to the project. The figure below shows typical department projects inside the portal.


4. Once the professor selects the project they can see standard “available products” in the Project. This project is used as a basic setup for a Training Lab. The figure below shows the sample screen for the available set of tools Professors can access by default. They can also add new products to the Lab Catalog.


For every Project (Lab) by default shared infrastructure is made available in the form of Project Storage, where curriculum-related data and information can be stored and made available to all students. Also, necessary security aspects for SSL connection, VPC, IAM roles, etc. are setup by default to make sure the Cloud Training Lab has a well-architected design.

5. A professor can control basic parameters for the Lab in terms of adding/deleting users, managing budgets, and also be able to take actions like “Pausing” a Project (no new products can be created while existing ones can be used) or “Stopping” the project (where all existing running machines are force stopped and no new ones can be created, however, data on the storage is accessible by students). The figure below shows how to manage project-level users and budget controls.


6. A professor can track the consumption of the lab resources by all users including professors and students as shown in the figure below.


7. Once a student logs into the project and accesses the lab resources, they can create their own workspaces like Rstudio and interact with the same from within the Portal. Once they are done with their work, they can stop the machine and log out to ensure no costs are being spent while the systems are not being used. When a researcher or student logs in, they can interact with active products and project storage as shown in the figure below.


8. The students can interact with their tools like RStudio from within the portal and connect to the same in a secure manner with a single click as shown in the figure below.


9. On Clicking the “Open Link” action, it allows access to an RStudio familiar environment for students to log in and learn as per their curriculum needs. The figure below shows the standard RStudio environment.


Summary
The new solution from Relevance Lab makes Scientific Research and Training in Cloud very easy for use cases like Bioinformatics. It provides flexibility, cost management, and secure collaborations to truly unlock the potential of the Cloud. For Higher Education Universities, this provides a fully functional Training Lab accessible by professors and students in less than 30 minutes.

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

References
University in a Box – Mission with Speed
Leveraging AWS HPC for Accelerating Scientific Research on Cloud
Enabling Frictionless Scientific Research in the Cloud with a 30 Minutes Countdown Now!



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2022 Blog, 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 Blog, SWB Blog, Blog, Featured

Relevance Lab launches its professional services for Service Workbench on AWS (SWB) available for customers through AWS Marketplace. SWB is a cloud-based open-source solution that caters the needs of the scientific research community by empowering both researchers & research IT teams.

Relevance Lab is a preferred partner for SWB to help customers adopt this open-source solution seamlessly. We have deep expertise and can help in assessment, planning, deployment, training, customization and ongoing managed services support in a cost effective manner.

Highlights of Professional Services Offering

  • Service Workbench on AWS which is an the open-source solution is fully supported with deep competence to help Plan-Build-Run lifecycle
  • Provide assessment, planning, deployment, training, customization and ongoing managed services support
  • Offer cost-effective and flexible engagement models

With Relevance Lab’s professional services for SWB, IT teams are able to deliver secure, repeatable, and federated access control to data, tooling, and compute power to researchers driving a frictionless scientific research on cloud.

Key Offerings

  • Assessment, Implementation and Training for new and existing setup
  • Advanced Setup & Premium Support including underlying infrastructure with special needs on Security, Compliance, Data Protection and Scalability
  • Ongoing Managed Services & Support including Upgrades, Monitoring and Incident Management
  • SWB Code and new feature customization, enhancement services for custom catalog like RStudio on ALB

What it Means for Scientific Researcher Community?
Relevance Lab’s Professional Services Offering for Service Workbench on AWS is a solution that enables IT teams to provide secure, repeatable, and federated control of access to data, tooling, and compute power that researchers need. With Service Workbench, researchers no longer have to worry about navigating cloud infrastructure. They can focus on achieving research missions and completing essential work in minutes, not months, in configured research environments.

Frequently Asked Questions

Question-1  How to get started using SWB and RStudio with ALB?
Answer:  We have a dedicated landing page, sign-up page and support model

Question-2  What is a typical customer end-to-end journey?
Answer:  Most customers look for the following support for the adoption lifecycle.

