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Intergenic and Repeat Transcription in Human, Chimpanzee and Macaque Brains Measured by RNA-Seq
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Date
2010-07-01
Author
Xu, Augix Guohua
He, Liu
Li, Zhongshan
Xu, Ying
Li, Mingfeng
Fu, Xing
Yan, Zheng
Yuan, Yuan
Menzel, Corinna
Li, Na
Somel, Mehmet
Hu, Hao
Chen, Wei
Paabo, Svante
Khaitovich, Philipp
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Transcription is the first step connecting genetic information with an organism's phenotype. While expression of annotated genes in the human brain has been characterized extensively, our knowledge about the scope and the conservation of transcripts located outside of the known genes' boundaries is limited. Here, we use high-throughput transcriptome sequencing (RNA-Seq) to characterize the total non-ribosomal transcriptome of human, chimpanzee, and rhesus macaque brain. In all species, only 20-28% of non-ribosomal transcripts correspond to annotated exons and 20-23% to introns. By contrast, transcripts originating within intronic and intergenic repetitive sequences constitute 40-48% of the total brain transcriptome. Notably, some repeat families show elevated transcription. In non-repetitive intergenic regions, we identify and characterize 1,093 distinct regions highly expressed in the human brain. These regions are conserved at the RNA expression level across primates studied and at the DNA sequence level across mammals. A large proportion of these transcripts (20%) represents 3'UTR extensions of known genes and may play roles in alternative microRNA-directed regulation. Finally, we show that while transcriptome divergence between species increases with evolutionary time, intergenic transcripts show more expression differences among species and exons show less. Our results show that many yet uncharacterized evolutionary conserved transcripts exist in the human brain. Some of these transcripts may play roles in transcriptional regulation and contribute to evolution of human-specific phenotypic traits.
Subject Keywords
Ecology
,
Modelling and Simulation
,
Computational Theory and Mathematics
,
Genetics
,
Ecology, Evolution, Behavior and Systematics
,
Molecular Biology
,
Cellular and Molecular Neuroscience
URI
https://hdl.handle.net/11511/48233
Journal
PLOS COMPUTATIONAL BIOLOGY
DOI
https://doi.org/10.1371/journal.pcbi.1000843
Collections
Department of Biology, Article