ElTetrado

ElTetrado identifies and classifies tetrads and quadruplexes in nucleic acid 3D structures to analyze secondary-structure topology of canonical and non-canonical quadruplex motifs relevant to telomeres and transcriptional regulatory regions.


Key Features:

  • Identification: Detects tetrads and quadruplexes by analyzing base-pairing patterns in DNA and RNA 3D structures.
  • Classification (ONZ taxonomy): Classifies tetrads and quadruplexes according to the ONZ taxonomy based on secondary-structure topology.
  • Secondary-structure topology analysis: Uses secondary-structure topology to distinguish between different structural configurations of tetrads and quadruplexes.
  • Support for canonical and non-canonical motifs: Accommodates both canonical and non-canonical quadruplex motifs.
  • Notation and visualization outputs: Generates dot-bracket notation and graphical depictions that reflect the unique secondary-structure topology of identified quadruplexes.

Scientific Applications:

  • Telomere studies: Characterizes tetrads and quadruplexes present in telomeric regions.
  • Transcriptional regulation: Analyzes quadruplex motifs in transcriptional regulatory regions to support studies of gene regulation.
  • Non-canonical motif characterization: Enables analysis of non-canonical quadruplexes beyond canonical sequence-focused approaches.
  • Complementary structural analysis: Provides secondary-structure topology insights that complement sequence and 3D structural examinations.

Methodology:

Analyzes base-pairing patterns in DNA/RNA 3D structures to identify tetrads and quadruplexes, classifies motifs using secondary-structure topology according to the ONZ taxonomy, and outputs dot-bracket notation and graphical depictions.

Topics

Details

License:
MIT
Tool Type:
command-line tool
Programming Languages:
Python, R
Added:
1/18/2021
Last Updated:
3/5/2021

Operations

Publications

Zok T, Popenda M, Szachniuk M. ElTetrado: a tool for identification and classification of tetrads and quadruplexes. BMC Bioinformatics. 2020;21(1). doi:10.1186/s12859-020-3385-1. PMID:32005130. PMCID:PMC6995151.