Evidence map›Paper›PMID 33619257›Full record

ArticleNature communications2021

Uniform genomic data analysis in the NCI Genomic Data Commons.

Zhenyu Zhang, Kyle Hernandez, Jeremiah Savage, Shenglai Li, Dan Miller, Stuti Agrawal, Francisco Ortuno, Louis M Staudt, Allison Heath, Robert L Grossman

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
95citing papers in PubMed, 2 pooled it
12.8field-weighted citation impact, top 1% of its field
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

95 citing papers in PubMed, 2 syntheses or guidelines pooled it, 141 citations in OpenAlex.

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35 more citing papers are in PubMed but not listed here.

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

10 authors at 5 institutions in 3 countries.

Zhenyu ZhangCenter for Translational Data Science, University of Chicago, Chicago, IL, USA.ORCID 0000-0001-6655-053X
Kyle HernandezCenter for Translational Data Science, University of Chicago, Chicago, IL, USA.ORCID 0000-0001-8267-644X
Jeremiah SavageCenter for Translational Data Science, University of Chicago, Chicago, IL, USA.ORCID 0000-0003-2391-1950
Shenglai LiCenter for Translational Data Science, University of Chicago, Chicago, IL, USA.
Dan MillerCenter for Translational Data Science, University of Chicago, Chicago, IL, USA.ORCID 0000-0002-2032-4358
Stuti AgrawalCenter for Translational Data Science, University of Chicago, Chicago, IL, USA.ORCID 0000-0002-2999-2192
Francisco OrtunoCenter for Translational Data Science, University of Chicago, Chicago, IL, USA.ORCID 0000-0001-7424-1753
Louis M StaudtNational Cancer Institute, Bethesda, MD, USA.
Allison HeathChildren's Hospital of Philadelphia, Philadelphia, PA, USA.ORCID 0000-0002-2583-9668
Robert L GrossmanCenter for Translational Data Science, University of Chicago, Chicago, IL, USA. robert.grossman@uchicago.edu.ORCID 0000-0003-3741-5739
University of Chicago · USChildren's Hospital of Philadelphia · USAbbVie (United States) · USMerck (Germany) · DENational Cancer Institute · US

Funding

CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001E
6 · The paper itself

Abstract

The goal of the National Cancer Institute's (NCI's) Genomic Data Commons (GDC) is to provide the cancer research community with a data repository of uniformly processed genomic and associated clinical data that enables data sharing and collaborative analysis in the support of precision medicine. The initial GDC dataset include genomic, epigenomic, proteomic, clinical and other data from the NCI TCGA and TARGET programs. Data production for the GDC started in June, 2015 using an OpenStack-based private cloud. By June of 2016, the GDC had analyzed more than 50,000 raw sequencing data inputs, as well as multiple other data types. Using the latest human genome reference build GRCh38, the GDC generated a variety of data types from aligned reads to somatic mutations, gene expression, miRNA expression, DNA methylation status, and copy number variation. In this paper, we describe the pipelines and workflows used to process and harmonize the data in the GDC. The generated data, as well as the original input files from TCGA and TARGET, are available for download and exploratory analysis at the GDC Data Portal and Legacy Archive ( https://gdc.cancer.gov/ ).

Indexed as

Data AnalysisDatabases, GeneticGenomicsBase SequenceDNA Copy Number VariationsDNA MethylationGene Expression RegulationGenome, HumanHumansMicroRNAsMolecular Sequence AnnotationMutationNational Cancer Institute (U.S.)Reproducibility of ResultsRNA-SeqUnited StatesMicroRNAs

Identifiers

PMID33619257
PMCPMC7900240
OpenAlexW3133283981

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.