  • One time on-boarding
  • Product customization services
  • On-Going managed services and support
  • T&M services for anything additional

Question-3  How long does onboarding take, and what does it cost?
Answer:  A standard onboarding for a new customer takes about 2 weeks covering initial assessment, installation, configurations, training, and basic functionality demonstration for a new setup. It costs about US $10,000.

Question-4  What sort of support is available post onboarding?
Answer:  Following are the common support activities requested:

  • L0 – Monitoring and Diagnostics
  • L1 – Technical Queries on how to use the product effectively
  • L2 – Ongoing upgrades, troubleshooting, configurations
  • L3 – Customization, enhancements (typically for less than 40-hour changes per request)
  • Project Engagement – for typically 40+ hours of enhancements/customization work

Question-5  What is the engagement model for ongoing support or customizations?
Answer:  Two models of support are offered – Basic and Premium. In case of customizations, both models of project-based and Time & Material engagement are possible.

Looking Ahead
SWB is available as an open-source solution and provides useful functionality to enable self-service portal for research customers. However, without a dedicated partner to support through the complete lifecycle, it can be a daunting exercise for customers and overheads for their internal IT teams. Based on the feedback from early adopters and in partnership with AWS, we are happy to launch specialized professional services on AWS Marketplace to make adoption by customers frictionless. Keeping the open source nature in mind, the services are optimized to be cost-effective and flexible with a goal to make scientific research in the cloud faster, cheaper and better.

To learn more about Relevance Lab’s professional services for Service Workbench, feel free to write to marketing@relevancelab.com

References
Relevance Lab Open-Source Collaboration with Service Workbench on AWS
Service Workbench Template on Github



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2022 Blog, 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 Blog, 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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2022 Blog, Blog, Featured

Software architecture provides a high-level overview of what a software system looks like. At the very minimum, it shows the various logical pieces of the overall solution and the interaction between those pieces. (See C4 Model for architecture diagramming). The software architecture is like a map of the terrain for anybody who must deal with the system. Contrary to what many might think, software architecture is important even for non-engineering functions like sales, as many customers like to review the architecture to see how well it fits within their enterprise and whether it could introduce future issues by its adoption.


Goals of the Architecture
It is important to determine the goals for the system when deciding on the architecture. This should include both short-term and long-term goals.

Some of our important goals for RLCatalyst Research Gateway are:
1. Ease of Use
The basic question in our mind is always “How would customers like to use this system?”. Our product is targeted to researchers and academics who want to use the scalability and elasticity of the AWS cloud for ad-hoc and high-performance computing needs. These users are not experts at using the AWS console. So, we made things extremely simple for the user. Researchers can order products with a single click, and the portal sets up their resources without the user needing to understand any of the underlying complexities. Users can also interact with the products through the portal, eliminating the need to set up anything outside the portal (though they always have that option).

We also kept in mind the administrators of the system for whom this might just be one amongst many others that they must manage. Thus, we made it easy for the administrator to add AWS accounts, create Organizational Units, and integrated Identity Providers. Our goals were: administrators to get the system up and running in less than 30 minutes.

2. Scalability, performance, and reliability
We followed the best practices recommended by AWS, and where possible, used standardized architecture models so that users would find it easy as well as familiar. For example, we deploy our system into a VPC with public and private subnets. The subnets are spread across multiple Availability Zones to guard against the possibility of one availability zone going down. The computing instances are deployed in the private subnet to prevent unauthorized access. We also use auto-scaling groups for the system to be able to pull in additional compute instances when the load is higher.

3. What is the time to market?
One of our main goals was to be able to bring the product to market quickly and put it in front of the customers to gain early and valuable feedback. Developing the product as a partner of AWS was a great help since we were able to use many AWS services for some of the common application needs without spending time in developing our own components for well known use-cases. For example, RLCatalyst Research Gateway does its user management via AWS Cognito, which provides the facility to create users, roles, and groups as well as the ability to interface with other Identity Provider systems.

Similarly, we use AWS DocumentDB (with MongoDB API compatibility) as our database. This allows developers to use a local MongoDB instance, while QA and production systems use AWS DocumentDB with high availability of multi-AZ clusters, automated backups via AWS Backup and Snapshots.

