Evidence map›Paper›PMID 25929466›Full record

ArticleBMC bioinformatics2015

VISIONET: intuitive visualisation of overlapping transcription factor networks, with applications in cardiogenic gene discovery.

Hieu T Nim, Milena B Furtado, Mauro W Costa, Nadia A Rosenthal, Hiroaki Kitano, Sarah E Boyd

Abstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Hieu T NimSystems Biology Institute (SBI) Australia, Monash University, Clayton, VIC, 3800, Australia. hieu.nim@monash.edu.
Milena B FurtadoAustralian Regenerative Medicine Institute, Monash University, Clayton, VIC, 3800, Australia. milena.furtado@monash.edu.
Mauro W CostaAustralian Regenerative Medicine Institute, Monash University, Clayton, VIC, 3800, Australia. mauro.dacosta@monash.edu.
Nadia A RosenthalSystems Biology Institute (SBI) Australia, Monash University, Clayton, VIC, 3800, Australia. nadia.rosenthal@monash.edu.
Hiroaki KitanoSystems Biology Institute (SBI) Australia, Monash University, Clayton, VIC, 3800, Australia. kitano@sbi.jp.
Sarah E BoydSystems Biology Institute (SBI) Australia, Monash University, Clayton, VIC, 3800, Australia. sarah.boyd@monash.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExisting de novo software platforms have largely overlooked a valuable resource, the expertise of the intended biologist users. Typical data representations such as long gene lists, or highly dense and overlapping transcription factor networks often hinder biologists from relating these results to their expertise.

resultsVISIONET, a streamlined visualisation tool built from experimental needs, enables biologists to transform large and dense overlapping transcription factor networks into sparse human-readable graphs via numerically filtering. The VISIONET interface allows users without a computing background to interactively explore and filter their data, and empowers them to apply their specialist knowledge on far more complex and substantial data sets than is currently possible. Applying VISIONET to the Tbx20-Gata4 transcription factor network led to the discovery and validation of Aldh1a2, an essential developmental gene associated with various important cardiac disorders, as a healthy adult cardiac fibroblast gene co-regulated by cardiogenic transcription factors Gata4 and Tbx20.

conclusionsWe demonstrate with experimental validations the utility of VISIONET for expertise-driven gene discovery that opens new experimental directions that would not otherwise have been identified.

Indexed as

Computer GraphicsGene Regulatory NetworksGenetic Association StudiesSoftwareAdultCells, CulturedFibroblastsGATA4 Transcription FactorGene Expression Regulation, DevelopmentalHeartHumansT-Box Domain ProteinsTranscription FactorsGATA4 protein, humanGATA4 Transcription FactorT-Box Domain ProteinsTBX20 protein, humanTranscription Factors

Identifiers

PMID25929466
PMCPMC4426166

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.