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A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data

Overview of attention for article published in PeerJ, December 2017
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Title
A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data
Published in
PeerJ, December 2017
DOI 10.7717/peerj.4133
Pubmed ID
Authors

Shirley Bikel, Leonor Jacobo-Albavera, Fausto Sánchez-Muñoz, Fernanda Cornejo-Granados, Samuel Canizales-Quinteros, Xavier Soberón, Rogerio R. Sotelo-Mundo, Blanca E. del Río-Navarro, Alfredo Mendoza-Vargas, Filiberto Sánchez, Adrian Ochoa-Leyva

Abstract

In spite of the emergence of RNA sequencing (RNA-seq), microarrays remain in widespread use for gene expression analysis in the clinic. There are over 767,000 RNA microarrays from human samples in public repositories, which are an invaluable resource for biomedical research and personalized medicine. The absolute gene expression analysis allows the transcriptome profiling of all expressed genes under a specific biological condition without the need of a reference sample. However, the background fluorescence represents a challenge to determine the absolute gene expression in microarrays. Given that the Y chromosome is absent in female subjects, we used it as a new approach for absolute gene expression analysis in which the fluorescence of the Y chromosome genes of female subjects was used as the background fluorescence for all the probes in the microarray. This fluorescence was used to establish an absolute gene expression threshold, allowing the differentiation between expressed and non-expressed genes in microarrays. We extracted the RNA from 16 children leukocyte samples (nine males and seven females, ages 6-10 years). An Affymetrix Gene Chip Human Gene 1.0 ST Array was carried out for each sample and the fluorescence of 124 genes of the Y chromosome was used to calculate the absolute gene expression threshold. After that, several expressed and non-expressed genes according to our absolute gene expression threshold were compared against the expression obtained using real-time quantitative polymerase chain reaction (RT-qPCR). From the 124 genes of the Y chromosome, three genes (DDX3Y, TXLNG2P and EIF1AY) that displayed significant differences between sexes were used to calculate the absolute gene expression threshold. Using this threshold, we selected 13 expressed and non-expressed genes and confirmed their expression level by RT-qPCR. Then, we selected the top 5% most expressed genes and found that several KEGG pathways were significantly enriched. Interestingly, these pathways were related to the typical functions of leukocytes cells, such as antigen processing and presentation and natural killer cell mediated cytotoxicity. We also applied this method to obtain the absolute gene expression threshold in already published microarray data of liver cells, where the top 5% expressed genes showed an enrichment of typical KEGG pathways for liver cells. Our results suggest that the three selected genes of the Y chromosome can be used to calculate an absolute gene expression threshold, allowing a transcriptome profiling of microarray data without the need of an additional reference experiment. Our approach based on the establishment of a threshold for absolute gene expression analysis will allow a new way to analyze thousands of microarrays from public databases. This allows the study of different human diseases without the need of having additional samples for relative expression experiments.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 28%
Student > Bachelor 2 11%
Student > Ph. D. Student 2 11%
Student > Postgraduate 2 11%
Student > Master 2 11%
Other 3 17%
Unknown 2 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 50%
Agricultural and Biological Sciences 2 11%
Immunology and Microbiology 2 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Medicine and Dentistry 1 6%
Other 0 0%
Unknown 3 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 December 2017.
All research outputs
#6,060,270
of 23,011,300 outputs
Outputs from PeerJ
#4,985
of 13,413 outputs
Outputs of similar age
#119,311
of 439,767 outputs
Outputs of similar age from PeerJ
#172
of 338 outputs
Altmetric has tracked 23,011,300 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 13,413 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.3. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 439,767 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 338 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.