Expanding genomic analysis: Inside NVIDIA Parabricks integration with Amazon Omics

Following the recent expansion of Amazon Omics, as covered in our previous article “Ready, set, analyze: Amazon Omics unveils new Ready2Run workflows”, we wanted to explore the role of third-party providers in shaping the landscape of genomic analysis and bioinformatics. NVIDIA is one of these providers. The GPU company has worked to integrate their Parabricks Ready2Run workflows into Amazon Omics, adding new capabilities to the platform.

To get a better sense of NVIDIA’s role and perspective in this domain, we reached out to Jason Fenwick, genomics business development at the company. In the following interview, Fenwick touches on topics ranging from the unique aspects of NVIDIA’s Parabricks Ready2Run workflows to the acceleration of genome analysis over CPU-based tools. He also explores the company’s collaboration with the GATK team at the Broad Institute, and muses on the company’s NVIDIA AI Enterprise offering.

What distingu…
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Ready, set, analyze: Amazon Omics unveils new Ready2Run workflows

Amazon Web Services (AWS) recently announced a significant expansion of Amazon Omics at the annual AWS Life Sciences Executive Symposium in Boston. Amazon Omics, which the company introduced last year, helps life science organizations to store, query and analyze genomic, transcriptomic and other omics data.

Other similar tools include Qlucore Omics Explorer, Genospace, StrandOmics, Signals Translational and the publicly-funded academic-developed platform Galaxy.

Omics data encompass genomics, transcriptomics and other related fields, providing a comprehensive understanding of the genetic, transcriptional and functional elements of biological systems. Such data, instrumental for researchers studying biological systems, plays a vital role in modern drug discovery and development.

Amazon Omics introduces Ready2Run workflows

AWS aims to distinguish Amazon Omics in the marketplace through its comprehensive managed service approach and unique features such as …

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Breaking down barriers: Prioritizing diversity in clinical trials

[Image courtesy of FDA]

Diversity in clinical trials is crucial for ensuring that new medical treatments are safe and effective for all populations. Last year, the FDA released draft guidance that aims to improve the enrollment of historically underrepresented populations in clinical trials.

Demand is growing for more transparency and diversity in clinical trials, according to Erin Leckrone, senior director of clinical trials at Be The Match BioTherapies. Additionally, a growing number of stakeholders are stepping forward to promote diversity. “The COVID pandemic really amplified a lot of people’s voices in this space,” she said.

A growing number of stakeholders have stepped forward to demand better diversity in clinical trials and to understand “what data is actually collected as part of the clinical trials,” Leckron said. Those players range from regulatory agencies like the FDA to clinical trial …

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Q&A: Keys to unlock data science potential for drug discovery

Image courtesy of Pexels

For all of its promise in healthcare and elsewhere, deploying artificial intelligence is frequently a challenging endeavor. “Close collaboration between data science teams, other project team members and stakeholders is essential,” said Jennifer Bradford, director of data science at Phastar, the London-headquartered contract research organization. While input from computational, statistical or medical experts could be essential to inform data science models, all stakeholders understand the requirements and are working “in sync with the project,” Branford said.

In the following interview, Bradford shares advice on how to collaborate effectively on data science projects, the impact of COVID-19 on data science in pharma and the potential for AI to accelerate R&D timelines. 

What comes next after alignment between different stakeholders on data science projects is confirmed? <…

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