4. Cost efficiency
This is one of the key concerns for every administrator. RLCatalyst Research Gateway uses a scalable architecture that not only lets the system scale up when the load is high but also scales down when the load is less to optimize on the cost. We use EKS clusters to deploy our solution and AWS DocumentDB clusters. This allows us to choose the size and instance type according to the cost considerations.

We have also brought in features like the automatic shutdown of resources so that idle compute instances, which are not running any jobs, can shut down after a 15-minute idle time. Additionally, even resources like ALBs are de-provisioned when the last compute instance behind them is de-provisioned.

We provide a robust cost governance dashboard, allowing users insights into their usage and budget consumption.

5. Security
Our target customers are in the research and scientific computing area, where data security is a key concern. We are frequently asked, “Will the system be secure? Can it help me meet regulatory requirements and compliances?”. RLCatalyst Research Gateway architecture is developed with security in mind at each level. The use of SSL certificates, encryption of data at rest, and the ability to initiate action at a distance are some of the architecture considerations.

Map of AWS Services

AWS Service Purpose Benefits
Amazon EC2, Auto-scaling Elastic Compute Provides easily managed compute resources without need to manage hardware. Integrates well with Infrastructure as Code (IaC)
Amazon Virtual Private Cloud (VPC) Networking Provides isolation of resources, easy management of traffic, isolation of traffic.
Application Load Balancer, AWS Certificate Manager Load-balancer, Secure end-point Provides an easy way to provide a single end-point which can route traffic to multiple target groups. Integrates with AWS Certificate manager to provide SSL support.
AWS CostExplorer, AWS Budgets Cost and Governance Provides fine-grained cost and usage data. Notifications when budget thresholds are reached.
AWS Service Catalog Catalog of approved IT Services on AWS Provides control on what resources can be used in an AWS account.
AWS WAF (Web Application Firewall) Application Firewall Helps manage malicious traffic
Amazon Route53 DNS (Domain Name System) Services Provides hosted zones and API access to manage the same.
Amazon Cloudfront CDN (Content Delivery Network) Caches content closest to end-users to reduce latency and improve customer experience.
AWS Cognito User Management Authentication and authorization
AWS Identity and Access Management (IAM) Identity Management Provides support for granular control based on policies and roles.
AWS DocumentDb NoSQL database MongoDB compatible API


Validation of the Solution
It is always good to validate your solution with an external review from the experts. AWS offers such an opportunity to all its partners by way of the AWS Foundational Technical Review. The review is valid for two years and is free of cost to partners. Looking at our design through the FTR Lens enabled us to see where our design could get better in terms of using the best practices (especially in the areas of security and cost-efficiency). Once these changes were implemented, we earned the “Reviewed by AWS” badge.

Summary
Relevance Lab developed the RLCatalyst Research Gateway in close partnership with AWS. One of the excellent tools available from AWS for any software architecture team is the AWS Well-Architected Framework with its five pillars of Operational Excellence, Security, Reliability, Performance Efficiency, and Cost Efficiency. Working within this framework greatly facilitates the development of a robust architecture that serves not only current but also future goals.

To know more about RLCatalyst Research Gateway architecture, feel free to write to marketing@relevancelab.com.

References
How to speed up the GEOS-Chem Earth Science Research using AWS Cloud?
Driving Frictionless Scientific Research on AWS Cloud
Leveraging AWS HPC for Accelerating Scientific Research on Cloud
Health Informatics and Genomics on AWS with RLCatalyst Research Gateway
Enabling Researchers with Next-Generation Sequencing (NGS) Leveraging Nextflow and AWS
8-Steps to Set-Up RLCatalyst Research Gateway



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2022 Blog, AWS Platform, Blog, Featured

Relevance Lab has been an AWS partner for almost a decade now. The primary transition in 2021 was moving from a pure consulting partner to a niche technology partner of AWS based on the strengths of two new ISV Product launches with RLCatalyst Research Gateway and RLCatalyst AppInsights.


