CleanFinder: A Scalable Framework for Comprehensive Genome Editing Analysis

Abstract

Genome editing experiments routinely generate complex mixtures of alleles rather than single, predefined outcomes. Resolving these heterogeneous edits across diverse editing modalities, sequencing platforms, and multiplexed designs remains a persistent analytical challenge. To address this, we developed CleanFinder, a browser-native framework for genotyping genome editing outcomes using a constrained semi-global alignment strategy. Context-aware alignment modes support a broad spectrum of editing scenarios, including indels, base substitutions, and complex prime editing modifications, across nuclear and mitochondrial targets. Additional modules include an optional turbo mode for high-throughput heuristic alignment in exploratory workflows, and an allele-aware module that leverages heterozygous SNPs to detect allelic dropout. To evaluate scalability and practical performance, we applied CleanFinder to a primary small-molecule screen of 1,849 compounds in HEK293T cells. The software efficiently processed the dataset, enabling high-throughput comparison of editing outcomes and nomination of candidate compounds for follow-up analysis. Together, CleanFinder provides a flexible and scalable platform for genome editing analysis, enabling detailed genotyping and systematic comparison of editing outcomes across diverse edit types and genomic contexts.