Evidence map›Paper›PMID 29653518›Full record

ArticleBMC bioinformatics2018

Genexpi: a toolset for identifying regulons and validating gene regulatory networks using time-course expression data.

Martin Modrák, Jiří Vohradský

Abstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. σCommunications biology · 2024
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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

2 authors.

Martin ModrákInstitute of Microbiology of the Czech Academy of Sciences, Vídeňská, 1083, Prague, Czech Republic. martin.modrak@biomed.cas.cz.ORCID 0000-0002-8886-7797
Jiří VohradskýInstitute of Microbiology of the Czech Academy of Sciences, Vídeňská, 1083, Prague, Czech Republic.

Funding

Ministerstvo Školství, Mládeže a Tělovýchovy LM20150055
6 · The paper itself

Abstract

backgroundIdentifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks.

resultsWe introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package.

conclusionsGenexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.

Indexed as

Databases, GeneticGene Expression RegulationGene Regulatory NetworksSoftwareBacteriaEukaryotaHumansRegulonReproducibility of ResultsSaccharomyces cerevisiaeTime FactorsCytoscapeGene network inferenceTime seriesTranscription regulation

Identifiers

PMID29653518
PMCPMC5899412

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.