  • RLCatalyst Research Gateway drives scientific research in AWS cloud, especially for Genomic Research and Analytics
  • RLCatalyst AppInsights is built on AWS Service Catalog AppRegistry and helps achieve an “Application-Centric” view for cloud assets, costs, health, and security to achieve Governance360

Customers have been demanding a “Solutions” approach from their partners that combine the strength of Products (own + third party) and Services to provide a unique business solution that removes friction and helps deliver key value. This is only possible by unifying the strength of Products + Services to create platform-based offerings delivered with a unique playbook for driving digital transformation.

The top-5 trends we observed in last one year regarding customer needs for cloud adoption are the following:


  • Cloud Adoption Acceleration
    • “Cloud Only” adoption to accelerate momentum of transitioning all internal systems, applications, and services to IaaS, PaaS, and SaaS solutions with an automation-first approach
  • DevOps Automation Led Operations
    • Critical focus on AIOps to ensure digital business operations are proactively managed with best practices on operations with Site Reliability Engineering (SRE) and DevOps, leveraging ServiceNow platform
  • Frictionless Digital Workflows and Business Interactions
    • End-to-end business process integration with applications across self-developed products, PaaS platforms, and third-party SaaS solutions covering Shopify, Adobe Experience Manager, Demandware, Oracle Fusion, SOA/API Gateways, etc.
  • Cloud Data Lakes and Actionable Intelligence
    • Focus on agile business analytics with use of cloud-based data platforms leveraging Snowflake, Databricks, Azure Data Factory, AWS Data Lakes, etc., and integration with AI/ML tools with Sagemaker and RStudio
  • Security, Compliance, and Cost Management with focus on Governance360
    • Critical focus on security, governance, and cost optimization with a proactive model driven by a strong automation foundation

In this blog, we will primarily cover the strategic AWS partnership achievements of our products and solutions leveraged to help our customers use cloud “The Right Way”. The business benefits of this automation and platform led approach helped some of the key customers achieve significant outcomes, as explained below:


  • Speeded up product delivery cycles by 3x leveraging Agile + DevOps approach for Product Engineering and Application Migrations
  • Cut down cloud cost spending by 30% with better capacity utilization and effective cloud costs tracking at a granular level of business units, applications, customer usage patterns, and transaction costs optimization
  • Leveraging Automated Service Management achieved 70% handling of inbound tickets by smart BOTs using our product and RPA tools creating an Automation Factory
  • Proactive security and vulnerability management reducing the cost of compliance and reduced outages by 30%
  • Focus on effective data management and analytics with more real-time insights to business transactions and actionable intelligence leading to savings in excess of $300K annually for large supply chain use cases

Leveraging AWS cloud is a foundation enabler for all Relevance Lab products and solutions. The diagram below shows a high level overview of our AWS Ecosystem coverage.


The journey snapshot of the last 12 months is captured in the diagram below.


Relevance Lab and AWS Journey Highlights
To recap our key progress for this year, we are presenting a quick brief of the last 12 months in reverse chronological order.


