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Transcriptome-wide identification of optimal reference genes for expression analysis of Pyropia yezoensis responses to abiotic stress

Overview of attention for article published in BMC Genomics, April 2018
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Title
Transcriptome-wide identification of optimal reference genes for expression analysis of Pyropia yezoensis responses to abiotic stress
Published in
BMC Genomics, April 2018
DOI 10.1186/s12864-018-4643-8
Pubmed ID
Authors

Dong Gao, Fanna Kong, Peipei Sun, Guiqi Bi, Yunxiang Mao

Abstract

Pyropia yezoensis, a marine red alga, is an ideal research model for studying the mechanisms of abiotic stress tolerance in intertidal seaweed. Real-time quantitative polymerase chain reaction (RT-qPCR) is the most commonly used method to analyze gene expression levels. To accurately quantify gene expression, selection and validation of stable reference genes is required. We used transcriptome profiling data from different abiotic stress treatments to identify six genes with relatively stable expression levels: MAP, ATPase, CGS1, PPK, DPE2, and FHP. These six genes and three conventional reference genes, UBC, EF1-α, and eif4A, were chosen as candidates for optimal reference gene selection. Five common statistical approaches (geNorm, ΔCt method, NormFinder, BestKeeper, and ReFinder) were used to identify the stability of each reference gene. Our results show that: MAP, UBC, and FHP are stably expressed in all analyzed conditions; CGS1 and UBC are stably expressed under conditions of dehydration stress; and MAP, UBC, and CGS1 are stably expressed under conditions of temperature stress. We have identified appropriate reference genes for RT-qPCR in P. yezoensis under different abiotic stress conditions which will facilitate studies of gene expression under these conditions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Researcher 5 16%
Student > Master 3 10%
Student > Bachelor 2 6%
Student > Postgraduate 2 6%
Other 3 10%
Unknown 9 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 32%
Agricultural and Biological Sciences 6 19%
Psychology 2 6%
Nursing and Health Professions 1 3%
Earth and Planetary Sciences 1 3%
Other 1 3%
Unknown 10 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 April 2018.
All research outputs
#20,481,952
of 23,043,346 outputs
Outputs from BMC Genomics
#9,325
of 10,697 outputs
Outputs of similar age
#289,255
of 327,997 outputs
Outputs of similar age from BMC Genomics
#196
of 222 outputs
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