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Nuclear RNA Sequencing of the Mouse Erythroid Cell Transcriptome

Overview of attention for article published in PLOS ONE, November 2012
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Title
Nuclear RNA Sequencing of the Mouse Erythroid Cell Transcriptome
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0049274
Pubmed ID
Authors

Jennifer A. Mitchell, Ieuan Clay, David Umlauf, Chih-yu Chen, Catherine A. Moir, Christopher H. Eskiw, Stefan Schoenfelder, Lyubomira Chakalova, Takashi Nagano, Peter Fraser

Abstract

In addition to protein coding genes a substantial proportion of mammalian genomes are transcribed. However, most transcriptome studies investigate steady-state mRNA levels, ignoring a considerable fraction of the transcribed genome. In addition, steady-state mRNA levels are influenced by both transcriptional and posttranscriptional mechanisms, and thus do not provide a clear picture of transcriptional output. Here, using deep sequencing of nuclear RNAs (nucRNA-Seq) in parallel with chromatin immunoprecipitation sequencing (ChIP-Seq) of active RNA polymerase II, we compared the nuclear transcriptome of mouse anemic spleen erythroid cells with polymerase occupancy on a genome-wide scale. We demonstrate that unspliced transcripts quantified by nucRNA-seq correlate with primary transcript frequencies measured by RNA FISH, but differ from steady-state mRNA levels measured by poly(A)-enriched RNA-seq. Highly expressed protein coding genes showed good correlation between RNAPII occupancy and transcriptional output; however, genome-wide we observed a poor correlation between transcriptional output and RNAPII association. This poor correlation is due to intergenic regions associated with RNAPII which correspond with transcription factor bound regulatory regions and a group of stable, nuclear-retained long non-coding transcripts. In conclusion, sequencing the nuclear transcriptome provides an opportunity to investigate the transcriptional landscape in a given cell type through quantification of unspliced primary transcripts and the identification of nuclear-retained long non-coding RNAs.

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Geographical breakdown

Country Count As %
United Kingdom 3 4%
Italy 1 1%
India 1 1%
Denmark 1 1%
Japan 1 1%
United States 1 1%
Unknown 77 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 32%
Student > Ph. D. Student 23 27%
Student > Doctoral Student 7 8%
Professor 5 6%
Student > Master 4 5%
Other 13 15%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 51%
Biochemistry, Genetics and Molecular Biology 24 28%
Computer Science 3 4%
Environmental Science 2 2%
Neuroscience 2 2%
Other 5 6%
Unknown 6 7%