five

Sex-specific and lineage-specific alternative splicing in primates

收藏
NIAID Data Ecosystem2026-03-11 收录
下载链接:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17274
下载链接
链接失效反馈
官方服务:
资源简介:
Comparative studies of gene regulation suggest an important role for natural selection in shaping gene expression patterns within and between species. Most of these studies, however, estimated gene expression levels using microarray probes designed to hybridize to only a small proportion of each gene. Here we used recently-developed RNA sequencing protocols, which side-step this limitation, to assess intra- and inter-species variation in gene regulatory processes in considerably more detail than was previously possible. Specifically, we used RNAseq to study transcript levels in humans, chimpanzees, and rhesus macaques, using liver RNA samples from three males and three females from each species. Our approach allowed us to identify a large number of genes whose expression levels likely evolve under natural selection in primates. These include a subset of genes with conserved sexually dimorphic expression patterns across the three species, which we found to be enriched for genes involved in lipid metabolism. Our data also suggest that while alternative splicing is tightly regulated within and between species, sex-specific and lineage-specific changes in the expression of different splice forms are also frequent. Intriguingly, among genes in which a change in exon usage occurred exclusively in the human lineage, we found an enrichment of genes involved in anatomical structure and morphogenesis, raising the possibility that differences in the regulation of alternative splicing have been an important force in human evolution. Keywords: Gene Regulation Study Examination of gene expression levels in livers from three primate species (human, chimpanzee, and rhesus macaque), using 3 male and 3 female samples from each species.
创建时间:
2019-05-15
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作