  • December
    • Solid partnership with AWS APJ teams for go-to-market in the region for scientific research with RLCatalyst Research Gateway. There is a strong endorsement from AWS business teams and Solution Architects on RL solutions being a relevant offering for regional needs
    • Launch of Cloud Academy to train a new batch of people based on a platform-led model for the ability to rapidly create a large and competent workforce for cloud opportunities
    • CoE (Center of Excellence) teams pursuing new use cases High-Performance Computing (large and growing ecosystem) and AppStream-based training labs for education customers.
    • AppInsights product on ServiceNow emerging as a brand new product conceptualized and launched in 2021 with joint efforts with AWS Control Services group
  • November
    • Relevance Lab’s focus on addressing the Digital Transformation jigsaw puzzle with RLCatalyst and SPECTRA Platforms
    • ServiceOne and RLCatalyst Intelligent Automation Reference Architecture
    • SPECTRA Reference Architecture for agile analytics applications
    • Relevance Lab Hyperautomation approach to business optimization
    • Relevance Lab Service Maturity Model
  • October
    • Taking AWS cloud & ServiceNow solutions to multiple new prospects interested in understanding our offering across cloud management, automation, DevOps, and AIOps managed services
    • Showcasing RLCatalyst Research Gateway solutions to multiple public sector institutions, non-profit research centers, and health care providers
  • September
    • Key tracks of RL Cloud CoE covering Cloud Management, Automation, DevOps and AIOps shared
    • Summary of 10 year journey for RL Company and Product lifecycle shared
    • Launch of MyResearchCloud, An easy way to enable small and mid-sized customers to use the RLCatalyst Research Gateway SaaS product using “Bring Your Own Account”
    • RLCatalyst AppInsights launched on ServiceNow Store
  • August
    • RLCatalyst Platform, Solutions and Products consolidated offering for automation published
    • Automation-First approach for Plan-Build-Run of cloud adoption detailed
    • Maturity model for BOTs design published
    • RLCatalyst Genomics Pipeline work with Nextflow started
  • June-July
    • Joint efforts for the co-development on the open-source solutions for scientific research in cloud that emphasize on Health Informatics and Genomic processing space using RStudio
    • RLCatalyst Research Gateway solution has been reviewed and approved for ISV Path – a special program exclusively meant for the Independent Software Vendor (ISV) capabilities
    • Listing of Relevance Lab products and professional services on AWS Marketplace, which includes RLCatalyst Research Gateway SaaS and Governance360 solution built on AWS Control Tower
    • Selection of the AppInsights ServiceNow solution as the partner-built solution for AWS AppRegistry
    • Partnership with a specialist HIPAA governance solution provider for integrations into our Governance360 solution
    • Collaborating with the AWS recent solution announcement teams driving AWS Management and Governance Lens (part of AWS Well-Architected Framework prescribed offering)
  • May
    • RLCatalyst Research Gateway “test drive” by the first prospect with useful inputs to make the onboarding process much simpler and frictionless. Expectation to go from “No-Cloud” to “Full-cloud” experience for scientific researchers in less than 15 min (Uber-style)
    • Relevance Lab enters the elite AWS Service Delivery Program for niche partners for AWS Service Catalog
    • Relevance Lab SmartView, built on AWS AppRegistry new concept for dynamic application CMDB, getting significant appreciation and visibility from AWS Management and Governance teams
    • Ongoing co-development and collaboration with AWS Service Workbench groups to scale up RStudio on AWS Cloud with shared AWS ALB (Application Load Balancer) architecture
  • April
    • RLCatalyst Research Gateway common use cases implementation
    • “Automation-First” model for Cloud adoption elaborated
    • Common use cases for Cloud migration with focus on Application Migration
    • Original concept of SmartView Solution (later renamed AppInsights) for Application CMDB created.
  • March
    • “Automation-First” approach to use AWS cloud “The Right Way” detailed
    • Common use cases for scientific research published
    • AWS ISV Partner Path program adoption initiated
  • February
    • Research@Scale Architecture Blueprint created for an integrated offering combining strengths of Relevance Lab product, solutions and services
    • Conceptualizing Governance360 Solution built with AWS Control Tower customization framework
    • Started evaluation of AWS Service Workbench with BioInformatics Blueprint, RStudio, Sagemaker
    • ServiceOne Transition Blueprint created
  • January
    • Relevance Lab RLCatalyst Research Gateway product positioning in market with focus on “Blue Ocean Strategy” shared to create a niche offering
    • RLCatalyst Research Gateway launched as a SaaS product on AWS Marketplace
    • ServiceOne team worked on the Compliance as a Code Framework involving AWS Control Tower

RLCatalyst – Our Platform through 2021


ServiceOne: Our Cloud CoE Stories in 2021


Partnership Journey through Blogs, Webinar & Videos in 2021

It was a busy year at Relevance Lab. We published a number of blogs covering our solutions powered by our partnership with AWS. The following is a collection of blogs published on our website throughout the year:

Product Related



List of product knowledge videos on our YouTube channel.

Solutions and Consulting Related



We have also published blogs listed below on APN blog network in collaboration with AWS teams.



In addition, we successfully conducted a webinar with AWS and Dash Solutions. You can watch the recording here and download the presentation pdf here.

