Customer Stories / Life Sciences

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Agilent Improves the Performance of Its Genomics Software and Saves Time Using AWS

Learn how genomic science and technology company Agilent Technologies is using AWS to unlock the full potential of molecular diagnostics.


reduction in time to run secondary analysis


of workflows run in parallel

Empowers researchers

to analyze genomic data at scale


the faster recommendation of targeted therapies

Optimizes costs

with per-second billing


Millions of people every year are diagnosed with serious diseases, including cancer. Genetic mutations are often the root cause of these conditions, which raises a crucial question: what if a patient’s DNA could be used to develop a targeted treatment plan?

Agilent Technologies (Agilent) is addressing this challenge with the Alissa Clinical Informatics Platform, powered by Amazon Web Services (AWS). This solution streamlines next-generation sequencing and genome analysis studies to search for genetic variants. It interprets this data and generates a report to help clinicians select the most effective therapies.

By adopting a suite of AWS services, Agilent can run these complex workloads quickly and at scale, helping clinical geneticists and molecular pathologists analyze and decode genetic information faster.

Opportunity | Using AWS Services to Improve Agility and Performance for Agilent Technologies

Established in 1999, Agilent is a life sciences company that provides software, instruments, and consumables for scientists. Its genomics division helps researchers unlock the potential of genomic data.
“The burden of data interpretation and reporting increases as labs expand their testing menu to include more genes, increasing the number of variants that need to be analyzed—making it critical to be able to prioritize clinically relevant variants that might play a role in the patient’s phenotype or tumor type,” says Elias Hage, product manager of tertiary analysis software solutions at Agilent. “Our software helps clinical genetics and molecular-pathology labs reduce turnaround time and increase productivity through standardization and automation of triage, review, and classification of genomic data.”
The Alissa Clinical Informatics Platform has three components: Alissa Reporter, which analyzes raw sequencing data and identifies variants; Alissa Interpret, which interprets and annotates the variants; and Alissa Annotation Server, which compiles annotation sources.

From the beginning, Alissa Interpret and Alissa Annotation Server have run on AWS. To improve performance and scalability, Agilent migrated Alissa Reporter from a third-party data center to AWS in 2018. It engaged AWS Enterprise Support, which provides 24-7 technical support from high-quality engineers, to optimize the process. “We started using AWS from the very beginning because our workloads needed massive amounts of compute power and storage,” says Gert Thijs, product manager of Alissa Annotation Server. “AWS has given us the needed flexibility and has made our solution platform agnostic.”


On AWS, we make it possible for customers to get fast results. We can speed up development and pass on time and cost savings."

Joachim de Schrijver
Product Owner of Alissa Reporter, Agilent

Solution | Reducing Time for Secondary Analysis by Over 96 Percent

To run large-scale genome analyses, Agilent uses Amazon Elastic Compute Cloud (Amazon EC2), which provides secure and resizable compute capacity for virtually any workload. Agilent can select the best available compute instances for a certain computation using AWS Auto Scaling, which automates application scaling to optimize performance and costs. Amazon CloudWatch, a service that collects and visualizes near-real-time logs, metrics, and event data in automated dashboards, is used to observe and monitor Agilent’s resources and applications on AWS.

Agilent implemented hardware-optimized variant-calling algorithms developed by NVIDIA Clara Parabricks that require GPUs instead of regular CPUs. To improve processing speeds for its variant-calling workflows, the company adopted Amazon EC2 G4 Instances, the industry’s most cost-effective GPU instances for machine learning (ML) inference and graphics-intensive applications, and Amazon EC2 G5 Instances, the latest generation of NVIDIA GPU-based instances. Using AWS Auto Scaling groups, Agilent can define the most appropriate instances for a certain computation. With this implementation, Alissa Reporter can complete a secondary analysis (the mapping a calling step) in 9 minutes instead of 9 hours—a 96 percent reduction—and run hundreds of workloads in parallel.

Amazon CloudWatch collects and visualizes near-real-time logs, metrics, and event data in automated dashboards, which helps Agilent streamline infrastructure and application maintenance. “Customers are very happy with the decrease in turnaround time,” says Christina Hörnlein, product manager of Alissa Reporter at Agilent. “They’re pleased with the scalability as well. When you can just analyze samples in parallel, you don’t have to prioritize workloads.”

Outside of getting results faster, Agilent can also reduce costs for customers with per-second billing on AWS. “We have computations that can take hours, and some just minutes,” says Thijs. “This makes the per-second billing very convenient to scale automatically.”

Because Agilent’s customers work with sensitive health information, its systems must comply with HIPAA and ISO requirements. For example, the risk of inadvertent access must be low, and all data must be encrypted and shielded from the outside internet. On AWS, Agilent can configure frameworks that meet these regulations. “We have an AWS business agreement that demonstrates that our services are running in a secure environment,” says Thijs. “This is recognized by different authorities who certify our product and shows that we are compliant with clinical data-interpretation rules.”

The benefits extend to Alissa Interpret and Alissa Annotation Server. On AWS, Agilent hosts the different knowledge sources needed for annotation and interpretation, with about 1.5 TB of annotation data served to all connected applications. With flexible AWS architecture, Agilent can scale as its customers grow. “Our customers typically start with a few analyses but scale fast thanks to all of these automation capabilities,” says Thijs. “If we see a customer increase their analysis volumes, we can scale to grow with them without causing any service interruptions.”

Outcome | Empowering Researchers to Analyze Genomic Data at Scale

Faster analysis and interpretation of genomic data are helping researchers suggest targeted therapies to doctors sooner. Scientists can broaden their research and recommend more treatments than they could previously. “On AWS, we make it possible for customers to get fast results,” says Joachim de Schrijver, product owner of Alissa Reporter. “If something takes 15 minutes instead of 8 hours, you can run 50 parameter sets in a single day, where it might take a month to get the same results. We can speed up development and pass on time and cost savings to the customer.”

The team is also looking to explore ML for annotating genetic data with Amazon SageMaker, a service used to build, train, and deploy ML models. “As more models emerge, we are trying to see how we can integrate these algorithms into our pipelines using Amazon SageMaker,” says Thijs.
In the future, Agilent will explore new technologies to accelerate analysis, save money, and improve reliability—all to help patients achieve better outcomes. “On AWS, we help people get a faster analysis of their cancer so that they can access custom-made medication,” says Remi Bruggeman, DevOpsSec lead at Agilent. “We are certainly touching patients’ lives and hopefully extending them.”

About Agilent Technologies

Agilent Technologies provides software, instruments, services, and consumable products to support scientists in 110 countries. Headquartered in Santa Clara, California, the global technology company’s mission is to bring great science to life.

AWS Services Used

Amazon EC2

Amazon Elastic Compute Cloud (Amazon EC2) offers the broadest and deepest compute platform to help you best match the needs of your workload.

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AWS Auto Scaling

AWS Auto Scaling monitors your applications and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost.

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Amazon CloudWatch

Amazon CloudWatch collects and visualizes real-time logs, metrics, and event data in automated dashboards to streamline your infrastructure and application maintenance.

Learn more »

Amazon SageMaker

Amazon SageMaker helps you build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows

Learn more »

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