APTANI: a computational tool to select aptamers through sequence-structure motif analysis of HT-SELEX data

Area of Science:

• Biotechnology
• Bioinformatics
• Computational Biology

Background:

• Aptamers are synthetic nucleic acid molecules selected via Systematic Evolution of Ligands by EXponential enrichment (SELEX).
• High-throughput sequencing (HT-SELEX) generates massive datasets of potential aptamer sequences.
• Computational analysis is crucial for sifting through HT-SELEX data to find relevant aptamers.

Purpose of the Study:

• To introduce APTANI, a computational tool for identifying target-specific aptamers from HT-SELEX data.
• To extend the capabilities of the AptaMotif algorithm for HT-SELEX analysis.
• To provide functionalities for aptamer motif identification, clustering, and cross-cycle comparison.

Main Methods:

• APTANI utilizes the AptaMotif algorithm, adapted for HT-SELEX data.
• The tool incorporates secondary structure information for aptamer analysis.
• It offers features for identifying binding motifs, clustering aptamer families, and comparing results across SELEX cycles.

Main Results:

• APTANI successfully identifies target-specific aptamers from HT-SELEX data.
• New functionalities include aptamer motif discovery and family clustering.
• Tabular and graphical outputs enhance the biological interpretation of results.

Conclusions:

• APTANI is a valuable computational tool for aptamer discovery from HT-SELEX data.
• The tool streamlines the analysis of large aptamer datasets.
• It facilitates the selection of biologically relevant aptamers for various applications.