Evidence map›Paper›PMID 28049410›Full record

ArticleBMC bioinformatics2017

TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas.

Fabio Cumbo, Giulia Fiscon, Stefano Ceri, Marco Masseroli, Emanuel Weitschek

Abstract read
In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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  5. GenoSurf: metadata driven semantic search system for integrated genomic datasets.Database : the journal of biological databases and curation · 2019
    Article
  6. Article
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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

5 authors.

Fabio CumboInstitute of Systems Analysis and Computer Science "A. Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185, Italy.
Giulia FisconInstitute of Systems Analysis and Computer Science "A. Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185, Italy.
Stefano CeriDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci 32, Milan, 20133, Italy.
Marco MasseroliDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci 32, Milan, 20133, Italy.
Emanuel WeitschekInstitute of Systems Analysis and Computer Science "A. Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185, Italy. emanuel@iasi.cnr.it.ORCID http://orcid.org/0000-0002-8045-2925

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData extraction and integration methods are becoming essential to effectively access and take advantage of the huge amounts of heterogeneous genomics and clinical data increasingly available. In this work, we focus on The Cancer Genome Atlas, a comprehensive archive of tumoral data containing the results of high-throughout experiments, mainly Next Generation Sequencing, for more than 30 cancer types.

resultsWe propose TCGA2BED a software tool to search and retrieve TCGA data, and convert them in the structured BED format for their seamless use and integration. Additionally, it supports the conversion in CSV, GTF, JSON, and XML standard formats. Furthermore, TCGA2BED extends TCGA data with information extracted from other genomic databases (i.e., NCBI Entrez Gene, HGNC, UCSC, and miRBase). We also provide and maintain an automatically updated data repository with publicly available Copy Number Variation, DNA-methylation, DNA-seq, miRNA-seq, and RNA-seq (V1,V2) experimental data of TCGA converted into the BED format, and their associated clinical and biospecimen meta data in attribute-value text format.

conclusionsThe availability of the valuable TCGA data in BED format reduces the time spent in taking advantage of them: it is possible to efficiently and effectively deal with huge amounts of cancer genomic data integratively, and to search, retrieve and extend them with additional information. The BED format facilitates the investigators allowing several knowledge discovery analyses on all tumor types in TCGA with the final aim of understanding pathological mechanisms and aiding cancer treatments.

Indexed as

User-Computer InterfaceDatabases, GeneticDNA Copy Number VariationsDNA MethylationHigh-Throughput Nucleotide SequencingHumansInternetMicroRNAsNeoplasmsSequence Analysis, DNAMicroRNAsCancerData extractionData integrationKnowledge extraction

Identifiers

PMID28049410
PMCPMC5210259

What OpenQuestion holds

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LicenceCC BY
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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.