Summary
With the start of the new year 2022, we are very bullish about leveraging our AWS cloud Products and Solutions that help in driving Frictionless Business for our customers. There are 100000+ AWS Partners in the ecosystem worldwide, but Relevance Lab has created a unique differentiator and positioning leveraging the power of our IP Products as a key technology provider to complement our deep services competencies that are leading to tremendous momentum on new customer solutions.

Customers are continuing to face challenges with their business and supply chains in the pandemic era, and new business models are emerging that demand a new level of Agility + Automation. At Relevance Lab, we are constantly enhancing our offerings to help our customers navigate the Digital Transformation Puzzle and provide a unique value proposition with our global workforce across regions, critical investments in our IP platforms, and constant efforts on building deep competencies across cloud, data, and digital platforms.

To learn more about our cloud products, services and solutions, feel free to contact us at marketing@relevancelab.com.



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2021 Blog, Digital Blog, Blog, Featured

Relevance Lab’s focus on addressing the digital transformation jigsaw puzzle has a strategic investment in leveraging Products & Platforms to create a unique differentiation and competitive advantage. We are a specialist cloud, DevOps, automation, and analytics services company with an IP (Intellectual Property) led technology strategy. This helps our customers achieve frictionless business outcomes by leveraging cloud across their infrastructure, applications, and data.

We optimize IT spending with smart cloud workload migration, reducing ongoing operations costs by leveraging automation & governance, speeding up innovation in the delivery of new software products with Agile & DevOps, and getting key real-time business insights with Actionable Analytics.

The key platforms and playbooks that we have are the following:


RLCatalyst provides an “Automation-First” approach for Enterprise Cloud Orchestration across “Plan, Build & Run” lifecycle, leveraging our unique IP. A pre-built library of quick-starts, BOTs and Open source solutions helps customers use Cloud “The Right Way” focused on best practices like “Infrastructure as Code” and “Compliance as Code”. We also have unique specialization on AWS and ServiceNow platforms leveraged to provide Intelligent Cloud Operations & managed services with our ServiceOne platform covering workload migration, security, governance, CMDB, ITSM, and DevOps.

SPECTRA provides a Digital and Agile Analytics platform that helps build enterprise data lakes and Supply Chain analytics with multiple ERP systems connectors (SAP, Oracle, Dynamics, JDE, etc.). It also provides a smart-document search engine for Google-like features on enterprise digital documents (images, PDF, engg drawings, etc.). We leverage the Digital platforms for Frictionless Application modernization and Cloud Product Engineering services extending across platforms covering content, commerce, CRM, and supply chain (Adobe, Shopify, SFDC, Oracle Fusion, Azure PowerApps, Services & ADF) integrated with actionable insights from SPECTRA.


The figure above explains our company’s focus in driving frictionless IT and business operations leveraging these key platforms. The focus on a “coded business model” that the platforms deliver help us engage across the full lifecycle with customers covering the following stages:

  • Assess the key customer needs as each customer has a unique model we evaluate based on 3C’s (Culture, Content & Constraints)
  • Standardize the internal systems, processes, engineering practices, and governance
  • Automate everything repetitive impacting speed, costs, quality, and compliance
  • Accelerate the achievement of business objectives with faster software delivery, better operational excellence, and real-time Agile Analytics

RLCatalyst Platform and ServiceOne Solution
RLCatalyst is an intelligent automation platform built with DevOps, Cloud, and Automation features covering infrastructure, applications, data, and workflows. RLCatalyst common services foundation is built using an open architecture in line with the industry standards and can be customized. On top of the foundation services, a set of specialized products, solutions, and services are created to cover the specialized needs of customers. Following are a few key foundation themes for RLCatalyst:

