ArticleGigaScience2018
Tracking the NGS revolution: managing life science research on shared high-performance computing clusters.
Article in GigaScience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Reassessing the promise of NGS in sepsis diagnosis: overcoming scalability barriers.Future microbiology · 2025Article
- NGS coverage accurately predictsFrontiers in oncology · 2025Article
- Dynamic configuration and data security for bioinformatics cloud services with the Laniakea Dashboard.NAR genomics and bioinformatics · 2024Article
- 0s and 1s in marine molecular research: a regional HPC perspective.GigaScience · 2021Article
- MaRe: Processing Big Data with application containers on Apache Spark.GigaScience · 2020Article
- FQStat: a parallel architecture for very high-speed assessment of sequencing quality metrics.BMC bioinformatics · 2019Article
- SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines.GigaScience · 2019Article
- On-demand virtual research environments using microservices.PeerJ. Computer science · 2019Article
- Tracking the NGS revolution: managing life science research on shared high-performance computing clusters.GigaScience · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Next-generation sequencing (NGS) has transformed the life sciences, and many research groups are newly dependent upon computer clusters to store and analyze large datasets. This creates challenges for e-infrastructures accustomed to hosting computationally mature research in other sciences. Using data gathered from our own clusters at UPPMAX computing center at Uppsala University, Sweden, where core hour usage of ∼800 NGS and ∼200 non-NGS projects is now similar, we compare and contrast the growth, administrative burden, and cluster usage of NGS projects with projects from other sciences. Results: The number of NGS projects has grown rapidly since 2010, with growth driven by entry of new research groups. Storage used by NGS projects has grown more rapidly since 2013 and is now limited by disk capacity. NGS users submit nearly twice as many support tickets per user, and 11 more tools are installed each month for NGS projects than for non-NGS projects. We developed usage and efficiency metrics and show that computing jobs for NGS projects use more RAM than non-NGS projects, are more variable in core usage, and rarely span multiple nodes. NGS jobs use booked resources less efficiently for a variety of reasons. Active monitoring can improve this somewhat. Conclusions: Hosting NGS projects imposes a large administrative burden at UPPMAX due to large numbers of inexperienced users and diverse and rapidly evolving research areas. We provide a set of recommendations for e-infrastructures that host NGS research projects. We provide anonymized versions of our storage, job, and efficiency databases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.