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Modernizing research and clinical infrastructure: How the Human Genome Sequencing Center at Baylor College of Medicine migrated its LIMS to AWS

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As research organizations face growing pressure to support complex, data-intensive workloads with less funding and resources, many are looking to modernize legacy infrastructure in a cost-effective way. For Baylor College of Medicine’s Human Genome Sequencing Center (BCM-HGSC), which conducts large-scale genomic sequencing and research, this meant rethinking how its on-premises Laboratory Information Management System (LIMS) was hosted and managed.

The LIMS had become costly to maintain and difficult to scale, relying on aging infrastructure and manual disaster recovery processes. In collaboration with Amazon Web Services (AWS), BCM-HGSC migrated its LIMS to a secure, hybrid cloud environment.

In this post, you will learn how the migration strengthened the system’s reliability, improved security, lowered costs—all while laying the groundwork for future growth.

An aging LIMS system faces growing research demands

Baylor College of Medicine is a leading academic health sciences center in Houston, Texas. With over 4,500 faculty members and nearly 4,000 trainees, the college is internationally recognized for its biomedical research and medical education programs.

One of Baylor’s most prominent research hubs is the Human Genome Sequencing Center (HGSC), which handles large-scale, high-throughput DNA sequencing and bioinformatics work across a broad spectrum of scientific and clinical projects. At the heart of these efforts is its LIMS platform, a robust system that tracks every sample from the moment it arrives in the lab through ingestion, processing, sequencing, analysis, and reporting.

“We track [a sample] from the time it enters the system all the way until it’s sent back to our collaborators and stakeholders,” said Javid Mohammed, senior software engineering manager and architect at the HGSC. “It goes through multiple workflows, gets sequenced and analyzed, and all of that is recorded in the LIMS.”

With the LIMS supporting such high-volume and high-impact work, maintaining reliability and scalability was essential. Until recently, the system ran entirely on-premises and required dedicated hardware, manual disaster recovery processes, and significant IT resources to manage. As the infrastructure became harder to scale and manage, the HGSC LIMS team looked to the cloud to future-proof its operations and lower infrastructure costs.

Designing a hybrid LIMS architecture with AWS

Starting in mid-2024, AWS and HGSC teams met biweekly to finalize the infrastructure design. Together, they approached the project using two AWS best practices: the 7Rs of cloud migration (Retire, Retain, Rehost, Relocate, Repurchase, Re-platform, and Re-factor) and the AWS Well-Architected Framework. The HGSC and AWS evaluated which systems could move to the cloud, ran pilot environments to benchmark performance, and refined the architecture to reduce costs, improve reliability, and align with NIST, CAP and CLIA security compliance requirements.

One key constraint shaped the solution: the lab’s robots, sequencers, and other instruments had to remain physically on site. To address this, the team designed a hybrid architecture that securely and reliably connected on-premises equipment with a cloud-based LIMS application. A central component was AWS Direct Connect, which established a dedicated, high-bandwidth link between the lab and AWS. “We didn’t want to go to the cloud without AWS Direct Connect,” said Javid. “Everything is connected to LIMS, so we needed to ensure reliable connections between the cloud and our on-premises equipment and systems.”

Building a cost-effective hybrid LIMS solution on AWS

To support the new environment, Baylor used a range of AWS services to run LIMS securely and at scale:

  • Amazon Elastic Compute Cloud (Amazon EC2) gives the LIMS application on-demand computing power.
  • Amazon Relational Database Service (Amazon RDS) keeps the LIMS database running smoothly and reliably.
  • Amazon Elastic File System (Amazon EFS) provides shared storage that grows automatically as data increases.
  • AWS Direct Connect delivers a fast, private link between the lab’s on-site equipment and the AWS Cloud.
  • Amazon CloudWatch and AWS CloudTrail show real-time health and performance information, as well as comprehensive activity logs.
  • AWS Identity and Access Management (IAM) controls who can do what in the environment.

Security and compliance were central to the architecture. HGSC enforced encryption in transit and at rest, centralized logging with AWS CloudTrail and Amazon CloudWatch, and deployed the solution within a dedicated landing zone via AWS Control Tower to efficiently align every account across the environment with the appropriate governance and compliance standards. All workloads run in private subnets and leverage AWS Direct Connect for secure, private connectivity.

