BATCH-GE: Batch analysis of Next-Generation Sequencing data for genome editing assessment

Area of Science:

• Genetics and Genomics
• Bioinformatics
• Molecular Biology

Background:

• CRISPR/Cas9 technology is transforming genetic research, enabling precise genome engineering.
• Next-Generation Sequencing (NGS) is crucial for analyzing CRISPR/Cas9 editing outcomes.
• Existing analysis tools struggle with the volume and flexibility required for large-scale NGS data.

Purpose of the Study:

• To develop a flexible and user-friendly bioinformatics tool for batch analysis of NGS data from genome editing experiments.
• To address the limitations of current tools in handling large datasets and providing adaptable analysis parameters.

Main Methods:

• Development of BATCH-GE, a novel bioinformatics software for processing NGS data.
• Implementation of algorithms to detect indel mutations and other precise genome editing events.
• Validation of BATCH-GE performance using zebrafish knock-out and knock-in mutant generation experiments.

Main Results:

• BATCH-GE enables parallel batch analysis of NGS data for genome editing assessment.
• The tool accurately detects indel mutations and calculates mutagenesis efficiencies.
• BATCH-GE demonstrated flexibility by allowing user-defined input variable adjustments.

Conclusions:

• BATCH-GE provides an efficient and flexible solution for analyzing CRISPR/Cas9 genome editing data.
• This tool supports genome editing evaluations across diverse experimental setups and model organisms.
• BATCH-GE facilitates the analysis of genome editing data from any organism with a sequenced genome.