Correction of the cytosine deamination artifacts in FFPE-based sequencing experiments
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Formalin-fixed, paraffin-embedded (FFPE) tissues remain an essential resource for molecular studies, yet formalin-induced cytosine deamination introduces characteristic C>T/G>A artifacts that compromise the accuracy of next-generation sequencing (NGS) analyses. Numerous computational methods and enzymatic DNA repair strategies have been proposed to reduce these artifacts, but no systematic comparison across tools and experimental conditions exists. Here, we evaluate the performance of seven computational approaches (SOBDetector, Ideafix, MicroSEC, FFPolish, DeepOmics FFPE/FFPE-PLUS, FFPErase) together with the NEBNext® FFPE DNA Repair Mix v2, a multi-enzyme repair system applied during DNA preparation. Using three independent datasets, one based on whole genome sequencing (CGCI-BL) and two on whole exome sequencing (TCGA-PC and SUT-LUAD, the latter containing enzymatically repaired samples), and matched fresh-frozen samples as the gold standard, we assess precision, sensitivity, and artifact reduction efficiency across all methods. We further examine the potential synergy between enzymatic repair and post-sequencing computational filtering. Our results provide practical guidelines for FFPE artifact correction and demonstrate that enzymatic treatment provides the best results, while among the computational methods, FFPErase offers the most robust reduction of cytosine deamination artifacts while maximizing the retention of true somatic variants.
KEY MESSAGES
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Formalin fixation in FFPE samples introduces artifacts that can significantly affect the accuracy of NGS analyses.
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Among the evaluated approaches, enzymatic repair using NEBNext® FFPE DNA Repair Mix v2 achieves the most effective reduction of sequencing artifacts.
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Computational methods vary in performance, with FFPErase showing the most robust balance between artifact removal and retention of true somatic variants.
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Combining enzymatic repair with computational filtering did not lead to consistent improvements in performance across datasets.