GalaxyCloudRunner: enhancing scalable computing for Galaxy.

Nuwan Goonasekera, Alexandru Mahmoud, John Chilton, Enis Afgan

Abstract read

In one paragraph

Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nuwan GoonasekeraMelbourne Bioinformatics, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC 3010, Australia.

Alexandru MahmoudDepartment of Biology, Johns Hopkins University, Baltimore, MD 21218, USA.

John ChiltonDepartment of Biochemistry and Molecular Biology, Penn State University, State College, PA 16801, USA.

Enis AfganDepartment of Biology, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0003-0922-6711

Funding

Democratization of Data Analysis in Life Sciences Through GalaxyU41HG006620 · NHGRI · PENNSYLVANIA STATE UNIVERSITY, THE · PI NEKRUTENKO, ANTON, SCHATZ, MICHAEL · 2012 to 2020

$14.4M

Democratization of Data Analysis in Life Sciences Through GalaxyU24HG006620 · NHGRI · PENNSYLVANIA STATE UNIVERSITY, THE · PI Daniel James Blankenberg, Jeremy Goecks · 2021 to 2026

$9.8M

NHGRI NIH HHS U24 HG006620NHGRI NIH HHS U41 HG006620NIH HHS 5U41HG006620-07

6 · The paper itself

Abstract

summaryThe existence of more than 100 public Galaxy servers with service quotas is indicative of the need for an increased availability of compute resources for Galaxy to use. The GalaxyCloudRunner enables a Galaxy server to easily expand its available compute capacity by sending user jobs to cloud resources. User jobs are routed to the acquired resources based on a set of configurable rules and the resources can be dynamically acquired from any of four popular cloud providers (AWS, Azure, GCP or OpenStack) in an automated fashion. AVAILABILITY AND IMPLEMENTATION: GalaxyCloudRunner is implemented in Python and leverages Docker containers. The source code is MIT licensed and available at https://github.com/cloudve/galaxycloudrunner. The documentation is available at http://gcr.cloudve.org/.

Indexed as

Computational BiologySoftwareAzure StainsDocumentationHumansAzure Stains

Identifiers

PMID33104194

DOI10.1093/bioinformatics/btaa860

PMCPMC8289383

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.