Fig. 1

Study design for benchmarking computational error-correction methods. a Schematic representation of the goal of error correction algorithms. Error correction aims to fix sequencing errors while maintaining the data heterogeneity. b Error-free reads for gold standard were generated using UMI-based clustering. Reads were grouped based on matching UMIs and corrected by consensus, where an 80% majority was required to correct sequencing errors without affecting naturally occurring single nucleotide variations (SNVs). c Framework for evaluating the accuracy of error-correction methods. Multiple sequence alignment between the error-free, uncorrected (original), and corrected reads was performed to classify bases in the corrected read. Bases fall into the category of trimming, true negative (TN), true positive (TP), false negative (FN), and false positive (FP)