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Detects barcodes and their abundances from raw next-generation sequencing data. Bartender employs a divide-and-conquer strategy that intelligently sorts barcode reads into distinct bins before performing comparisons, in order to improve speed and reduce unnecessary pairwise comparisons. To improve accuracy and reduce over-clustering artifacts, Bartender employs a modified two-sample proportion test that uses information on both the cluster sequence distances and cluster sizes to make merging decisions. Additionally, Bartender includes a “multiple time point” mode, which matches barcode clusters between different clustering runs for seamless handling of time course data.