Lab device and system integration are too expensive, inflexible and time-consuming due to a lack of industry-wide standards. Instead of advocating for one specific software standard, this contribution suggests another way – a lab device integration ‘rule book’. The intention is to streamline the communication between instrument vendor, IT department, and lab scientist by defining different lab instrument complexity and integration levels.

Water-Agile-Fall is a way to develop software without using full agile practices. It is the way to combine traditional planning in waterfall and execution in Agile way. Organizations are trying to execute agile practices but unable to give full control to project teams and prefer to have top down control similar to waterfall methodology. In this presentation you will learn the advantages and disadvantages of using Water-Agile-Fall, when Agile teams fall into Waterfall, hybrid model for executing projects with sample projects.

Understanding the genomic content of the microbial communities from diverse ecosystems requires environmental contextual information; however, a lack of high-quality FAIR semantics to represent such information hinders the integration and annotation of existing data. Further, no standard exists to encapsulate multi-component marine metagenomic datasets and their annotation semantics within a machine-readable framework. Here we present a platform for FAIR data and tools called Planet Microbe that leverages ontology terms to annotate, standardize, and make each dataset's metadata findable, accessible, interoperable, and reusable (FAIR). This work pioneers a complete cyberinfrastructure system that not only includes the best available metadata-rich marine-omics datasets that are cleaned, standardized, and annotated but also allows for in-depth comparative analyses, via metadata searches and embedded computational tools (Apps).

The FDA receives a high proportion of data as unstructured text. Natural Language Processing (NLP) is used to normalize information for further analysis or machine learning. A challenge is the variation in the ways that concepts are referred. Although there are a number of open source terminologies, not all are designed for text processing. We will describe some of the uses of text mining at the FDA, how to adapt terminologies, and how to extend coverage using machine learning.

In this presentation, Scott Weiss, VP of Product strategy at IDBS, will take us through building an ecosystem that not only fills a need, but also fits into your lab’s workflows to drive R&D processes and ensure data is in an accessible and digestible format.

Personalized medicine and technology advances are transforming healthcare & disease management.  Data Analytics, integration of Genomics & Multi-omics, clinical and other diagnostic technologies make precision medicine a reality through informed decisions to precisely diagnose, treat, and prevent diseases.Learn how Persistent manages and analyses diverse data sets for actionable insights

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

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.

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

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. 

We report on building a new toolkit to help life science industry implement the FAIR (Findable, Accessible, Interoperable, Reusable) principles for data management and stewardship. It provides practical support by bringing together relevant methods for tools, training and managing change, which are illustrated by use cases mostly from life science industry. These elements are assembled together as one user-friendly and freely accessible website.

A sequence similarity search often provides essential information about a DNA or protein sequence. With the rapidly expanding use of high-throughput sequencing, a few issues may occur for BLAST users. First, the need for searches may come in bursts, with many searches needing to be done at once and current resources unable to handle the load. Second, many searches are now part of a pipeline, which can be a powerful multiplier for bioinformatics tools, but is not straight-forward to maintain if it does not conform to FAIR (Findable, Accessible, Interoperable, and Reusable) principles. The cloud can help with the first problem, allowing a user to scale up the computational resources per their specific needs. Containerization and formal pipeline languages can help with the second issue, making pipelines more reproducible and easier to maintain. We discuss a containerized version of BLAST, usage with CWL, and a cloud infrastructure that includes databases hosted on cloud providers.

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.