Sensitive Chromosomal Translocation Quantitation from Amplicon Sequencing Using Primer-Anchored Statistical Translocation Analysis (PASTA)

Abstract

Chromosomal translocations are rare structural rearrangement outcomes of genome editing, requiring analytical frameworks that combine high quantitative accuracy with performant sensitivity and specificity. Amplicon sequencing offers a scalable means to detect rare rearrangements with ultra-deep targeted sequencing, but existing methods often rely on heuristic thresholds or ad hoc normalization steps that limit reproducibility and have unknown analytical performance. Here, we present a computational tool we call PASTA (Primer-Anchored Statistical Translocation Analysis), using a count-based differential-event statistical framework to quantify and statistically confirm translocation junctions from targeted amplicon sequencing data. Comparison of this method to ddPCR demonstrates that quantitation is highly accurate, and outperforms other NGS-based methods even when randomized adapter chemistry is not present in amplicon sequencing structures. To measure analytical performance, we create a benchmarking dataset for measuring chromosomal translocation analysis performance with frequencies ranging from 1% to sub-0.01%, and demonstrate that the method can detect frequencies down to 0.01% with >75% sensitivity when sufficient read depth is present. Taken together, this work demonstrates using amplicon sequencing with PASTA as a bioinformatics analysis tool is a solution for translocation detection in amplicon sequencing genotoxicity assessments, enabling identification of rare genome rearrangements in both research and preclinical applications