csRNA-seq

csRNA-seq identifies transcription start sites (TSSs) of stable and unstable RNAs at single-nucleotide resolution from total RNA to map transcription initiation and regulatory elements across eukaryotes.


Key Features:

  • Single-Nucleotide Resolution: Detects TSSs with single-nucleotide precision.
  • Sensitivity to Transcriptional Changes: Highly sensitive to acute changes in transcription, identifying more regulated transcripts than traditional RNA sequencing methods.
  • Detection of Unstable Transcripts: Captures unstable transcripts including enhancer RNAs, primary microRNAs (pri-miRNAs), antisense transcripts, and promoter upstream transcripts.
  • Broad Eukaryotic Applicability: Applicable across eukaryotes, including multicellular animals, plants, and fungi.
  • Integration with Epigenomic Data: Facilitates integration with epigenomic data and reveals histone modification patterns such as histone H3 trimethylation (H3K4me3) enrichment at TSSs of stable transcripts and H3K27ac marking nucleosomes downstream of active TSSs.
  • Capture of 5' Cap-Protected RNAs: Sequences the 5' ends of cap-protected RNAs from total RNA to capture both stable and unstable transcripts for TSS mapping.
  • Dynamic Quantification: Enables identification and dynamic quantification of regulatory elements from total RNA.

Scientific Applications:

  • Regulatory Element Identification: Enables identification and dynamic quantification of regulatory elements within total RNA to study transcriptional regulation.
  • Evolutionary Insights: Provides comparative insights into conserved mechanisms of transcription initiation spanning 1.6 billion years of eukaryotic evolutionary history.
  • Histone Modification Roles: Aids elucidation of the roles of posttranslational histone modifications in transcription regulation, indicating ancient and fundamental functions.

Methodology:

Sequences the 5' ends of cap-protected RNAs from total RNA to capture stable and unstable transcripts and identify TSSs at single-nucleotide resolution.

Topics

Details

Tool Type:
command-line tool
Programming Languages:
Perl
Added:
1/9/2020
Last Updated:
12/17/2020

Operations

Publications

Duttke SH, Chang MW, Heinz S, Benner C. Identification and dynamic quantification of regulatory elements using total RNA. Genome Research. 2019;29(11):1836-1846. doi:10.1101/gr.253492.119. PMID:31649059. PMCID:PMC6836739.

PMID: 31649059
PMCID: PMC6836739
Funding: - National Institutes of Health: U19AI106754, U19AI135972