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SOLQC: Synthetic Oligo Library Quality Control tool
Omer Sabary1, Yoav Orlev2, Roy Shafir1,2
1The Henry and Marilyn Taub Faculty of Computer Science, Technion, Haifa, 3200003, Israel.
Bioinformatics (Oxford, England)
|August 26, 2020
Summary
Synthetic oligo libraries are crucial in synthetic biology. A new tool, SOLQC, offers fast, comprehensive analysis of these libraries, improving quality control and data interpretation for researchers.
Area of Science:
- Synthetic Biology
- Bioinformatics
- Genomics
Background:
- Synthetic oligo libraries are increasingly used in synthetic biology.
- Growing complexity of experiments necessitates advanced analysis tools for quality control.
Purpose of the Study:
- To introduce SOLQC, a novel software tool for analyzing synthetic oligo libraries.
- To provide fast and comprehensive analysis based on user-performed Next-Generation Sequencing (NGS) data.
Main Methods:
- SOLQC analyzes synthetic oligo libraries using NGS data.
- It generates statistical information on variant representation and error rates.
- The tool produces flexible graphical reports.
Main Results:
- SOLQC enables detailed statistical analysis of synthetic oligo libraries.
- It identifies error rates and their dependencies on sequence and library properties.
- Demonstrated utility through analysis of literature-based libraries.
Conclusions:
- SOLQC enhances quality control and assessment of synthetic oligo libraries.
- The tool supports better inference from complex synthetic biology experiments.
- Provides valuable insights into library properties and potential errors.
