Evidence map›Paper›PMID 29892925›Full record

ArticleGenes & genomics2018

Transcriptomic analysis of the heat stress response for a commercial baker's yeast Saccharomyces cerevisiae.

Duygu Varol, Vilda Purutçuoğlu, Remziye Yılmaz

Abstract read
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Article in Genes & genomics, 2018. 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. Review
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  3. 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

3 authors.

Duygu VarolDepartment of Statistics, Faculty of Art and Science, Middle East Technical University, Ankara, Turkey.
Vilda PurutçuoğluDepartment of Statistics, Faculty of Art and Science, Middle East Technical University, Ankara, Turkey.
Remziye YılmazDepartment of Food Engineering, Faculty of Engineering, Hacettepe University, Ankara, Turkey. remziye@hacettepe.edu.tr.ORCID 0000-0003-2041-1205

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study is to explore the effects of heat stresses on global gene expression profiles and to identify the candidate genes for the heat stress response in commercial baker's yeast (Saccharomyces cerevisiae) by using microarray technology and comparative statistical data analyses. The data from all hybridizations and array normalization were analyzed using the GeneSpringGX 12.1 (Agilent) and the R 2.15.2 program language. In the analysis, all required statistical methods were performed comparatively. For the normalization step, among alternatives, the RMA (Robust Microarray Analysis) results were used. To determine differentially expressed genes under heat stress treatments, the fold-change and the hypothesis testing approaches were executed under various cut-off values via different multiple testing procedures then the up/down regulated probes were functionally categorized via the PAMSAM clustering. The results of the analysis concluded that the transcriptome changes under the heat shock. Moreover, the temperature-shift stress treatments show that the number of differentially up-regulated genes among the heat shock proteins and transcription factors changed significantly. Finally, the change in temperature is one of the important environmental conditions affecting propagation and industrial application of baker's yeast. This study statistically analyzes this affect via one-channel microarray data.

Indexed as

Gene Expression Regulation, FungalHeat-Shock ResponseTranscriptomeComputational BiologyGene Expression ProfilingSaccharomyces cerevisiaeSequence Analysis, RNABioinformaticsHeat shockOne-channel microarraySaccharomyces cerevisiaeStatistical analysisThermal processes

Identifiers

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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.