Three Ways to Leverage Translational Research for the Understanding of Complex Disease

Translational Research

Not so long ago, medical records were scribed on paper. A doctor’s office visit or hospital procedure yielded a paper record that appended a patient’s physical file. Imagine how difficult it was to retrieve files and maintain them without the benefit of computer searches!

Times have changed, and the vast majority of medical providers now use either electronic medical record (EMR) or electronic health record (EHR) systems. These records generally include diagnosis codes, procedure codes, clinical labs, medications, clinician notes, and imaging. While the original intent of these systems was to provide better patient care, they were also grounded in the need to track medical procedures for the purpose of billing.

Good news: recent literature suggests EHRs are helping to improve care [1]. And for their next act, EHRs will be instrumental in facilitating translational research.

How? According to Marylyn Ritchie, PhD, Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, with consent given by patients, EHRs represent a cost effective and straightforward method for medical centers and pharmaceutical companies to gain access to diverse phenotypic data for performing translational research. Additionally, states Ritchie, they are even more effective when combined with biobanks.

Voila! Cost Effective Recruitment, Continuous Data Collection, and Diverse Phenotypes

When you compare EHRs and biobanks to a typical epidemiology study, it’s easy to see why they are well suited for research studies. A typical epidemiology study involves recruiting people, incentivizing them to participate, and asking them back at regular intervals to gather additional information. The process of follow-up and collecting longitudinal data is difficult.

EHRs and biobanks offer a less expensive, more simplified route to gaining phenotypic and environmental data. If patients have consented and enrolled, then their data are already available and get updated with each new visit.

And astute researchers began to ask: why aren’t we using these data to gain a greater understanding of complex disease? After all, EHR data, when combined with biobank data, can offer us the holy grail: genomic data combined with rich phenotypic and environmental data.

There are three practical ways to use these data in translational research. Watch the video below to learn more.

Works Cited

  1. Manca DP. Do electronic medical records improve quality of care? Yes. Canadian family physician Medecin de famille canadien. Published October 2015. Accessed January 29, 2020.
  2. Mills MC, Rahal C. A scientometric review of genome-wide association studies. Communications biology. Published January 7, 2019. Accessed January 29, 2020.
  3. Verma A, Lucas A, Verma SS, et al. PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger. Am J Hum Genet. 2018;102(4):592-608.

2022 Forecast: Responding to the Rising Tide of Diagnostic Tests

Next-generation sequencing (NGS) has reached a turning point in diagnosing and treating rare and inherited diseases, and is overtaking traditional approaches in a wide variety of other indications as well, from cancer to rheumatology, transplants to non-invasive prenatal testing.  Lower sequencing costs improved reimbursement, and consumer demand is likely to drive the market even further. 

Businesses in this rapidly growing market sector have a lot to consider when it comes to technology. In a recent article in The Pathologist, David Fenstermacher, Vice President of Precision Medicine and Data Science, shares some tips to help laboratories leverage cloud-based informatics solutions. 

As he points out, cloud-based systems enable you to optimize analysis pipelines for quality, speed, runtime, and cost. They provide an environment that can flexibly scale to meet the demand for increased test volume, and are great for those looking to expand their footprint, either locally or globally.When it comes to security, compliance, and intellectual property (IP) protection, going with a purpose-built NGS informatics platform provided by a well-established, customer-focused, company can be invaluable. 

Ultimately, Clinical Diagnostics companies need to stay ahead of innovation. With a purpose-built NGS genomics platform, the latest technological advancements in cloud computing, analytics, knowledge or rich visualizations are already incorporated. Diagnostics teams can focus on what they do best, test development and delivery of results. 

Fenstermacher advises that labs ask themselves the following: 

  • How will the changing genetic testing landscape impact my operations and support needs?
  • Is my informatics system sufficient?
  • Is it scalable?
  • Does it give me the flexibility I need?
  • How does it handle quality, security, and compliance?
  • Can it help me improve my sample turnaround time or pipeline development?

“By keeping pace with technology and industry innovations in the NGS and genomics field, you can ensure that you are not only ahead of the tide, but making your own waves,” Fenstermacher said.

Read the full article here. 
Learn more about how DNANexus can help grow your diagnostic business.

Partnering with NVIDIA Clara Parabricks to Accelerate NGS Data Processing

Looking for ways to accelerate data processing and genomic analysis? By using graphics processing units (GPUs), it’s possible to achieve throughput comparable to nearly 40-50 CPU servers with just one GPU server reducing overall operating costs.

We’re excited to be working with NVIDIA Clara Parabricks to make the DNA and RNA pipelines available on our DNAnexus Titan or Apollo Platforms. For a limited time, we are offering license-free access to test, validate, and use the following Clara Parabricks pipelines (more on the way):

GATK4 and DeepVariant users will appreciate NVIDIA Clara Parabricks Pipelines. They provide equivalent results, but at much greater throughput and speed — 50-90 times faster. In fact, Parabricks Pipelines v3.0 achieved a record for the germline analysis of a whole human genome in under 20 minutes when running on the just-announced NVIDIA A100 GPUs. On DNAnexus today, GATK analyses which take nearly 30 hours of computation on a 32-vCPU machine complete, will complete in 30 minutes with identical results on an eight v100 GPU instance using Parabricks.  The Parabricks Pipelines are compatible with GATK4.1 and DeepVariant 0.10.0, the latest major releases for both analysis packages.

The tools are also fully configurable, so users can choose to run the full GATK4 best practices, for instance, or specific steps from the pipeline.

DNAnexus makes it easy to run NVIDIA Parabricks pipelines by providing an out-of-the-box solution. Users simply supply input files and receive results delivered in their DNAnexus project. Launching NVIDIA Parabricks apps is a seamless process, removing the hassle of provisioning GPU instances in the cloud manually or installing CUDA or NVIDIA Parabricks apps themselves.

For secondary analyses of sequencer-generated FASTQ files to variant call files (VCFs), we recommend you register today to take advantage of this limited-time, license-free opportunity to try out Parabricks available through June 30th