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

Overview
Publisher Cell Press
Date 2015 Mar 4
PMID 25734917
Citations 121
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Abstract

High-throughput sequence (HTS) analysis of combinatorial selection populations accelerates lead discovery and optimization and offers dynamic insight into selection processes. An underlying principle is that selection enriches high-fitness sequences as a fraction of the population, whereas low-fitness sequences are depleted. HTS analysis readily provides the requisite numerical information by tracking the evolutionary trajectory of individual sequences in response to selection pressures. Unlike genomic data, for which a number of software solutions exist, user-friendly tools are not readily available for the combinatorial selections field, leading many users to create custom software. FASTAptamer was designed to address the sequence-level analysis needs of the field. The open source FASTAptamer toolkit counts, normalizes and ranks read counts in a FASTQ file, compares populations for sequence distribution, generates clusters of sequence families, calculates fold-enrichment of sequences throughout the course of a selection and searches for degenerate sequence motifs. While originally designed for aptamer selections, FASTAptamer can be applied to any selection strategy that can utilize next-generation DNA sequencing, such as ribozyme or deoxyribozyme selections, in vivo mutagenesis and various surface display technologies (peptide, antibody fragment, mRNA, etc.). FASTAptamer software, sample data and a user's guide are available for download at http://burkelab.missouri.edu/fastaptamer.html.

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References
1.
Szeto K, Latulippe D, Ozer A, Pagano J, White B, Shalloway D . RAPID-SELEX for RNA aptamers. PLoS One. 2013; 8(12):e82667. PMC: 3869713. DOI: 10.1371/journal.pone.0082667. View

2.
Thiel W, Bair T, Wyatt Thiel K, Dassie J, Rockey W, Howell C . Nucleotide bias observed with a short SELEX RNA aptamer library. Nucleic Acid Ther. 2011; 21(4):253-63. PMC: 3198618. DOI: 10.1089/nat.2011.0288. View

3.
Hoon S, Zhou B, Janda K, Brenner S, Scolnick J . Aptamer selection by high-throughput sequencing and informatic analysis. Biotechniques. 2011; 51(6):413-6. DOI: 10.2144/000113786. View

4.
Wang A, Farokhzad O . Current progress of aptamer-based molecular imaging. J Nucl Med. 2014; 55(3):353-6. PMC: 4110511. DOI: 10.2967/jnumed.113.126144. View

5.
Pitt J, Ferre-DAmare A . Rapid construction of empirical RNA fitness landscapes. Science. 2010; 330(6002):376-9. PMC: 3392653. DOI: 10.1126/science.1192001. View