Evidence map›Paper›PMID 28472919›Full record

ArticleBMC bioinformatics2017

WebGIVI: a web-based gene enrichment analysis and visualization tool.

Liang Sun, Yongnan Zhu, A S M Ashique Mahmood, Catalina O Tudor, Jia Ren, K Vijay-Shanker, Jian Chen, Carl J Schmidt

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PubMed Full text at PMC Publisher
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 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. In Silico Analysis of the Effect ofMedicina (Kaunas, Lithuania) · 2023
    Article
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  8. Review
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

8 authors.

Liang SunDepartment of Animal and Food Sciences, University of Delaware, Newark, DE, USA.
Yongnan ZhuDepartment of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA.
A S M Ashique MahmoodDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, 19716, USA.
Catalina O TudorDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, 19716, USA.
Jia RenCenter for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA.
K Vijay-ShankerDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, 19716, USA.
Jian ChenDepartment of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA.
Carl J SchmidtDepartment of Animal and Food Sciences, University of Delaware, Newark, DE, USA. schmidtc@udel.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task.

resultsWe have developed WebGIVI, an interactive web-based visualization tool ( http://raven.anr.udel.edu/webgivi/ ) to explore gene:iTerm pairs. WebGIVI was built via Cytoscape and Data Driven Document JavaScript libraries and can be used to relate genes to iTerms and then visualize gene and iTerm pairs. WebGIVI can accept a gene list that is used to retrieve the gene symbols and corresponding iTerm list. This list can be submitted to visualize the gene iTerm pairs using two distinct methods: a Concept Map or a Cytoscape Network Map. In addition, WebGIVI also supports uploading and visualization of any two-column tab separated data.

conclusionsWebGIVI provides an interactive and integrated network graph of gene and iTerms that allows filtering, sorting, and grouping, which can aid biologists in developing hypothesis based on the input gene lists. In addition, WebGIVI can visualize hundreds of nodes and generate a high-resolution image that is important for most of research publications. The source code can be freely downloaded at https://github.com/sunliang3361/WebGIVI . The WebGIVI tutorial is available at http://raven.anr.udel.edu/webgivi/tutorial.php .

Indexed as

GenesSoftwareData MiningGenomicsInterneteGIFTGene enrichmentGene iTermVisualizationWeb development

Identifiers

PMID28472919
PMCPMC5418709

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.