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Comprehensive identification of sexually dimorphic genes in diverse cattle tissues using RNA-seq

Overview of attention for article published in BMC Genomics, January 2016
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Title
Comprehensive identification of sexually dimorphic genes in diverse cattle tissues using RNA-seq
Published in
BMC Genomics, January 2016
DOI 10.1186/s12864-016-2400-4
Pubmed ID
Authors

Minseok Seo, Kelsey Caetano-Anolles, Sandra Rodriguez-Zas, Sojeong Ka, Jin Young Jeong, Sungkwon Park, Min Ji Kim, Whan-Gook Nho, Seoae Cho, Heebal Kim, Hyun-Jeong Lee

Abstract

Molecular mechanisms associated with sexual dimorphism in cattle have not been well elucidated. Furthermore, as recent studies have implied that gene expression patterns are highly tissue specific, it is essential to investigate gene expression in a variety of tissues using RNA-seq. Here, we employed and compared two statistical methods, a simple two group test and Analysis of deviance (ANODEV), in order to investigate bovine sexually dimorphic genes in 40 RNA-seq samples distributed across two factors: sex and tissue. As a result, we detected 752 sexually dimorphic genes across tissues from two statistical approaches and identified strong tissue-specific patterns of gene expression. Additionally, significantly detected sex-related genes shared between two mammal species (cattle and rat) were identified using qRT-PCR. Results of our analyses reveal that sexual dimorphism of metabolic tissues and pituitary gland in cattle involves various biological processes. Several differentially expressed genes between sexes in cattle and rat species are shared, but show tissue-specific patterns. Finally, we concluded that two distinct statistical approaches have their advantages and disadvantages in RNA-seq studies investigating multiple tissues.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 22%
Student > Bachelor 4 15%
Student > Ph. D. Student 4 15%
Professor 3 11%
Student > Postgraduate 2 7%
Other 3 11%
Unknown 5 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 44%
Biochemistry, Genetics and Molecular Biology 4 15%
Medicine and Dentistry 2 7%
Veterinary Science and Veterinary Medicine 1 4%
Environmental Science 1 4%
Other 0 0%
Unknown 7 26%
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 29 January 2016.
All research outputs
#18,437,241
of 22,842,950 outputs
Outputs from BMC Genomics
#8,183
of 10,655 outputs
Outputs of similar age
#287,100
of 396,850 outputs
Outputs of similar age from BMC Genomics
#251
of 275 outputs
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