  • Built on Open-source products to provide flexibility and scalability for hybrid cloud deployments
  • Uses “Infrastructure as Code” best practices and DevOps standards covering CI/CD, end to end monitoring, and orchestration
  • The platform is built to have a UI Portal front-end, Node.JS API-based backend, integration layer for executing BOTs, and database layer based on NoSQL
  • The core concept uses a “self-aware” paradigm to embed dynamic configurations, end to end monitoring, and dynamic CMDB to enable smart operations using other ITSM and Cloud platforms
  • The Cloud Portal drives self-service models of DevOps and can be customized to add domain-specific business rules for customers or industry type
  • There is “Compliance as Code” embedded into the design to make sure customers can be aligned with well-architected principles
  • The platform is built on top of AWS and ServiceNow ecosystem but can also be deployed on-prem or other cloud platforms
  • The solutions are pre-integrated with other popular DevOps and Cloud tools like Docker, Chef, Anisible, Terraform, Jenkins, ELK, Sensu, Consul, etc
  • The platform comes with a pre-built library of BOTs and Quickstart templates

The combination of RLCatalyst and ServiceOne integrated solution provides an intelligent automation architecture, as explained in the figure below. The key building blocks are:

  • Discover the underlying assets, health, costs, vulnerability, security, and compliance.
  • Automate using a framework of BOTs built with self-aware intelligence covering tasks, workflows, decisioning, and AI/ML algorithms.
  • Resolve at speed all service management tickets and requests with complex workflows & integration across multiple systems

SPECTRA Platform and Business Process Automation
SPECTRA, the AI-driven Analytics platform from Relevance Lab, based on Open Source technology, can fast track your journey from data to actionable insights. It can process data from structured data from different ERP Systems based on pre-existing adapters and unstructured data from PDFs, Emails, engineering drawings, and commercial labels. Every organization has invested in a combination of tools, technologies and solutions to create their Data platforms. However, most of these platforms are built with legacy technologies or fragmented components. When companies try to leverage the new technologies of Big Data, Cloud Architectures and Artificial Intelligence to achieve more meaningful Analytics a pre-built Platform like SPECTRA can save tremendous efforts, costs and time to provide a scalable and flexible alternative.

Similar to the RLCatalyst IT Optimization we leverage SPECTRA Platform for Business Optimization with Agile Analytics, as explained in figure below.


We have also leveraged SPECTRA Platform and UiPath Integration to Achieve business process hyper automation, as explained briefly below.


Customer Impact with RL Playbooks for IT and Business Transformation
Relevance Lab leverages our strengths in platforms for all our customer engagements to bring out special value on services delivery in areas of:

  • Cloud Infrastructure Automation
  • Data Analytics Platforms
  • Digital Applications and Product Engineering
  • Intelligent Operations and DevOps

The figure below highlights the value created for some of our key customers.


We have adopted the following maturity model as a specialist technology company with significant investments on competency and IP creation that guides the investments in RLCatalyst and SPECTRA platforms.


Level-1 Deep Technology Expertise Continuous learning and skills upgrade on latest/emerging Technologies focus across Cloud, Automation, Analytics, DevOps, Digital
Level-2 Focus on Certifications – Basic & Advanced Promoting “Industry Certifications” to benchmark the competencies against the global standards and make this part of every developer’s career enhancement goal
Level-3 Solutions and Best Practices (Process & Tools) Focus on customer solutions and recurring use cases to build a knowledge base of best practices across software engineering, operations excellence, business domains
Level-4 Platform Focus “Codified Knowledge” in the form of Platforms for Data Analytics, DevOps, Cloud & Automation with source code in re-usable formats. Well-Architected Frameworks and leveraging open-source platforms with custom component enhancements & integrations to save effort, time, and improved quality with each new engagement
Level-5 Product Offerings Prescriptive and pre-created products that customers can use in a “touchless” manner as SaaS or Marketplace offerings like a typical ISV solution with little or no dependency on associated professional services. Major benefit in enabling frictionless jumpstart on specific business problems.

Summary
Relevance Lab has made significant investments in creating IT and Business Transformation platforms that provide us a key competitive advantage in unlocking value for our customers across DevOps, Analytics, Cloud, Automation and Digital Engineering. By following a service maturity model that goes beyond just headcount and competencies we have been able to bring the value of platform and products to solve the digital transformation puzzle for our 50+ customers.

To know more about how can our products and platforms help feel free to contact us marketing@relevancelab.com.



