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Nuclear RNA Sequencing of the Mouse Erythroid Cell Transcriptome

Authors
Journal
PLoS ONE
1932-6203
Publisher
Public Library of Science
Publication Date
Volume
7
Issue
11
Identifiers
DOI: 10.1371/journal.pone.0049274
Keywords
  • Research Article
  • Biology
  • Biochemistry
  • Nucleic Acids
  • Rna
  • Genetics
  • Gene Expression
  • Dna Transcription
  • Chromatin
  • Genomics
  • Genome Analysis Tools
  • Transcriptomes
  • Functional Genomics
  • Genome Expression Analysis
  • Genome Sequencing
  • Molecular Cell Biology
  • Cellular Types
  • Blood Cells
Disciplines
  • Biology

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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