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Tracking the NGS revolution: managing life science research on shared high-performance computing clusters.

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Next-generation sequencing (NGS) projects strain e-infrastructures with rapid growth and high resource demands. Managing these computationally intensive projects requires specialized support and infrastructure adjustments for optimal efficiency.

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Area of Science:

  • Life Sciences
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) has become integral to life sciences research, increasing reliance on computational clusters for data analysis.
  • Existing e-infrastructures face challenges adapting to the unique demands of NGS projects compared to computationally mature research in other fields.

Purpose of the Study:

  • To compare and contrast the growth, administrative burden, and cluster usage patterns of NGS projects with those from other scientific disciplines.
  • To identify challenges and provide recommendations for e-infrastructures managing NGS research.

Main Methods:

  • Analysis of cluster usage data from UPPMAX computing center, comparing approximately 800 NGS projects with 200 non-NGS projects.
  • Development and application of usage and efficiency metrics to evaluate computational job performance.

Main Results:

  • NGS projects have shown rapid growth since 2010, with storage demands increasing significantly since 2013, nearing disk capacity limits.
  • NGS users generate nearly double the support tickets per user and necessitate more frequent tool installations.
  • NGS jobs exhibit higher RAM usage, greater core usage variability, and less efficient resource utilization compared to non-NGS projects.

Conclusions:

  • Hosting NGS projects presents a substantial administrative burden due to a large number of inexperienced users and rapidly evolving research areas.
  • Recommendations are provided for e-infrastructures to better support NGS research.
  • Anonymized databases of storage, job, and efficiency metrics are made available.