Browse

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

Cited 16 time in Web of Science Cited 16 time in Scopus
Authors
Seo, Minseok; Caetano-Anolles, Kelsey; Rodriguez-Zas, Sandra; Ka, Sojeong; Jeong, Jin Young; Park, Sungkwon; Kim, Min Ji; Nho, Whan-Gook; Cho, Seoae; Kim, Heebal; Lee, Hyun-Jeong
Issue Date
2016-01-27
Publisher
BioMed Central
Citation
BMC Genomics, 17(1):81
Keywords
RNA-seqSexual dimorphismTissue-specific gene expressionANODEV
Description
This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.
Abstract
Abstract

Background
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.


Results
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.


Conclusions
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.
Language
English
URI
http://hdl.handle.net/10371/100483
DOI
https://doi.org/10.1186/s12864-016-2400-4
Files in This Item:
Appears in Collections:
College of Natural Sciences (자연과학대학)Program in Bioinformatics (협동과정-생물정보학전공)Journal Papers (저널논문_협동과정-생물정보학전공)
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse