RNAscClust

RNAscClust clusters paralogous RNA sequences by integrating structural conservation and compensatory base pair changes into minimum free-energy structure prediction and graph kernel–based analysis.

Key Features:

• Structure-Constrained MFE Prediction: Computes minimum free-energy secondary structures for each sequence using conserved and compensatory base pairs from orthologous structural alignments as prior constraints.
• Graph Kernel-Based Clustering: Applies a graph kernel strategy to cluster paralogous RNAs based on predicted structural features and shared motifs.

Scientific Applications:

• RNA Family and Evolutionary Analysis: Enables identification of structurally conserved and functionally related RNA paralogs to support studies of RNA structure–function relationships and evolutionary dynamics.

Methodology:

RNAscClust takes multiple structural alignments containing orthologous and paralogous sequences, infers conserved and compensatory base pairs, predicts constrained minimum free-energy structures, and clusters sequences using graph kernel similarity measures derived from structural features.