2010 Best Practices Competition IT & Informatics HPC

Published Resources for the Life Sciences
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Technology ROI:
Individual research groups no longer have to over‐spec IT solutions to meet peak requirements. By
moving towards centralized solutions, there are substantial gains thanks to the coordination of
staff, computer halls, etc.
Research ROI:
An example research project, that leveraged UPPNEX, has reduced its time‐to‐completion by several
months . The project focused on gaining a deeper understanding of the relationship between
genetic variation and phenotypic variation. Through whole genome resequencing, the researchers
distinguished key genes causing the differences between wild and domestic chickens. They have
identified candidate mutations that cause special effects on the phenotype. This is an efficient
strategy to increase our understanding of how different genes control different traits.
One gene, associated with the fast growth of broiler chickens, is associated with obesity in humans.
The study established a new animal model that can be used to explore the mechanics of how this
gene influences human growth and obesity.
Lastly, the domestic chicken is the most important global source of animal protein. The research has
established the possibility to develop domestic chickens that are extremely efficient producers of
animal proteins, namely eggs and meat.
E. CONCLUSIONS/implications for the field.
The recent technological advancements, affordability and wide deployment of NGS machines is
feeding a tsunami of digital data. The information technology infrastructure required to compute
and store such vast amounts of data is beyond the funding of Individual research groups.
Centralized HPC and data‐storage facilities are being deployed at regional, national and global level
to provide researchers with access to the IT infrastructure they require.
The challenge for the centralized facilities is to provide sufficient compute and data‐storage
resources to fuel multiple research projects simultaneously. With ever‐increasing amounts of
digital data being ingested, how do they process, manage and store the data both reliably and
efficiently.
Traditional storage technologies cannot keep pace. Their limitations on capacity encourage data
silos, multiple copies of data, system administration headaches and an escalating management
overhead. Clustered storage technologies struggle to address diverse performance requirements
within the life sciences workflow, again encouraging data silos and disparate storage management
layers.