Quantifying orthogonal barcodes for sequence census assays

Author:

Booeshaghi A Sina1ORCID,Min Kyung Hoi (Joseph)2,Gehring Jase3,Pachter Lior14ORCID

Affiliation:

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

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.

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Genetics,Molecular Biology,Structural Biology

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