Evidence map›Paper›PMID 27788220›Full record

ArticlePloS one2016

TCGA Expedition: A Data Acquisition and Management System for TCGA Data.

Uma R Chandran, Olga P Medvedeva, M Michael Barmada, Philip D Blood, Anish Chakka, Soumya Luthra, Antonio Ferreira, Kim F Wong, Adrian V Lee, Zhihui Zhang and 5 more

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

  1. Article
  2. Significance ofPeerJ · 2024
    Article
  3. Article
  4. Article
  5. Article
  6. Data Provenance in Biomedical Research: Scoping Review.Journal of medical Internet research · 2023
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  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

15 authors.

Uma R ChandranDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America.
Olga P MedvedevaDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America.
M Michael BarmadaDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America.
Philip D BloodPittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, United States of America.
Anish ChakkaDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America.
Soumya LuthraDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America.
Antonio FerreiraCenter for Simulation and Modeling, University of Pittsburgh, Pittsburgh, PA, United States of America.
Kim F WongCenter for Simulation and Modeling, University of Pittsburgh, Pittsburgh, PA, United States of America.
Adrian V LeeUniversity of Pittsburgh Cancer Institute, Pittsburgh, PA, United States of America.
Zhihui ZhangPittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, United States of America.
Robert BuddenPittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, United States of America.
J Ray ScottPittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, United States of America.
Annerose BerndtUPMC Corporate Services, Pittsburgh, PA, United States of America.
Jeremy M BergInstitute for Precision Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.
Rebecca S JacobsonDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America.

Funding

VECTOR CORE FACILITYP30CA047904 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Jessie Nedrow · 1988 to 2026
$158.0M
NCI NIH HHS P30 CA047904
6 · The paper itself

Abstract

backgroundThe Cancer Genome Atlas Project (TCGA) is a National Cancer Institute effort to profile at least 500 cases of 20 different tumor types using genomic platforms and to make these data, both raw and processed, available to all researchers. TCGA data are currently over 1.2 Petabyte in size and include whole genome sequence (WGS), whole exome sequence, methylation, RNA expression, proteomic, and clinical datasets. Publicly accessible TCGA data are released through public portals, but many challenges exist in navigating and using data obtained from these sites. We developed TCGA Expedition to support the research community focused on computational methods for cancer research. Data obtained, versioned, and archived using TCGA Expedition supports command line access at high-performance computing facilities as well as some functionality with third party tools. For a subset of TCGA data collected at University of Pittsburgh, we also re-associate TCGA data with de-identified data from the electronic health records. Here we describe the software as well as the architecture of our repository, methods for loading of TCGA data to multiple platforms, and security and regulatory controls that conform to federal best practices.

resultsTCGA Expedition software consists of a set of scripts written in Bash, Python and Java that download, extract, harmonize, version and store all TCGA data and metadata. The software generates a versioned, participant- and sample-centered, local TCGA data directory with metadata structures that directly reference the local data files as well as the original data files. The software supports flexible searches of the data via a web portal, user-centric data tracking tools, and data provenance tools. Using this software, we created a collaborative repository, the Pittsburgh Genome Resource Repository (PGRR) that enabled investigators at our institution to work with all TCGA data formats, and to interrogate these data with analysis pipelines, and associated tools. WGS data are especially challenging for individual investigators to use, due to issues with downloading, storage, and processing; having locally accessible WGS BAM files has proven invaluable.

conclusionOur open-source, freely available TCGA Expedition software can be used to create a local collaborative infrastructure for acquiring, managing, and analyzing TCGA data and other large public datasets.

Indexed as

Database Management SystemsGenomicsHumansInformation Storage and RetrievalNeoplasmsSoftwareUser-Computer Interface

Identifiers

PMID27788220
PMCPMC5082933

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.