Before migrating, the team benchmarked on-premises performance and ran pilot environments in AWS to validate cost assumptions. This allowed HGSC to right-size the environment—including scaling down database instance types—and confidently optimize spend. “We made sure that when we moved to the cloud, the cost savings and optimization would be what we expected,” said Javid.

Together, these services create a reliable, secure, and cost-effective foundation for HGSC’s LIMS in the cloud. With the new environment in place, the team was ready to complete the migration and begin seeing results.

Doing more with less through cloud modernization

Today, HGSC’s LIMS application and database are running entirely in the cloud, with additional systems expected to follow in future phases. ​​“It’s going to be an ongoing process,” said Javid. “But our mission-critical systems are all in the cloud.”

For lab users, the transition was seamless. The user interface and workflow remained unchanged, with researchers continuing to access the application through their browsers, just as they did before. “If we had not told them, they wouldn’t even have noticed,” Javid said. “That was one of our goals.”

Behind the scenes, however, the changes have been significant. The AWS environment provides richer observability through Amazon CloudWatch, allowing the team to track metrics like CPU and memory usage, application uptime, and connection health in real time. That visibility has improved operational confidence and allowed issues to be addressed before they impact users. “We had monitoring on-prem, but here it’s much richer,” Javid said. “We’re able to pull metrics when we need them, and that helps us stay ahead.”

HGSC’s IT team has also been able to streamline operations. Using Reserved Instances for Amazon EC2 and Amazon RDS helped lower infrastructure costs. By shifting to managed services, the team has also significantly reduced operational overhead. “We’re able to do more with less,” Javid added.

A scalable foundation for research growth

By modernizing LIMS with AWS, HGSC has strengthened a key part of its research infrastructure without disrupting the day-to-day work of its operations and lab teams. The migration delivered exactly what it set out to achieve: better monitoring, reduced operational overhead, and a reliable, scalable foundation for future innovation.

As research demands continue to evolve, BCM-HGSC is well positioned to move forward with confidence—building on what they’ve learned and expanding what’s possible in the cloud.

To learn more about how AWS helps research institutions modernize mission-critical systems like LIMS, visit AWS for Research or contact us today.

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Javid Mohammed

Javid Mohammed

Javid is a senior software engineering manager and architect at the Department of Human Genome Sequencing Center at Baylor College of Medicine. With a focus on leveraging AI and cloud computing, Javid is dedicated to accelerating advancements in medicine and research. He brings expertise in designing and implementing scalable, secure, and high-performance systems that empower scientific discovery and innovation in genomics and beyond.

Hayan T. Madhloom

Hayan T. Madhloom

Hayan is a senior programmer analyst with extensive experience in software engineering and research. Played a key role in migrating a Laboratory Information Management System (LIMS) to the AWS Cloud, utilizing services such as EC2, RDS, and AWS Cognito. He is passionate about designing and building scalable applications using cloud infrastructure and generative AI.

Mullai Murugan

Mullai Murugan

Mullai directs engineering at Baylor College of Medicine’s Human Genome Sequencing Center. She spearheads the center’s information systems—including LIMS and HIPAA-ready clinical applications—powering genomic testing and EHR interoperability. Mullai drives the cloud strategy—migrating applications to AWS, building secure and scalable services, standing up compliant environments, and implementing billing and cost governance. Her current focus is applying generative AI to pharmacogenomics and clinical informatics.

Niris Okram

Niris Okram

Niris is a senior specialist academic research solutions architect at AWS. He has extensive experience working with public, private, and research customers from various fields related to cloud. He is passionate about designing and building systems to accelerate missions on the AWS Cloud.

Piyushkumar Panchal

Piyushkumar Panchal

Piyushkumar is a lead solution architect at the Human Genome Sequencing Center at Baylor College of Medicine. He has significant experience in the development and maintenance of compute and storage technologies. He is responsible for maintaining both on-premises and cloud computing resources essential for the center.

Poonam Ghorpade

Poonam Ghorpade

Poonam is a lead programmer/analyst at the Human Genome Sequencing Center, Baylor College of Medicine. Her work focuses on the development and implementation of Java-based software systems that support genomic research. Passionate about continuous learning, she has a strong interest in emerging technologies particularly cloud computing (AWS) and generative AI and is driven to apply them to real-world challenges.