Details

Autor(en) / Beteiligte

• Zhao, Lu
• Liu, Zhimin
• Levy, Sasha F
• Wu, Song
• Berger, Bonnie

Titel

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

Ist Teil von

• Bioinformatics, 2018-03, Vol.34 (5), p.739-747

Ort / Verlag

England: Oxford University Press

Erscheinungsjahr

2018

Link zum Volltext

Quelle

EZB-FREE-00999 freely available EZB journals

Beschreibungen/Notizen

• Abstract 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. Supplementary information Supplementary data are available at Bioinformatics online.