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SOLQC: Synthetic Oligo Library Quality Control tool.

Omer Sabary¹, Yoav Orlev², Roy Shafir^1,2

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.

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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:

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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.