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Computational analysis of mRNA expression profiling in the inner ear reveals candidate transcription factors associated with proliferation, differentiation, and deafness

Overview of attention for article published in Human Genomics, June 2018
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Title
Computational analysis of mRNA expression profiling in the inner ear reveals candidate transcription factors associated with proliferation, differentiation, and deafness
Published in
Human Genomics, June 2018
DOI 10.1186/s40246-018-0161-7
Pubmed ID
Authors

Kobi Perl, Ron Shamir, Karen B. Avraham

Abstract

Hearing loss is a major cause of disability worldwide, impairing communication, health, and quality of life. Emerging methods of gene therapy aim to address this morbidity, which can be employed to fix a genetic problem causing hair cell dysfunction and to promote the proliferation of supporting cells in the cochlea and their transdifferentiation into hair cells. In order to extend the applicability of gene therapy, the scientific community is focusing on discovery of additional deafness genes, identifying new genetic variants associated with hearing loss, and revealing new factors that can be manipulated in a coordinated manner to improve hair cell regeneration. Here, we addressed these challenges via genome-wide measurement and computational analysis of transcriptional profiles of mouse cochlea and vestibule sensory epithelium at embryonic day (E)16.5 and postnatal day (P)0. These time points correspond to developmental stages before and during the acquisition of mechanosensitivity, a major turning point in the ability to hear. We hypothesized that tissue-specific transcription factors are primarily involved in differentiation, while those associated with development are more concerned with proliferation. Therefore, we searched for enrichment of transcription factor binding motifs in genes differentially expressed between the tissues and between developmental ages of mouse sensory epithelium. By comparison with transcription factors known to alter their expression during avian hair cell regeneration, we identified 37 candidates likely to be important for regeneration. Furthermore, according to our estimates, only half of the deafness genes in human have been discovered. To help remedy the situation, we developed a machine learning classifier that utilizes the expression patterns of genes to predict how likely they are to be undiscovered deafness genes. We used a novel approach to highlight novel additional factors that can serve as points of intervention for enhancing hair cell regeneration. Given the similarities between mouse and human deafness, our predictions may be of value in prioritizing future research on novel human deafness genes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 18%
Other 5 13%
Student > Doctoral Student 4 10%
Student > Master 4 10%
Student > Ph. D. Student 3 8%
Other 4 10%
Unknown 12 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 21%
Medicine and Dentistry 7 18%
Neuroscience 3 8%
Agricultural and Biological Sciences 2 5%
Nursing and Health Professions 1 3%
Other 5 13%
Unknown 13 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 June 2018.
All research outputs
#16,728,456
of 25,385,509 outputs
Outputs from Human Genomics
#370
of 564 outputs
Outputs of similar age
#210,128
of 342,290 outputs
Outputs of similar age from Human Genomics
#7
of 11 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 564 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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 342,290 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.