CleanFinder: a scalable framework for comprehensive genome editing analysis

Area of Science:

• Genetics
• Bioinformatics
• Molecular Biology

Background:

• Genome editing frequently produces heterogeneous allelic mixtures, posing analytical challenges for diverse editing techniques and sequencing platforms.
• Accurate genotyping of these complex edits is crucial for understanding editing outcomes and for applications like high-throughput screening.

Purpose of the Study:

• To develop a flexible and scalable browser-native framework, CleanFinder, for comprehensive genotyping of genome editing outcomes.
• To address the analytical challenges in resolving heterogeneous edits across various editing modalities and genomic contexts.

Main Methods:

• Developed CleanFinder, a browser-native framework utilizing a constrained semi-global alignment strategy for genotyping.
• Implemented context-aware alignment modes supporting indels, base substitutions, and prime editing modifications in nuclear and mitochondrial DNA.
• Integrated optional turbo mode for high-throughput analysis and an allele-aware module for detecting allelic dropout using heterozygous SNPs.

Main Results:

• CleanFinder efficiently processed a large dataset from a small-molecule screen (1849 compounds in HEK293T cells).
• The framework enabled high-throughput comparison of genome editing outcomes and identification of candidate compounds.
• Demonstrated scalability and practical performance for analyzing diverse editing scenarios.

Conclusions:

• CleanFinder provides a robust and scalable platform for detailed genome editing analysis.
• The software facilitates systematic comparison of editing outcomes across various edit types and genomic contexts.
• Enables efficient analysis for applications such as high-throughput screening and discovery.