FASTAptamer: A Bioinformatic Toolkit for High-throughput Sequence Analysis of Combinatorial Selections

Area of Science:

• Bioinformatics
• Molecular Biology
• Computational Biology

Background:

• High-throughput sequencing (HTS) analysis is crucial for understanding combinatorial selection processes.
• Existing tools for genomic data analysis are not tailored for combinatorial selection, necessitating custom software development.
• FASTAptamer addresses the need for user-friendly sequence-level analysis in this field.

Purpose of the Study:

• To develop and introduce FASTAptamer, an open-source software toolkit for analyzing HTS data from combinatorial selections.
• To provide a user-friendly solution for sequence-level analysis in aptamer and other selection-based discovery processes.
• To facilitate the tracking of evolutionary trajectories and identify high-fitness sequences.

Main Methods:

• FASTAptamer processes FASTQ files to count, normalize, and rank sequence reads.
• The toolkit compares sequence population distributions and generates sequence family clusters.
• It calculates fold-enrichment and identifies degenerate sequence motifs.

Main Results:

• FASTAptamer offers comprehensive sequence-level analysis for combinatorial selections.
• The software supports various selection strategies beyond aptamers, including ribozyme, deoxyribozyme, in vivo mutagenesis, and surface display technologies.
• It provides dynamic insights into selection pressures and evolutionary trajectories.

Conclusions:

• FASTAptamer simplifies and standardizes HTS data analysis for combinatorial selections.
• The toolkit accelerates lead discovery and optimization by providing efficient sequence analysis.
• FASTAptamer is a versatile tool applicable to a wide range of next-generation sequencing-based selection strategies.