universal TTSs

universal TTSs identifies DNA regions capable of forming triple helices with diverse RNAs to enable systematic discovery of "universal triplex target sites" relevant to lncRNA-mediated chromatin interactions.


Key Features:

  • Identification of Universal TTSs: Identifies DNA sequences that form triple helices with a majority (>90%) of analyzed transcripts, defining "universal TTSs" and reporting their genome-wide rarity (~0.5%) alongside increased frequency in specific regions.
  • Statistical Significance Estimation: Estimates significance of predicted RNA–DNA triplex interactions using a novel statistical method with an adjusted p-value threshold of <0.01.
  • Enrichment Analysis: Detects enrichment of universal TTSs in purine-rich low complexity regions, indicating specific sequence characteristics associated with triplex formation.
  • Genomic Context and Frequency Reporting: Reports differential frequencies of universal TTSs across genomic contexts, including >15% prevalence among MEG3 binding sites and ~40% prevalence in shared Capture-seq peaks.

Scientific Applications:

  • Chromatin Structure Analysis: Supports investigation of the role of RNA–DNA triple helices in three-dimensional chromatin organization and long-distance chromosomal contacts.
  • Enhancer–Promoter Interactions: Enables exploration of potential contributions of universal TTSs to distal enhancer–promoter interactions involved in gene regulation.

Methodology:

Integrates computational analyses with experimental RNA–DNA interaction data to predict triplex-forming DNA regions and applies a novel statistical significance estimation with an adjusted p-value threshold of <0.01.

Topics

Details

License:
GPL-3.0
Maturity:
Mature
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Linux, Windows, Mac
Programming Languages:
R, Python
Added:
8/9/2019
Last Updated:
6/16/2020

Operations

Publications

Antonov I, Medvedeva YA. Purine-rich low complexity regions are potential RNA binding hubs in the human genome. F1000Research. 2019;7:76. doi:10.12688/f1000research.13522.2. PMID:31131080. PMCID:PMC6518440.

PMID: 31131080
PMCID: PMC6518440
Funding: - Russian Science Foundation: 14-15-30002

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