APTANI

APTANI analyzes HT-SELEX datasets to identify target-specific nucleic acid aptamers by integrating sequence and secondary-structure information to detect binding motifs and characterize aptamer families.

Key Features:

• Extension of AptaMotif: Builds upon the AptaMotif algorithm to adapt motif discovery approaches for high-throughput SELEX data.
• HT-SELEX data integration: Processes large-scale HT-SELEX sequence libraries to enable analysis of enriched sequences across selection experiments.
• Secondary structure analysis: Incorporates secondary-structure information to relate sequence motifs to predicted structural elements relevant for target binding.
• Binding motif identification: Identifies sequence and structure-associated motifs that are putatively responsible for target specificity and affinity.
• Aptamer family clustering: Clusters similar aptamer sequences to define families that share common motifs or structural features.
• Comparative analysis across cycles: Compares sequence and motif enrichment across HT-SELEX cycles to assess selection dynamics.

Scientific Applications:

• Aptamer discovery: Identification of novel target-specific aptamers from HT-SELEX datasets for downstream experimental validation.
• Therapeutic and diagnostic development: Support for selecting aptamers with potential application in therapeutic or diagnostic assays.
• Rational aptamer design: Informing design by linking sequence motifs and secondary-structure features to target binding.
• Selection dynamics analysis: Characterizing sequence enrichment and evolutionary trajectories across SELEX cycles.

Methodology:

Extends the AptaMotif algorithm to process HT-SELEX datasets and performs secondary-structure incorporation, binding motif identification, aptamer clustering, and comparative analysis across selection cycles.