Quantifying orthogonal barcodes for sequence census assays

A Sina Booeshaghi; Kyung Hoi Joseph Min; Jase Gehring; Lior Pachter

doi:10.1093/bioadv/vbad181

Quantifying orthogonal barcodes for sequence census assays

Bioinform Adv. 2023 Dec 20;4(1):vbad181. doi: 10.1093/bioadv/vbad181. eCollection 2024.

Authors

A Sina Booeshaghi¹, Kyung Hoi Joseph Min², Jase Gehring³, Lior Pachter^{1

4}

Affiliations

¹ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States.
² Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
³ Arcadia Science, Berkeley, CA 94702, United States.
⁴ Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, United States.

Abstract

Summary: Barcode-based sequence census assays utilize custom or random oligonucloetide sequences to label various biological features, such as cell-surface proteins or CRISPR perturbations. These assays all rely on barcode quantification, a task that is complicated by barcode design and technical noise. We introduce a modular approach to quantifying barcodes that achieves speed and memory improvements over existing tools. We also introduce a set of quality control metrics, and accompanying tool, for validating barcode designs.

Availability and implementation: https://github.com/pachterlab/kb_python, https://github.com/pachterlab/qcbc.