Evidence map›Paper›PMID 30835723›Full record

ArticlePLoS computational biology2019

New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx.

Mohamed Mounir, Marta Lucchetta, Tiago C Silva, Catharina Olsen, Gianluca Bontempi, Xi Chen, Houtan Noushmehr, Antonio Colaprico, Elena Papaleo

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 342 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
342citing papers in PubMed, 1 pooled it
23.6field-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

342 citing papers in PubMed, 1 synthesis or guideline pooled it, 593 citations in OpenAlex.

  1. Pooled it
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282 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

9 authors at 5 institutions in 4 countries.

Mohamed MounirComputational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark.
Marta LucchettaComputational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark.
Tiago C SilvaDepartment of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.ORCID 0000-0003-1343-6850
Catharina OlsenInteruniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium.
Gianluca BontempiInteruniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium.
Xi ChenSylvester Comprehensive Cancer Center, Miami, Florida, United States of America.
Houtan NoushmehrDepartment of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.
Antonio ColapricoInteruniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium.
Elena PapaleoComputational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark.ORCID 0000-0002-7376-5894
Université Libre de Bruxelles · BEDanish Cancer Society · DKUniversidade de São Paulo · BRUniversity of Copenhagen · DKUniversity of Miami · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7.

Indexed as

High-Throughput Nucleotide SequencingCarcinogenesisDatasets as TopicGenome, HumanHumansNeoplasms

Identifiers

PMID30835723
PMCPMC6420023
OpenAlexW2918849193

What OpenQuestion holds

Textmetadata
LicenceCC0
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.