A quantitative analysis of secondary RNA structure using domination based parameters on trees.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics
• Proteomics

Background:

• A comprehensive RNA motif database is crucial for advancing genomic and proteomic research.
• Secondary RNA structures are often modeled as graph-theoretic trees.
• Graph theory offers tools for numerical identification of RNA motifs using graphical invariants.

Purpose of the Study:

• To investigate the utility of graph-theoretic parameters, specifically variations of the domination number, in identifying RNA-like secondary structures.
• To define and calculate new parameters based on graph invariants for small order trees.
• To statistically analyze these parameters for trees of orders seven and eight to assess their RNA-likeness.

Main Methods:

• Utilizing graph theory and graphical invariants, focusing on variations of the domination number.
• Partitioning graphical invariants into two classes and defining two parameters for each class.
• Calculating these parameters for small order trees.
• Conducting statistical analysis on the calculated parameters for trees of orders seven and eight.

Main Results:

• Domination-based parameters effectively distinguish between trees representing native RNA structures and non-candidates.
• The study refines the identification of potential RNA structures, classifying some previously identified candidates as highly RNA-like and others as less likely.
• This approach aids in narrowing down the search space for RNA motifs.

Conclusions:

• Graph-theoretic quantifiers offer a promising mathematical approach to characterize RNA motifs.
• This method complements existing search algorithms by reducing the computational difficulty in identifying similar motifs.
• The findings suggest graph theory tools can enhance genomics and proteomics research.