Journal article
Galaxy Workflows for Web-based Bioinformatics Analysis of Aptamer High-throughput Sequencing Data
Molecular therapy. Nucleic acids, Vol.5(8), pp.e345-e345
2016
DOI: 10.1038/mtna.2016.54
PMCID: PMC5023399
PMID: 28131286
Abstract
Development of RNA and DNA aptamers for diagnostic and therapeutic applications is a rapidly growing field. Aptamers are identified through iterative rounds of selection in a process termed SELEX (Systematic Evolution of Ligands by EXponential enrichment). High-throughput sequencing (HTS) revolutionized the modern SELEX process by identifying millions of aptamer sequences across multiple rounds of aptamer selection. However, these vast aptamer HTS datasets necessitated bioinformatics techniques. Herein, we describe a semiautomated approach to analyze aptamer HTS datasets using the Galaxy Project, a web-based open source collection of bioinformatics tools that were originally developed to analyze genome, exome, and transcriptome HTS data. Using a series of Workflows created in the Galaxy webserver, we demonstrate efficient processing of aptamer HTS data and compilation of a database of unique aptamer sequences. Additional Workflows were created to characterize the abundance and persistence of aptamer sequences within a selection and to filter sequences based on these parameters. A key advantage of this approach is that the online nature of the Galaxy webserver and its graphical interface allow for the analysis of HTS data without the need to compile code or install multiple programs.
Details
- Title: Subtitle
- Galaxy Workflows for Web-based Bioinformatics Analysis of Aptamer High-throughput Sequencing Data
- Creators
- William H Thiel - Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA
- Resource Type
- Journal article
- Publication Details
- Molecular therapy. Nucleic acids, Vol.5(8), pp.e345-e345
- Publisher
- Elsevier Inc
- DOI
- 10.1038/mtna.2016.54
- PMID
- 28131286
- PMCID
- PMC5023399
- ISSN
- 2162-2531
- eISSN
- 2162-2531
- Language
- English
- Date published
- 2016
- Academic Unit
- Cardiovascular Medicine; Internal Medicine
- Record Identifier
- 9984094383602771
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