As commercial, governmental, and research organizations continue to move from manual pipelines to automated processing of their vast and growing datasets, they are struggling to find meaning in their repositories. With an open, policy-based platform, metadata can be elevated beyond assisting in just search and discoverability. Metadata can associate datasets, help build cohorts for analysis, coordinate data movement and scheduling, and drive the very policy that provides the data governance. Data management should be data centric, and metadata driven.

The topic of an NIH Data Commons has been an area of great interest and activity, as has the general FAIR data movement. These broad notions are playing out with a future focus while NIEHS works to build its own Data Commons to manage today’s research data. Managing daily work while observing future trends, incorporating key capabilities, often in a tentative and piecemeal fashion, without losing sight of the big picture; this is the challenge we all face.

Next-generation sequencing (NGS) is routinely being used in cancer research. This produces large amounts of data during data collection and as it gets processed through the pipeline. Tracking and apply context to large amount of data becomes a challenge. Using AWS as a means of data delivery and processing, has the advantage of automating data delivery and processing. In addition, applying context to the data using iRODS can be automated as data gets delivered and processed. This will provide a primary source of metadata that can be used by other applications downstream.

Life sciences researchers must contend with data spread across a wide variety of storage system types, including on-premises and cloud storage. In order to efficiently and reproducibly execute data analysis pipelines, researchers need secure and sophisticated data management capabilities that provide unified data access irrespective of storage location and type. In this talk, we will discuss how Globus provides a solution to this challenge, including a success story of a popular data analysis system built on the Globus framework.

AWS has developed a Reference Architecture for compression and read back using PetaGene’s PetaSuite Cloud Edition and deployed it with mutual client AstraZeneca. By doing so, AstraZeneca has reduced their storage footprint, sped up data ingress and egress, and accelerated analysis, while maintaining the data in a fully-accessible format.

Learn how Igneous is helping a leading research institute create efficiencies in data management that are reducing storage costs by over 64% and migrating 4PB of data to the cloud in just weeks, all while maintaining the much-needed visibility which allows end-users to make informed decisions.

Join us for a lively discussion among prominent pharma leaders, and learn:

Why, when & how to implement a public Cloud for your computing needs

Challenges and opportunities when setting and managing stakeholder expectations

Critical keys to success to realize the best outcomes

To learn more about RCH Solutions, visit our Virtual Booth

Hosted by Joe Donahue, Managing Director, Life Sciences, Accenture

Participants include: 

Andreas Matern, Head of Digital Translational Medicine, Sanofi

John Quackenbush, Professor of Computational Biology and Bioinformatics; Harvard T.H. Chan School of Public Health

Seungtaek Lee, VP, Strategic Partnerships and AI RWE Head of CoE; ConcertAI

Preston Keller, PhD, MBA, President & CCO, PercayAI

Philip Payne, PhD, Becker Professor and Chief Data Scientist, Washington University in St. Louis

Most large scale analysis of clinical trial data only leverages part of the picture, ignoring unstructured data and limiting findability across all the information collected throughout multiple disparate data sources.  This roundtable will discuss leveraging a cognitive platform to combine all data from multiple sources into one unified view using a single entry point to the data.

Evaluating, optimizing and benchmarking of next generation sequencing (NGS) methods are essential for clinical, commercial and academic NGS pipelines. Optimizations for speed and accuracy often require making trade-offs relative to other constraints. Join this roundtable to discuss benchmarking strategies, trade-offs, and the value of benchmarking genomics tools and applications. 

When it comes to moving and managing life science and bioinformatics data, performance is influenced by a multitude of factors. Researchers want their data management solutions to “just work,” without concern for what tend to be esoteric technical details. But delivering tools that simplify a researcher’s life requires a tradeoff between performance, reliability, usability, security, compliance, and technical complexity. In this talk we will explore how service providers should evaluate and prioritize these and other factors, and illustrate how the Globus service strikes an optimal balance, making it the de facto data management solution for over 100,000 researchers around the world. We will present case study examples, and attendees will be encouraged to share their own experiences.

Dell and Faction share insights and results from a multi-cloud architecture that tests the NVIDIA Clara Parabricks application across multiple CSPs with the latest 1000 Genomes high coverage data on a cloud-adjacent Dell PowerScale appliance.

Biotechnology companies are facing new challenges in the amount of data that needs processing for genomics analysis. What used to be Terabytes of data is now petabytes of data and beyond. This data needs to be collected, analyzed, processed and then ultimately retained for compliance and research purposes - resulting in massive data storage and management challenges, unsolvable by legacy technology solutions. Our session will explain how to leverage new all-flash storage and hybrid-cloud solutions to make genomics analysis run quantum leaps faster than before.

How do you use data / digitization today to drive scientific discovery / product development?

What are you greatest scientific pain points / gaps that are not being met by digitization?

What kinds of outcomes do you believe digital tools could help you achieve?

Welcome to this discussion group on the growth of demand for HPC in scientific research. We are looking forward to a lively forum. We'll start by looking at three related topics:

- What events trigger demand in your organization? How has the current pandemic impacted resources?

- What could make scale and collaboration more accessible to more researchers?

- Share a recent experience of shifting workloads to manage HPC capacity.

In this session we’ll discuss how to provide researchers with performance and scale in genomics & research analytics, to drive results at a price point that’s economically viable on public & private cloud.

Storage solutions we’ve been using force bioinformaticists to make trade-offs between the capacity and low-cost of disk and the performance of flash. This results in complex tiering configurations that only deliver performance for a small slice of the data. In this session, we will review how advancements in technology enable VAST Data to revolutionize the cost of all-flash and allows bioinformatists faster analysis across larger datasets for deeper insights.