Bartender: a fast and accurate clustering algorithm to count barcode reads

Motivation Barcode sequencing (bar‐seq) is a high‐throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar‐seq are quickly growing—from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo‐barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar‐seq clustering are not well developed. Available methods often yield a high frequency of under‐clustering artifacts that result in spurious barcodes, or over‐clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next‐generation sequencing data. Results In contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two‐sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under‐ and over‐clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a ‘multiple time point’ mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple‐to‐use command line tools that can be performed on a laptop at comparable run times to existing methods. Availability and implementation Bartender is available at no charge for non‐commercial use at https://github.com/LaoZZZZZ/bartender‐1.1. Contact sasha.levy@stonybrook.edu or song.wu@stonybrook.edu Supplementary information Supplementary data are available at Bioinformatics online.