Evidence map›Paper›PMID 28344774›Full record

ArticleF1000Research2017

The Dockstore: enabling modular, community-focused sharing of Docker-based genomics tools and workflows.

Brian D O'Connor, Denis Yuen, Vincent Chung, Andrew G Duncan, Xiang Kun Liu, Janice Patricia, Benedict Paten, Lincoln Stein, Vincent Ferretti

Abstract read
In one paragraph

Article in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing 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

50 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023
    Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. IoT cloud laboratory: Internet of Things architecture for cellular biology.Internet of things (Amsterdam, Netherlands) · 2022
    Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
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

9 authors.

Brian D O'ConnorUC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA.
Denis YuenOntario Institute for Cancer Research, MaRS Centre, Toronto, Canada.ORCID 0000-0002-6130-1021
Vincent ChungOntario Institute for Cancer Research, MaRS Centre, Toronto, Canada.
Andrew G DuncanOntario Institute for Cancer Research, MaRS Centre, Toronto, Canada.
Xiang Kun LiuOntario Institute for Cancer Research, MaRS Centre, Toronto, Canada.
Janice PatriciaOntario Institute for Cancer Research, MaRS Centre, Toronto, Canada.
Benedict PatenUC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA.
Lincoln SteinOntario Institute for Cancer Research, MaRS Centre, Toronto, Canada.
Vincent FerrettiOntario Institute for Cancer Research, MaRS Centre, Toronto, Canada.

Funding

Training SubprojectU54HG007990 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI HAUSSLER, DAVID H, PATTERSON, DAVID · 2014 to 2018
$14.8M
Dockstore - A Platform for Sharing Tools & Workflows on the Cloud CommonsR01HG009737 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI PATEN, BENEDICT, PHILIPPAKIS, ANTHONY · 2017 to 2020
$3.0M
NHGRI NIH HHS R01 HG009737
6 · The paper itself

Abstract

As genomic datasets continue to grow, the feasibility of downloading data to a local organization and running analysis on a traditional compute environment is becoming increasingly problematic. Current large-scale projects, such as the ICGC PanCancer Analysis of Whole Genomes (PCAWG), the Data Platform for the U.S. Precision Medicine Initiative, and the NIH Big Data to Knowledge Center for Translational Genomics, are using cloud-based infrastructure to both host and perform analysis across large data sets. In PCAWG, over 5,800 whole human genomes were aligned and variant called across 14 cloud and HPC environments; the processed data was then made available on the cloud for further analysis and sharing. If run locally, an operation at this scale would have monopolized a typical academic data centre for many months, and would have presented major challenges for data storage and distribution. However, this scale is increasingly typical for genomics projects and necessitates a rethink of how analytical tools are packaged and moved to the data. For PCAWG, we embraced the use of highly portable Docker images for encapsulating and sharing complex alignment and variant calling workflows across highly variable environments. While successful, this endeavor revealed a limitation in Docker containers, namely the lack of a standardized way to describe and execute the tools encapsulated inside the container. As a result, we created the Dockstore ( https://dockstore.org), a project that brings together Docker images with standardized, machine-readable ways of describing and running the tools contained within. This service greatly improves the sharing and reuse of genomics tools and promotes interoperability with similar projects through emerging web service standards developed by the Global Alliance for Genomics and Health (GA4GH).

Indexed as

big databioinformaticscloudcontainersDockergenomics

Identifiers

PMID28344774
PMCPMC5333608

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.