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2021 Blog, Blog, Featured

The CMS-1500 form is vital to the smooth functioning of the American health insurance system, and yet processing these manually filled-up, paper-based forms can be a nightmare. Relevance Lab has developed a new approach to automating claims processing that improves output quality and delivers cost savings.

CMS-1500 Processing Challenges
The CMS-1500 form, formerly known as the HCFA-1500 form, is the standard claim form used by a non-institutional provider or supplier to bill Medicare carriers under certain circumstances. Processing these important documents presents several challenges:


  • Large volume of information: A single form has 33 fields (plus sub-fields) to be filled up manually; multi-page claims are required if more than 6 services are provided.
  • Illegible handwriting: Since the forms are filled manually (and often in a hurry), it is quite common to find illegible or difficult to read entries.
  • Incomplete or inconsistent information: Fields are often missing or inconsistent (e.g. multiple spellings of the same name), complicating the task.
  • Poor scan quality: The scan quality can be poor due to misorientation of the form, folds, etc., making it difficult to recognize text.

Most users are faced with a Hobson’s choice between costly manual processing and low-accuracy conventional automation solutions, neither of which produce acceptable results. Manual processing can be slow, laborious, fatigue-prone and costs tend to grow linearly with claims volumes, regardless of whether the manpower is in-house or outsourced. Conventional automation solutions based on simple scanning and optical character recognition (OCR) techniques struggle to deal with such non-standardized data leading to high error rates.

Relevance Lab has developed a solution to address these issues and make CMS-1500 claims processing simpler and more cost-efficient without compromising on accuracy.

Relevance Lab’s Smart Automation Solution
Our solution enhances the effectiveness of automation by utilizing artificial intelligence and machine learning techniques. At the same time, the workflow design ensures that the final sign-off on form validation is provided by a human.
An illustrative solution architecture is given below:


Salient features of the solution are as follows:


Best-in-class OCR The solution utilizes the Tesseract open-source OCR engine (supported by Google), which delivers a high degree of character recognition accuracy, to convert the scanned document into a searchable pdf.
Processing & Validation against master data The document is analyzed using RL’s proprietary SPECTRA platform. Common errors (such as misaligned check-box entries) are corrected, and relevant fields are validated against the master data to catch anomalies (e.g. spelling errors).
Assisted human review The updated document is presented for human review. Fields that require attention are highlighted, together with algorithm-generated guesstimates suggesting possible corrections.
Automatic update of downstream data Once approved, downstream systems are automatically updated with validated data.
Self-learning The iterative self-learning algorithm improves with every validation cycle resulting in continuous refinement in accuracy. This improvement can be tracked over time through built-in trackers.
Workflow tracking The solution is equipped with dashboards that enable tracking the progress of a document through the cycle.
Role-based access It is possible to enable role-based access to different modules of the system to ensure data governance.


The following diagram presents a typical process flow incorporating our solution:


Demonstrated Benefits
Our CMS-1500 processing solution delivers significant time and cost savings, and quality improvements. It frees up teams from tedious tasks like data entry, without compromising human supervision and control over the process. The solution is scalable, keeping costs low even when processing volumes increase manifold.

The solution is tried, tested, and proven to deliver substantial value. For example, in a recent implementation at a renowned healthcare provider, the Relevance Lab SPECTRA solution was able to reduce the claims processing time by over 90%. Instead of a dedicated team working daily to process claim forms, manual intervention is now required only once a week for review and approvals. The resources freed up are now more productively utilized. This has also led to an increase in accuracy through the elimination of “human errors” such as typos.

Powered by the RL SPECTRA analytics platform, the solution has successfully delivered productivity gains to multiple clients by efficiently ingesting and processing structured and unstructured data. The plug-and-play platform is easy to integrate with most common system environments and applications.

Conclusion
CMS-1500 claims processing can be significantly optimized by using Relevance Lab’s intelligent solution based on its SPECTRA platform that combines the speed and scalability of automation with the judgment of a human reviewer to deliver substantial productivity gains and cost savings to organizations.

For more details, please feel free to reach out to marketing@relevancelab.com.



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